Methodology for assessing and mapping the heat demand · “Intelligent Energy – Europe”...

“Intelligent Energy – Europe” Programme Project BIOENERGIS – IEE/07/638/SI2.499702 GIS-based decision support system aimed at a sustainable energetic exploitation of biomass at regional level

Methodology for assessing and mapping

the heat demand

in

Lombardy Region, Italy

Northern Ireland, UK

Slovenia

Wallonia Region, Belgium

with the contribution of:

Centre for Technological Development, Energy and Competitiveness of Lombard SMEs (CESTEC)

South West College (SWC)

Slovenian Business and Research Association (SBRA)

Agricultural Research Centre of Wallonia (CRAW)

South West College (SWC)

February 2011

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND i

Lombardy Region

Contribution from Centre for Technological Development, Energy and Competitiveness of Lombard SMEs (CESTEC):

Claudia Beltrami, Anna Boccardi, Alessandro Chiesa, Stefania Ghidorzi, Alessio Morimondi

Contribution from Environmental Research Centre of Catholic University “Sacro Cuore” (CRASL):

Carlo Dalledonne, Christian Loda, Francesco Lussignoli, Stefano Oliveri, Denise Salvi, Irene Tomasoni, Maria Luisa Venuta

Northern Ireland Region

Contribution of South West College (SWC):

Mark Mc Guigan, David Millar

Slovenia

Contribution from Actum

Miha Marinšek

Wallonia Region

Contribution from Agricultural Research Centre of Wallonia (CRAW):

Romain Crehay, Alice Delcour

The sole responsibility for the content of this report lies with the authors. It does not necessarily reflect

the opinion of the European Communities. The European Commission is not responsible for any use

that may be made of the information contained therein.

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND ii

Table of Contents

2 OVERALL METHODOLOGY FOR ASSESSING THE POTENTIAL HEAT DEMAND ............................ 1

2.1 Overall technical method ....................................................................................................... 1

2.1.1 Overall method, Lombardy ................................................................................................ 1

2.1.2 Overall method, Northern Ireland...................................................................................... 2

2.1.3 Overall method, Slovenia .................................................................................................. 5

2.1.4 Overall method, Wallonia .................................................................................................. 7

3 INVENTORY OF AVAILABLE HEAT DEMAND DATABASES BY REGION....................................... 9

3.1.1 Inventory of available databases, Lombardy ................................................................... 10

3.1.2 Inventory of available databases, Northern Ireland ......................................................... 12

3.1.3 Inventory of available databases, Slovenia ...................................................................... 13

3.1.4 Inventory of available databases, Wallonia ..................................................................... 13

4 ASSESSMENT OF THE HEAT DEMAND IN LOMBARDY ............................................................. 14

4.1 Background .......................................................................................................................... 14

4.1.1 Energy statistics ............................................................................................................... 14

4.1.2 Surveys on estimation of the district heating potential .................................................... 17

4.2 Heat demand in the residential sector .................................................................................. 18

4.3 Heat demand in the public sector ......................................................................................... 23

4.3.1.1 Schools .................................................................................................................... 26

4.3.1.2 Hospitals and surgeries ........................................................................................... 29

4.3.1.3 Pools, gyms and sport centres ................................................................................. 32

4.3.1.4 Other public buildings............................................................................................. 33

4.4 Heat demand in the tertiary sector ....................................................................................... 35

4.4.1.1 Commercial end-users ............................................................................................ 35

4.5 Heat demand in the production sector .................................................................................. 36

4.6 Survey results ....................................................................................................................... 38

4.6.1 Residential sector ............................................................................................................. 38

4.6.2 Public sector .................................................................................................................... 38

4.6.3 Tertiary sector .................................................................................................................. 38

4.6.4 Production sector ............................................................................................................. 38

5 ASSESSMENT OF THE HEAT DEMAND IN NORTHERN IRELAND ............................................... 39


METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND iii




5.5 Survey results ....................................................................................................................... 44


5.5.2 Public sector .................................................................................................................... 44

5.5.3 Industrial sector ............................................................................................................... 45

5.5.4 Tertiary sector .................................................................................................................. 45

6 ASSESSMENT OF THE HEAT DEMAND IN SLOVENIA ............................................................... 46

6.1 Background .......................................................................................................................... 46

6.1.1 Energy statistics ............................................................................................................... 46

6.1.1.1 CO2 emissions per capita ........................................................................................ 50

6.1.1.2 Energy saving ......................................................................................................... 51

6.1.2 Climate coefficient by regions ......................................................................................... 51


6.2.1 Average heat demand in residential sector ...................................................................... 55


6.3.1 Administrative buildings and schools and other educational buildings........................... 57

6.3.2 Hospitals and medical buildings ...................................................................................... 57

6.3.3 Churches and places of religious worship ....................................................................... 57

6.3.4 Fire houses ....................................................................................................................... 57

6.3.5 Sport objects .................................................................................................................... 58


6.4.1 Shopping facilities ........................................................................................................... 58

6.4.2 Fair halls .......................................................................................................................... 58


6.6 Survey results ....................................................................................................................... 59


6.6.2 Public sector .................................................................................................................... 59

6.6.3 Tertiary sector .................................................................................................................. 59


7 ASSESSMENT OF THE HEAT DEMAND IN WALLONIA .............................................................. 61

7.1 Background .......................................................................................................................... 61

7.1.1 Cartography on buildings ................................................................................................ 61

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND iv

7.1.2 Energy carriers ................................................................................................................. 64





7.5.1 Heat demand from industrial buildings ........................................................................... 69

7.5.2 Heat demand from industrial processes ........................................................................... 70

7.6 Survey Results ..................................................................................................................... 74


7.6.2 Public sector .................................................................................................................... 74

7.6.3 Tertiary sector .................................................................................................................. 74


Sources and Literature ....................................................................................................................... 75

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND v

List of Tables Table 2.1: Average building type heat consumption values for Northern Ireland (Source: CIBSE) ...... 5

Table 3.1: Summary of the available database and of the information on heat demand per sector

derivable from those sources, Lombardy Region .................................................................................. 11

Table 3.2: Data sources referred to in the assessment of the heat demand for Lombardy .................... 12

Table 3.3: Datasets used to calculate heat demand from final bioenergy users for all building type in

Northern Ireland .................................................................................................................................... 12

Table 3.4: Datasets used to calculate heat demand from final bioenergy users for all building types in

Slovenia ................................................................................................................................................. 13

Table 4.1: Characteristic of census tracts in Lombardy (Source: ISTAT, 2001) ................................... 19

Table 4.2: Overall residential energy demand (all fuels except electricity, not cooking uses) in

Lombardy and in each Province and medium value per municipality in 2008 (Source: evaluation from

SIRENA) ................................................................................................................................................ 20

Table 4.3: Overall residential energy demand (all fuels except electricity, not cooking uses) in

Lombardy and in each Province: maximum and medium values per census tract maximum, and

medium values per urban area per census tract, 2008 (Source: evaluation from SIRENA and from

ISTAT) ................................................................................................................................................... 21

Table 4.4: Overall residential energy demand (all fuels except electricity, not cooking uses) per

municipalities (classification by number of inhabitants per municipality):total, medium per

municipality, medium per census tract, and medium per census tract on urban area, 2008 (Source:

evaluation from SIRENA and from ISTAT) ........................................................................................... 21

Table 4.5: Apartments per type of buildings in Lombardy and in Italy (total include blocks without

apartments) (Source: CRESME, 2010) .................................................................................................. 22

Table 4.6: Audit-GIS: number and percentage of the involved Municipalities, number, surface and

volume of the analysed buildings (Source: Fondazione Cariplo) ......................................................... 25

Table 4.7: Schools in Audit-GIS and total schools (Source: Fondazione Cariplo) ............................... 27

Table 4.8: Reference values for energy consumption as a function of number of beds and surface in

Italian hospitals (FIRE - Italian Federation for Rational use of Energy, ENEA - National Agency for

New Technologies, Energy and Sustainable Economic Development) ................................................. 30

Table 4.9: Overall heat demand of hospitals (Source: survey and estimations) ................................... 31

Table 4.10: Median value of heat demand per surface, per surface and degree-days and per building in

different typologies of sports facilities (Source: elaboration from Audit-GIS) ..................................... 32

Table 4.11: Industrial sectors whose heat demand could be fulfilled by heat from district heating

networks ................................................................................................................................................ 37

Table 5.1: Residential sector buildings mapped for heat demand use ................................................... 39

Table 5.2: Cumulative heat demand for residential sector buildings summarised at County level ....... 39

Table 5.3: Public sector buildings mapped for heat demand use. ......................................................... 41

Table 5.4: Tertiary sector buildings mapped for heat demand use ........................................................ 41

Table 5.5: Production sector buildings mapped for heat demand use ................................................... 43

Table 6.1: Change in energy intensity of the common end-use energy and by sector in the period 2002-

2007 (Source: Statistical office of the Republic of Slovenia, 2008) ...................................................... 47

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND vi

Table 6.2: Energy Indicators, 2000, 2005 - 2008 (Source: Statistical office of the Republic of Slovenia,

2008)...................................................................................................................................................... 49

Table 6.3: Balance of production and consumption of electricity (GWh), 2008 (Source: Statistical

office of the Republic of Slovenia, 2008) ............................................................................................... 50

Table 6.4: Average temperature in Slovenia, 2009 (Environmental Agency of Republic of Slovenia) . 52

Table 7.1: PICC category codes for each building type ........................................................................ 61

Table 7.2: PICC codes and categories not relevant ............................................................................... 62

Table 7.3: PLI's coefficient ................................................................................................................... 63

Table 7.4: PICC codes and categories selected for civil users .............................................................. 65

Table 7.5: Structure of database for heat demand from civil users ....................................................... 66

Table 7.6: PICC codes and categories selected for public properties sector ......................................... 66

Table 7.7: Heat demand coefficient for non categorized buildings of PICC......................................... 67

Table 7.8: Database structure of heat demand of public buildings ....................................................... 67

Table 7.9: PICC codes and categories selected for tertiary sector ........................................................ 68

Table 7.10: Coefficients of heat demand for department store ............................................................. 68

Table 7.11: Database structure of heat demand from services and commercial activity buildings ....... 69

Table 7.12: Database structure of heat demand from industrial buildings ............................................ 70

Table 7.13: NACE codes selection based on ICEDD report ................................................................. 71

Table 7.14: Energy Atlas of Wallonia ................................................................................................... 72

Table 7.15: Margin of heat demand ...................................................................................................... 72

Table 7.16: Consumption (kWh/year) for companies common between Energy Atlas of Wallonia and

CD Enterprises ...................................................................................................................................... 73

Table 7.17: Total number of companies selected .................................................................................. 73

Table 7.18: Database structure of heat demand from industrial processes............................................ 74

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND vii

List of Figures

Figure 2.1: National Mapping Agency base mapping, showing the outline of a selected public use

sports and recreation building. Building area can be directly calculated using a GIS, if not already

recorded in the base mapping polygon data ............................................................................................ 3

Figure 2.2: Heat demand map example for residential and public use sports centre buildings (Source:

CIBSE - Chartered Institute of Building Service Engineers 2004 Data Report) .................................... 4

Figure 2.3: Ortho-photo map of buildings ............................................................................................... 6

Figure 2.4: Map of buildings ................................................................................................................... 6

Figure 2.5: Shows examples of average energy consumption figures for public building types from the

Local Energetic Concept of Vrhnika municipality ................................................................................. 7

Figure 4.1: Final consumption of energy per energy carriers in Lombardy 2000-2007 (Source: SIRENA

Regional Informative System for Environment and Energy of Lombardy Region) ............................... 14

Figure 4.2: Final consumption of energy in Lombardy 2008 – per sector of use (Source: SIRENA) ... 15

Figure 4.3: RES contribution in covering regional energy demand, 2008 (Source: SIRENA) .............. 15

Figure 4.4: Final consumption of energy (toe) in residential sector per municipalities, 2007 (Source:

SIRENA) ................................................................................................................................................ 16

Figure 4.5: Final consumption of energy (toe) in tertiary sector per municipalities, 2007 (Source:

SIRENA) ................................................................................................................................................ 17

Figure 4.6: Surface of apartment per Census Area (Monza – MB) (Source: ISTAT, 2001) ................. 19

Figure 4.7: Webpage of Audit–GIS db, Fondazione Cariplo (in the page is showed the yearly heat

demand of the public schools of the Municipality of Seveso). ............................................................. 23

Figure 4.8: Municipalities involved in energy audit campaign promoted by Fondazione Cariplo in

2006-2008 .............................................................................................................................................. 24

Figure 4.9: Intended use of buildings analysed in Audit-GIS (Source: Fondazione Cariplo) .............. 25

Figure 4.10: Heating systems in buildings analysed in Audit-GIS (Source: Fondazione Cariplo). ...... 26

Figure 4.11: Energy carriers in heating systems of buildings analysed in Audit-GIS (Source:

Fondazione Cariplo). ............................................................................................................................ 26

Figure 4.12: Average heat demand in the schools analysed in Audit-GIS (Source: Fondazione

Cariplo). ................................................................................................................................................ 27

Figure 4.13: Heat demand for schools: detail of Sarnico, BG (Source: audits and estimations) .......... 28

Figure 4.14: Energy consumption for heating (2008) in the hospitals from the questionnaire answers 30

Figure 4.15: Heat demand for hospitals and for medical offices (ASL) in Lombardy – detail of

Saronno, VA (Source: survey and from estimations) ............................................................................ 31

Figure 4.16: Energy consumption for heating in sport facilities in Lombardy – detail of

Casalpusterlengo, LO (data from audits and from estimations) ........................................................... 33

Figure 4.17: Energy consumption for heating in public administrative, recreation centers, libraries,

residential and social welfare facilities in Lombardy– detail of Osnago, LC (data from audits and from

estimations) ........................................................................................................................................... 34

Figure 4.18: Share per municipalities of the heat demand in Lombardy Region venues (Source:

Lombardy Region Energy Management, Cestec) .................................................................................. 35

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND viii

Figure 4.19: Energy consumption for heating in medium and large sales structures (Source: audits and

estimations) ........................................................................................................................................... 36

Figure 4.20: Heat demand (suitable for DHS) in industrial sector ........................................................ 37

Figure 5.1: Residential heat demand at Municipal Ward level ............................................................. 40

Figure 5.2: Tertiary sector buildings heat demand use .......................................................................... 42

Figure 5.3: Industrial sector buildings mapped for heat demand use .................................................... 44

Figure 6.1: Final consumption of energy per sector in Slovenia (Source: Statistical office of the

Republic of Slovenia, 2008; Institut Jožef Stefan). ................................................................................ 47

Figure 6.2: Production of electricity from renewables, and aim to 2010 ReNEP and growth of

electricity production from RES and gross electricity use during the period 2000-2007 (Source:

Statistical office of the Republic of Slovenia, 2008). ............................................................................ 48

Figure 6.3: Final energy consumption, 2008 (Source: Statistical office of the Republic of Slovenia,

2008)...................................................................................................................................................... 49

Figure 6.4: Final energy supply (electric energy market not included), 2008 (Source: Statistical office

of the Republic of Slovenia, 2008) ......................................................................................................... 50

Figure 6.5: Consumption of various energy sources for heating purposes in households (Source:

Statistical Office of the Republic of Slovenia) ....................................................................................... 53

Figure 6.6: The daily chart for household consumption in the winter period in Slovenia .................... 55

Figure 6.7: The daily chart for household consumption in spring/autumn period in Slovenia ............. 55

Figure 6.8: The daily chart for household consumption in summer period in Slovenia ....................... 55

Figure 6.9: Consumption of various energy sources for heating purposes in wide usage ..................... 56

Figure 6.10: Specific Average, Target and Bad practice Energy Consumption for schools and public

buildings, Slovenia (Gradbeni Inšitut ZRMK d.o.o) ............................................................................. 57

Figure 7.1: Progress of coverage of PICC database (Source: http://cartopro2.wallonie.be) ................ 62

Figure 7.2: PLI and PICC coordination ................................................................................................. 63

Figure 7.3: Natural gas distribution network ......................................................................................... 64

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND ix

Abbreviations

CEN European committee for standardization

CHP Combined heat and power (generation)

DSS Decision Support System

EC European Commission

GIS Geographic Information System

toe ton of oil equivalent: unit of energy corresponding to the output of 1 ton of oil, used to

express the energy production or consumption of a country

LPG Liquefied Petroleum Gas

DHS District Heating System(s)

RES Renewable Energy Source(s)

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND x

Background

Funded by the European Commission under the Intelligent Energy Europe Programme, the

BioEnerGIS Project (“the Project” or “BioEnerGIS”) has developed a GIS-based Decision Support

System, which allows both public decision makers and private operators to identify the most suitable

sites for bioenergy plants installation, in terms of energy, environmental and economic sustainability.

The DSS “BIOPOLE” combines supply and demand-side data, regional legislation, technological

options and business plans to generate maps for potential localization and configuration. The end-use

technology focus is on district heating systems and cogeneration plants. BioEnerGIS has also explored

the public and private interest in implementing the identified plants, the different stakeholders’ needs

and the possible financial or legislative instruments in order to encourage a shared action programme.

The decision-making tool has been developed in four target regions, representing different

environmental and economic conditions: Lombardy (Italy), Northern Ireland (United Kingdom),

Slovenia and Wallonia (Belgium).

For this purpose, maps of potential biomass resources have been produced in the four regions, along

with maps of heat demand: these two layers of information have been cross-analyzed by the DSS with

the integration of technical, financial and legal conditions specifically assessed in each region, in order

to determine the most suitable plant localizations and configurations.

This report presents the methodology applied to assess and map the potential heat demand and heat

demand density in the four target regions.

METHODOLOGY FOR ASSESSING AND MAPPING THE HEAT DEMAND 1

2 OVERALL METHODOLOGY FOR ASSESSING THE POTENTIAL HEAT DEMAND

2.1 OVERALL TECHNICAL METHOD

The localization suitability for biomass District Heating plants (combustion boilers or gasification

units, heat generation or cogeneration) must be primarily driven by the demand side and by the

availability of biomass at a “reasonable distance”: this is one of the main theses adopted in

BioEnerGIS.

For this reason, special emphasis was placed on geo-referencing the potential energy end-users.

Statistics on energy demand by sector at the regional level are generally available, but in order to meet

the objectives of the Project it was necessary to investigate the localization and the intensity of the

heat demand of end users and to weight which users could actually be connected to a DHS.

Optimum heat demand calculation would be at point building level: if this is not attainable,

Municipality polygon can be considered a fairly good level of detail.

In a similar degree to the biomass potential’s assessment procedure, the mapping of heat demand and

heat demand density can be achieved by initially identifying the main consumers of heat.

The four main user types have been identified as

• residential users,

• public users,

• tertiary sector users,

• production activity users.

In keeping with BioEnerGIS purposes, each region has been asked for heat demand from each of the

different sectors, and – where needed - for population, population density, residential volume etc. at

the highest spatial resolution possible.

Data were acquired from the Government energy efficiency monitoring bodies that annually collect

building heat and electricity consumption figures from public, tertiary and possibly production activity

sector buildings.

Large-scale base mapping used in GIS computer systems supplied by national mapping Agencies that

survey and map all geographic information down to building level can provide the mean for

determining heat demand and heat density for specific building types.

2.1.1 Overall method, Lombardy

The adopted methodology for Lombardy combines energy data “samples” (from audit campaigns, projects and an investigation specifically realized for BioEnerGIS on hospitals’ heat demand) with statistical indicators (e.g. population, number of students for each school, climate data).

They are compared and integrated with the annual energy demand per sector and per energy carrier from the Regional Informative System for Environment and Energy (SIRENA): these data are not


immediately equivalent to heat demand values because they include also non-thermal uses (e.g. cooking), and they are values of primary energy supply (before the energy transformation) and not of energy demand.

To complete the mapping of the heat demand with relation to the existing databases, the main effort has been devoted to achieve a good spatial location and to identify punctually public utilities or public service users, that, in the experience of biomass systems already implemented in Lombardy, are the drivers of small DHS.

In the residential sector, the average energy consumption was combined with the number of building types and the average floor area per census tract to produce an estimation of residential heat demand. In the public, service and commercial sectors, the localization of the heat demand starts from databases including address of each single building; in the industry sector localization comes from the land use map.

2.1.2 Overall method, Northern Ireland

Building types, divided in the residential, public, tertiary and production sector buildings, have been

located in a GIS using a geographic dataset of all building locations and types in Northern Ireland.

This dataset, which is sub-categorised into Education, Public Use, Office and Warehouse, can be

queried, selected and then mapped for each building type.

Building footprint/floor areas have been acquired from large-scale Agency’s base mapping

information using a GIS. Area data from base mapping building polygons were appended to point

datasets in the case of large datasets such as the residential building sector and then mapped

accordingly.

Data on building heat demand were also acquired from a recent public national survey: these data were

appended to the data already queried and mapped and also used, consequently, to update the existing

data.

For the over 600.000 residential buildings, heat demand was calculated at building level, before being

merged into a smaller scale Municipal Ward Level (containing 582 polygons).


Figure 2.1: National Mapping Agency base mapping, showing the outline of a selected public use sports

and recreation building. Building area can be directly calculated using a GIS, if not already recorded in

the base mapping polygon data

Figure 2.1 shows an example of base mapping building polygons for which heat demand was

calculated.

Figure 2.2 represents a map of residential and public sector data.

The average heat demand of building types was estimated using a combination of national building

energy consumption benchmark figures, which give the average heat and electricity consumption

figures per building type. Energy consumption benchmarks in kWh/m2 year can then be combined

with total floor areas for building types to produce total heat demand values for each building type in

kWh of heat demand per building per year: Table 2.1 shows typical energy consumption benchmark

figures for some of the main building types in the residential, public, tertiary and production sectors.

The adopted methodology considers all these different quantities and the “conversion factor” among these to give a picture of the heat demand for DHS per sector.


Figure 2.2: Heat demand map example for residential and public use sports centre buildings (Source:

CIBSE - Chartered Institute of Building Service Engineers 2004 Data Report)


Building type Low fossil

figure (kWh/m² year)

Year benchmarking data published

Data source Sector

Residential housing 281 2005 CIBSE Residential

Leisure centre (no pool) 144 2005 CIBSE Public

Leisure centre with pool 325 2005 CIBSE Public

Hospital (long stay) 413 1998 CIBSE Public

Primary school 85 2005 CIBSE Public

Secondary school 87 2005 CIBSE Public

College – Further education 83 2005 CIBSE Public

University – Non-residential 185 1998 CIBSE Public

Library 121 2005 CIBSE Public

Office generic 100 2005 CIBSE Tertiary

Shop – small 80 1998 CIBSE Tertiary

Shop – Supermarket 160 1998 CIBSE Tertiary

Industrial – General & light

manufacturing 125 1998 CIBSE Production

Industrial – Storage &

distribution 135 1998 CIBSE Production

Table 2.1: Average building type heat consumption values for Northern Ireland (Source: CIBSE)

2.1.3 Overall method, Slovenia

To map the heat demand and heat demand density in the four main consumers (residential, public, tertiary sector and production activity users), different building types were defined by using data from the Real Estate Register, which is based on a 2008 national survey. The Government Agency in Slovenia responsible for this data collection is the Ministry of the Environment and Spatial Planning. Buildings can also be located using datasets created by Slovenian Surveying and Mapping Authority. The building floor areas of different building types can be acquired from the database mapping information. Data from database mapping building polygons can also be appended to point datasets in the case of large datasets such as the residential building sector and then mapped accordingly.


Figure 2.3: Ortho-photo map of buildings

Figure 2.4: Map of buildings


The heat demand of building types can be estimated using a combination of data collected from the Local Energetic Concepts of Slovenian municipalities, which have data mainly on the public sector buildings and larger companies, and from the legal directives for the private (residential) sector buildings.

Energy consumption, as demonstrated in Figure 2.5 in kWh/m2 year, can then be combined with total floor areas for building types to produce total heat demand values for each building type in kWh per building per year.

Figure 2.5: Shows examples of average energy consumption figures for public building types from the

Local Energetic Concept of Vrhnika municipality

The average energy consumption can then be combined with the number of building types and the average floor area (which can be accessed from National Census survey statistic bodies) to produce an estimation of total floor area per building type.

2.1.4 Overall method, Wallonia

The mapping of energy demand in Wallonia has been split into four sectors:

- residential heat demand (e.g. households);

- public properties (e.g. municipal school, public hospital, etc.);


- services and commercial (e.g. supermarket, restaurant, etc.);

- production activities (e.g. industries, etc.);

The heat demand from buildings belonging to these four categories was assessed and mapped using

the same primary information - the PICC (Project Informatique de Cartographie Continue - Computer

Continuous Cartography Project) and PLI (Plan de Localisation Informatique - Computer Processing

Location Plan) databases – that, together, provide a map for the entire region at building level.

A database was produced for each of the four sectors, as a separate shapefile. The information on heat

demand is detailed in the table of attribute of each database.

The heat demand from industrial processes was assessed using standard figures. The location of these

industries was given with the enterprises database.

The heat demand coefficients necessary to estimate the overall heat demand come from the Institut de

Conseil et d'Etudes en Développement Durable (ICEDD) - Institute of research and consulting in

sustainable development, from documents of the Confederation Construction of Wallonia, from the

heat demand coefficient specific by municipality and from the Laboratoire d’Etude en Planification

Urbaine et Rurale (LEPUR) of Gembloux Agricultural University.


3 INVENTORY OF AVAILABLE HEAT DEMAND DATABASES BY REGION

The first step to develop the heat demand for bioenergy final users is the inventory of existing heat demand databases. The survey in general was made considering the following specifications:

Residential users:

- energy demand, per fuel type and per uses (air conditioning, water heating, etc...)

- population, population density, residential volume or floor area (at least on municipality scale)

- number, year of building, surface/volume ratio, climatic conditions (degree day per municipality), building's overall thermal performance.

Public users:

- energy demand, per fuel type, per uses (air conditioning, water heating, etc...) and per type of building (school, hospital, etc...)

- number, year of building, volume or floor area, surface/volume ratio per type of building (at least on municipality scale), climatic conditions (degree day per municipality), building's overall thermal performance.

Tertiary users:

- energy demand, per fuel type and per uses (air conditioning, water heating, etc...)

- number of employees per specific sector, type of building (office, shopping centre, etc)

- number, year of building, volume or floor area, surface/volume ratio (at least on municipality scale), climatic conditions (degree day per municipality), building's overall thermal performance.

Industrial users:

- Energy demand, referred to – at least – the last available 3 years, per fuel type and per industrial processes

- Number of employees or fuel consumption or quantity of product per specific sector (at least on municipality scale).

For every existing dB above indicated, each regional Partner noticed metadata on:

- data structure

- data accuracy

- data availability (procedure, restrictions, waiting time, etc.)

- spatial resolution

- year and frequency of updating


The survey pointed out specific indicators and databases that best fit the regional case, as described below.

3.1.1 Inventory of available databases, Lombardy

In the case of Lombardy in the survey were used:

- the Regional Informative System for Environment and Energy of Lombardy Region (SIRENA), that reports energy demand per carriers and per sector at municipal level;

- specific energy data from audit campaigns, projects and investigation specifically realized for BioEnerGIS;

- statistical indicators (e.g. population, number of students for each school, climate data), used to disaggregate data at a better spatial resolution.

Good quality information (in terms of spatial resolution, value and characterization of heat demand) was obtained for the public sectors (particularly schools and hospitals) and for the residential sector, while for the industrial sector the estimation of the heat demand is less accurate. Data and statistical indicators used to construct the energy demand in different sectors are summarized in Table 3.1; the main data sources are listed in Table 3.2.

Sector Existing data Outputs

Residential

Information on surface of houses per census area (ISTAT)

Information on energy demand per fuel per municipality, from energy model (SIRENA)

- “Type” of energy demand (residential), - kWh/y per municipality/census area

(estimation), per fuel (estimation), - surface of building, - number of apartments / number of

buildings per census area.

Hospitals and health facilities

Punctual information on localization (RL-HD);

For about 65% of the hospitals, punctual information on energy demand (RL-HD, Audit-GIS); for the others, estimation from building volume or number of beds.

- “Type” of energy demand (hospital), - kWh/y per point, per fuel, - surface and volume of buildings.

Schools

Punctual information on localization (Provveditorato agli Studi);

For about 17% of the schools punctual information on energy demand (Audit-GIS); for the others, estimation from number of students per school.

- “Type” of energy demand (schools), - kWh/y per point (estimation), - surface and volume of buildings

(estimation)


Table 3.1: Summary of the available database and of the information on heat demand per sector derivable

from those sources, Lombardy Region

Other public buildings

Punctual information from energy audits on about 3.500 public buildings in about 580 small municipalities (Audit-GIS)

Punctual information from Energy Management of Lombardy Region (23 buildings)

Localization and data on surface of about 1.200 libraries (RL-ED, ISTAT).

- “Type” of energy demand (only general),

- kWh/y per point (measured and estimated).

Shopping centres

Punctual information from Energy Management of Lombardy Region on some shops.

Information on localization and surface for about 8.500 medium and large shops (Trade Observatory).

- “Type” of energy demand (schools),


Pools, gyms and sport centres

Information on some proxies; energy data from the results of energy audits on public buildings (Audit-GIS)

Typology and localization of all the sport centres (RL-SD).

- “Type” of energy demand (schools)


Small industries

Information on some proxies (ISTAT) and information on overall energy demand per industrial sector per municipality (SIRENA)

Map of the industrial areas (DUSAF).

- Estimation of heat demand (excluding not thermal uses and high temperature uses) per municipalities, allocated on the industrial areas.

Data sources Full name Dataset(s) and availability

SIRENA

Regional Informative System for Environment and Energy of

Lombardy Region

Lombardy Region, CESTEC

Energy demand per carriers and per sector at regional and at municipal level; GHG emissions (in tons of CO2 eq) from

energy uses (energy consumption and production) at regional and at municipal level; power plants (size, production,

efficiency, ...) at regional level (for each year 2000-2008).

Data available on web.

ISTAT Italian National Institute of

Statistics

Information on population and houses; the Institute produces knowledge of Italy's environmental, economic and social

dimensions at various levels of geographical detail.

Last Census dates back to 2001.

Data available on web at aggregated level.

RL-HD

Dedicated survey on hospitals and local health departments

Lombardy Region Health Department, CESTEC

Punctual information on heated volume, type of energy carrier and energy demand (2008).

Confidential data.

Fondazione CARIPLO

Audit-GIS

Audit GIS Project by Fondazione Cariplo (a private,

grantmaking foundations, involved in supporting the

social, cultural, political, and economic development).

Data from energy audit for public buildings in municipalities participating in the audit campaign.



Table 3.2: Data sources referred to in the assessment of the heat demand for Lombardy

3.1.2 Inventory of available databases, Northern Ireland

Table 3.3 highlights the list of datasets that were acquired for heat demand estimation in Northern

Ireland.

Data were acquired from the Northern Ireland Mapping Agency (Ordnance Survey Northern Ireland),

from the Northern Ireland Statistical and Research Agency and from the Chartered Institute of

Building and Surveying Engineers.

Table 3.3: Datasets used to calculate heat demand from final bioenergy users for all building type in

Northern Ireland

Provveditorato agli Studi

Provveditorato agli Studi – Regional School Office,

Lombardy Region

Punctual information on localization and number of students per school.


Energy Management of Lombardy

Region

Energy Management of Lombardy Region

Lombardy Region, CESTEC

Punctual information on energy demand for the building owned by Lombardy Region.

Confidential data.

RL-ED Lombardy Region – Education

Department

Database of the regional libraries (localization and data on surface of about 1.200 libraries).


Commerce Obs.

Regional Observatory on Commerce

Lombardy Region – Commerce Department

Information on localization and surface for about 8500 medium and large shops.


RL-SD Lombardy Region – Sport and

young people Department Typology and localization of 2.300 sport centres.

Data available on web; confidential data.

DUSAF

Lombardy Region – Territory and Urban planning

Department – Land use in Lombardy map (2010)

Land use cartography:

Map (polygons) of area for productive sector (minimum area 400 m2).


Data sources Full name Dataset(s) Age of data

OSNI Northern Ireland National Mapping

Agency (OSNI)

1:1.000/1:2.500 Basemapping; Pointer

Building Dataset 2010

NISRA Northern Ireland

Statistical Research Agency (NISRA)

Public sector buildings point dataset

2008

CIBSE Chartered Institute of

Building and Surveying Engineers (CIBSE)

Building heat consumption dataset

2008


3.1.3 Inventory of available databases, Slovenia

The data collection has started with the enquiries made to prepare the pre-assessment studies. Data were acquired from the Slovenian Surveying and Mapping Authority, including a point dataset of all buildings and their types and a further base mapping dataset containing all building footprints and their area in square meters. A dataset on average annual building type heat consumption was acquired from the Local Energetic Concepts of several municipalities, from relevant literature, and from data directly acquired through interviews.

The collected data allowed heat demand to be calculated using a combination of building category type (Surveying and Mapping Authority of Slovenia), building footprint area (Surveying and Mapping Authority of Slovenia), and average annual building category type heat consumption (Local Energetic

Concepts and legal directives).

Full name Dataset

Surveying and Mapping Authority of Slovenia Vector point dataset of all building types

Raster base mapping dataset

Local Energetic Concepts Building heat consumption dataset

Table 3.4: Datasets used to calculate heat demand from final bioenergy users for all building types in

Slovenia

In more detail, the data on average consumption was collected from Gradbeni Inštitut ZRMK d.o.o. for

residential buildings and for administrative buildings and schools, from Recknagel, Sprenger,

Schramek “Taschenbuch fur Heizung + Klimatechnik” 2002 and Univerza na primorskem, Fakulteta

za manegement, diplomska naloga “Strategija energetske učinkovitosti bolnišnice” for hospitals and

medical buildings, from Recknagel, Sprenger, Schramek “Taschenbuch fur Heizung + Klimatechnik”

2002 for churches, from N. Lojevec “Energetski preračuni” for shopping facilities and from Municipal

Local Energetic Concepts for fire houses.

3.1.4 Inventory of available databases, Wallonia

The PICC (Project Informatique de Cartographie Continue - Computer Continuous Cartography

Project) database was the main data source for the assessment of heat demand for buildings. In the

areas not actually covered by PICC, the alternative option was to use the PLI database (Plan de

Localisation Informatique - Computer Processing Location Plan), in which the polygons representing

each building (in 1:10.000 scale) aren’t classified per typology.

In PICC each building is represented as a polygon, with accuracy of 25 cm: for each polygon is given

a category code corresponding to a detailed typology (about 30 different typologies are included in the

db).

The PICC and PLI database provide the shape of every building along with its area.


4 ASSESSMENT OF THE HEAT DEMAND IN LOMBARDY

4.1 BACKGROUND

4.1.1 Energy statistics

The territory of Lombardy Region extends over a total surface area of 23.863 km2: Lombardy is the fourth largest region in Italy by extension, with a territory that is 47% flat plains, 40% mountainous and the remaining 13% hill.

Having a population of around 9,8 million inhabitants (on 60 million total in Italy), it is the most populated region, and the second most densely populated, with about 400 inhabitants per km2.

Figure 4.1: Final consumption of energy per energy carriers in Lombardy 2000-2007 (Source: SIRENA

Regional Informative System for Environment and Energy of Lombardy Region)


Figure 4.2: Final consumption of energy in Lombardy 2008 – per sector of use (Source: SIRENA)

Concerning energy demand, Lombardy is slightly less than 20% of the national energy demand. The overall final energy consumption in Lombardy in 2008 is about 25 million toe (300 TWh), of which about 40% in the civil sector (residential and tertiary) and 30% in the industry.

Only around 7% of the final energy consumption in Lombardy comes from renewable energy sources, mainly hydroelectricity, wastes and biomasses.

In June 2007 Lombardy Region has adopted its Energy Action Plan that defines the general strategy and the actions list to meet Kyoto Protocol objective on regional basis, and EU directives on renewable and on energy savings. Biomass production is one of the key points to reach these ambitious aims.

In February 2010 the Plan for a Sustainable Lombardy, that strengths the line of action started with the Energy Action Plan, was approved. This document points out the contribution that the region wants to give towards the 20-20-20 European Climate Plan.

Figure 4.3: RES contribution in covering regional energy demand, 2008 (Source: SIRENA)


In order to help decision making and to monitor the plans execution effectiveness, the web tool SIRENA (see chapter 3.1) has been developed and published: in the public area the regional energy flows and their environmental impact on the air quality can be evaluated and monitored.

Statistics on the main energy carriers in Lombardy are well known at regional level and generally at provincial scale (in Lombardy there are 12 provinces and 1.546 municipalities), while the main data sources are lacking if searched for type of final users (sectors) and for municipalities.

However, using a mixed top-down and bottom-up methodology, based on both point data than statistic indicators, the energy demand per carriers and per sector at municipal level can be built.

Figure 4.4: Final consumption of energy (toe) in residential sector per municipalities, 2007 (Source:

SIRENA)


Figure 4.5: Final consumption of energy (toe) in tertiary sector per municipalities, 2007 (Source: SIRENA)

This knowledge, although not exhaustive for the purposes of BioEnerGIS (the municipal scale cannot be enough detailed for the localization of a medium-small district heating system), is a fundamental reference for the assessment of heat demand in Lombardy.

4.1.2 Surveys on estimation of the district heating potential

An important research on the potential of district heating systems in Italy was made by CESI – Ricerca

di Sistema (a research and technical innovation and technological development for the purpose of general interest to the electricity sector) in 2005. This survey identifies, among the Italian municipalities with over 25.000 inhabitants (ISTAT, 2001), the volume of buildings potential suitable for DHS; starting from data on the urban cities, are identified and calculated:

- the proportion of the heated residential volume in high density populated area - where the cost-effective of the heat networks is convenient – divided per centralized/single heating system and per energy carrier;

- the proportion of the previous volume that could be effectively served by a DHS, applying a specific factor of connection is identified.


- the thermal requirements for the users identified in the previous phase (in terms of maximum power and annual energy demand) and the possible technological solutions.

The study shows that in Italy the unexploited potential of DHS is large (about 1000 Mm3, 14 times the volume served in 2004) and that, because of climatic conditions and types of settlements, the more interesting solutions are located in northern regions (55% of the potential volume).

This methodology was developed to offer an overall analysis and particularly focused on large centres; since the interest of BioEnerGIS is meant to systems of small and medium scale and on a regional level, the methodology described in the CESI researches is a useful reference but cannot be immediately applied in BioEnerGIS.

At European level, in 2005-2006, under the IEE Programme was founded the Ecoheatcool Project (www.euroheat.org). Ecoheatcool, among others, analyses the heating and cooling demands in Europe with a view to provide comprehensive, aggregate information about the whole heating and cooling market and its dynamics, and analyses and makes visible possibilities for district heating and cooling in Europe. Again, the purpose and the outputs of Ecoheatcool aren’t directly applicable to BioEnerGIS, but that project shows how district heating (and cooling) can expand further in Europe (6,8 EJ heat/year - 160 Mtoe heat/y) and offer higher energy efficiency and higher security of supply with the benefit of lower CO2 emissions. In particular, the overall potential for biomass in Europe is considered approximately 13 – 18 EJ/year (308 – 427 Mtoe/y): the present use (2003) in DHS is about 0.17 EJ (4 Mtoe) – 1% of the potential. Ecoheatcool also highlights the conditions that make DHS convenient and the main evaluation factors, starting from the heat demand density (depending on the population density, the type of buildings, …), the presence of central heating systems in buildings, and the availability of heat from RES, waste plants, surplus heat from industries and CHP.

4.2 HEAT DEMAND IN THE RESIDENTIAL SECTOR

For residential sector input data is the yearly energy consumption, available at municipal scale and itemized by fuel, unbundled in census tracts, using no. of apartments, buildings, average apartment size and percentage of heating per energy carriers at municipal level as proxies. Census tracts are georeferenced (see an example in the figure below), so it’s possible to convert the amount of energy demand per census area into amount per cell (as required by BIOPOLE).


Figure 4.6: Surface of apartment per Census Area (Monza – MB) (Source: ISTAT, 2001)

The census tract level, even if not a punctual information, has a very good spatial resolution.

In Lombardy there are about 49.000 census tracts, in 1.546 municipalities (see Table 4.1), with an average population of 200 inhabitants per census area.

Number of census tracts 48.912

Area of census tract: medium 0,488 km2

Area of census tract: median 0,340 km2

Population: medium inhabitants per census tract 185 inh.

Population: median inhabitants per census tract 75 inh.

Population density per census tract: medium 6.184 inh./km2

Population density per census tract: median 2.537 inh./km2

Table 4.1: Characteristic of census tracts in Lombardy (Source: ISTAT, 2001)

The census tracts are larger in less crowed areas (about 5.200 census tracts with no residential buildings, corresponding to 6.200 km2, with an average area per census tract of 1,2 km2) and smaller in high-density areas (about 16.000 census tracts with more than 5.000 inhabitant/km2, corresponding to 410 km2, medium area per census tract 0,025 km2).


The yearly energy consumption for residential sector at municipal scale and itemized by fuel is available for the period 2000 – 2008 in SIRENA.

The energy consumption is relative to all fuels, except electricity. In fact, in Lombardy the use of electricity for heating is uncommon. On the other hand, the energy consumption in the residential sector of other carriers (natural gas, diesel, LPG, wood, solar heating and district heating) is designated primarily for heating and hot water. At national level, the statistic survey shows that the percentage of energy used for cooking is around 5 - 6% of the total non electric uses; in Lombardy, this percentage is likely to be less. Conservatively, however, the energy consumption of natural gas and LPG (fuel used for cooking facilities) have been reduced by 6%.

The output of the residential data analysis gives kWh/y per municipality and per census tract (estimation), per fuel (estimation), in the period 2000-2008.

In the tables below are showed some outputs, referred to 2008.

Province Residential heat

demand (toe)

Residential heat

demand (GWh)

Average residential

heat demand per

Municipality (toe)

Average residential

heat demand per

Municipality (GWh)

Bergamo 663.985 7.722 2.721 32

Brescia 932.729 10.848 4.528 53

Como 406.632 4.729 2.495 29

Cremona 268.844 3.127 2.338 27

Lecco 243.472 2.832 2.705 31

Lodi 136.984 1.593 2.246 26

Milano 1.924.280 22.379 10.236 119

Mantova 264.469 3.076 3.778 44

Pavia 396.681 4.613 2.088 24

Sondrio 160.869 1.871 2.062 24

Varese 569.360 6.622 4.038 47

Lombardy 5.968.306 69.411 3.860 45

Table 4.2: Overall residential energy demand (all fuels except electricity, not cooking uses) in Lombardy

and in each Province and medium value per municipality in 2008 (Source: evaluation from SIRENA)


Province

Residential heat

demand per

census tract MAX

(kWh)

Residential heat

demand per census

tract MEDIUM

(kWh)

Residential heat

demand/census

tract Area MAX

(kWh/m2)

Residential heat

demand/census tract

Area MEDIUM

(kWh/m2)

Bergamo 12.267.566 1.955.469 265 23

Brescia 20.555.473 2.003.258 438 31

Como 12.024.681 1.749.630 318 27

Cremona 12.591.702 1.238.769 401 33

Lecco 12.875.606 1.779.749 378 30

Lodi 14.015.051 1.362.811 262 26

Milano 14.576.629 1.282.026 16.641 59

Mantova 20.330.337 1.218.609 399 20

Pavia 18.912.610 965.551 1.048 30

Sondrio 11.005.094 1.153.912 391 22

Varese 13.173.254 1.274.867 948 30

Lombardy 20.555.473 1.419.108 16.641 39

Table 4.3: Overall residential energy demand (all fuels except electricity, not cooking uses) in Lombardy

and in each Province: maximum and medium values per census tract maximum, and medium values per

urban area per census tract, 2008 (Source: evaluation from SIRENA and from ISTAT)

Municipality

inhabitants

Residential

heat demand

per

Municipality

total (toe)

Residential heat

demand per

Municipality

MEDIUM (toe)

Residential heat

demand/Municipalit

y Area MEDIUM

(kWh/m2)

Residential heat

demand per

census tract

MEDIUM (kWh)

Residential heat

demand/census

tract Area

MEDIUM

(kWh/m2)

< 5.000 1.799.079 1.562 2,42 1.538.364 14,74

5.000 –

10.000

1.152.143 5.031 6,32 2.047.588 23,93

10.000 –

30.000

1.311.541 10.246 9,65 1.981.453 33,34

30.000 –

80.000

859.221 26.851 15,73 980.546 51,91

> 80.000 846.322 169.264 21,29 904.828 71,07

Table 4.4: Overall residential energy demand (all fuels except electricity, not cooking uses) per

municipalities (classification by number of inhabitants per municipality):total, medium per municipality,

medium per census tract, and medium per census tract on urban area, 2008 (Source: evaluation from

SIRENA and from ISTAT)


The recent study on the construction sector in Italy, published by CRESME - Centre for Economic Research, Social, and Market for the Construction Sector and the Territory (“The construction market and the outlook for heating and air conditioning”), provides an estimate of the subdivision of the houses in Lombardy by typology (number of apartments that compose the building) and type of heating system (individual or centralized).

Regarding the number of buildings in Lombardy, about 75% are single or double family, while 13% of the buildings have more than 5 apartments. As for the number of apartments, more than 57% are located in buildings with more than 5 apartments (Table 4.5).

N apartments -

Lombardy

% apartments -

Lombardy % apartments - Italy

Single houses 702.311 15,3 % 24,0 %

Blocks of 2 apartments 692.665 15,1 % 16,0 %

Blocks of 3-4 apartments 576.236 12,5 % 12,6 %



Blocks of > 15 apartments 1.425.119 31,0 % 23,1 %

Total 4.600.478 100 % 100%

Table 4.5: Apartments per type of buildings in Lombardy and in Italy (total include blocks without apartments) (Source: CRESME, 2010)

As concern the typology of heating system, in Lombardy about 54% of the houses has an independent heating system, 35% centralized system, and the remaining 11% do not have a fixed heating system. Again at national level, the percentage of centralized systems is lower (25%).

As the existing district heating networks in Lombardy and previous studies (among which the aforementioned studies of CESI and the FP6 Project Biomass Use in Brianza) show, the buildings most likely connected to a district heating network are the large buildings and with central heating systems. Unfortunately information about typology of heating system at municipality level (and census tract level) are available only for 1991. However from the intersection between this data and statistics from the study of CRESME, it’s possible to extract a parameter at the municipal level, representing "the propensity to connection to DHS”, which may be used in BIOPOLE.

Another indicator of preference suitable for the situation of Lombardy, is the rate of heat demand satisfied by fuels other than natural gas. In Lombardy the distribution of natural gas is widespread and reaches 92% of municipalities; to exclude totally from the assessment of the potential energy demand for district heating the residential buildings served by natural gas does not reflect the real market of the DHS, but it remains true that it is more convenient - both from economic and environmental point of view - to replace an oil-fired plant with heat produced by the DHS.


4.3 HEAT DEMAND IN THE PUBLIC SECTOR

Many of the existing small – medium biomass district heating systems in Northern Italy were developed starting from the fulfilment of public buildings heat demand, because they often have a large heat demand and because the interest and the investment in DHS usually start by the Local Administration. This sector is therefore very important for the purposes of BioEnerGIS, and requires many efforts to evaluate its heat demand.

As for residential sector, also for public sector we use both information on heated surface (proxy for heat demand) and energy data of sample buildings. Unfortunately a complete census on the heated surface of all public buildings – as indeed for residential sector – isn’t available.

Regarding energy data sample, an important source of data, useful to evaluate the heat demand of public end-users in Lombardy, is the energy audit campaign carried out by Fondazione Cariplo, in the period 2006-2008. The founding program involved 650 municipalities below 30.000 inhabitants in the provinces of Lombardy (and also Novara and Verbania provinces, in Piedmont), that is more than one third of the small and medium towns of the region.

The information collected through the campaign is available on: www.webgis.fondazionecariplo.it.

Figure 4.7: Webpage of Audit–GIS db, Fondazione Cariplo (in the page is showed the yearly heat demand of the public schools of the Municipality of Seveso).

After three years of funding campaign, more than 4.000 “light” audits were made, while the detailed diagnosis, comprehensive of energy efficiency measures, exceed 1.500 units. Their database, Audit-GIS, used for our analysis, refers to 576 Lombard municipalities, corresponding to 3.477 buildings.


For each building, the available data are: building features and destination of use, types of heating system, heat and electric consumption, possible actions for energy saving and CO2 reduction, related benefits and costs, return periods of investments.

Figure 4.8: Municipalities involved in energy audit campaign promoted by Fondazione Cariplo in 2006-2008


Province

Municipalities Public buildings

No.

% of the

municipalities in

the Province

No. Overall surface

(m2)

Overall volume

(m3)

Bergamo 75 13,0% 391 454.067 2.342.700

Brescia 90 15,6% 568 658.549 3.169.749

Como 38 6,6% 204 186.475 814.153

Cremona 31 5,4% 142 123.677 540.781

Lecco 30 5,2% 169 198.045 800.965

Lodi 49 8,5% 194 165.818 690.296

Mantova 39 6,8% 255 249.106 1.257.942

Milano 103 17,9% 898 1.516.224 6.408.173

Pavia 35 6,1% 99 114.348 575.747

Sondrio 28 4,9% 178 216.755 891.123

Varese 58 10,1% 379 462.354 2.191.668

Total 576 100,0% 3.477 4.345.418 19.783.296

Table 4.6: Audit-GIS: number and percentage of the involved Municipalities, number, surface and volume

of the analysed buildings (Source: Fondazione Cariplo)

Figure 4.9: Intended use of buildings analysed in Audit-GIS (Source: Fondazione Cariplo)


Figure 4.10: Heating systems in buildings analysed in Audit-GIS (Source: Fondazione Cariplo).

Figure 4.11: Energy carriers in heating systems of buildings analysed in Audit-GIS (Source: Fondazione Cariplo).

4.3.1.1 Schools In Audit-GIS 1.322 schools and educational buildings are enclosed. From the db is possible to extract a representative index of energy consumption of schools (the average consumption in kWh/degree day, student), usable to estimate the consumption of all school buildings in Lombardy.

As proxy are known (from Regional School Office) the number of schools by type (kindergarten, primary school, secondary school of first and second grade), location (town and address) and number of students for the 2009/10 school year.


Total no. of schools Schools in Audit-GIS %

Bergamo 992 148 15 %

Brescia 1.128 211 19 %

Como 564 65 12 %

Cremona 337 44 13 %

Lecco 340 51 15 %

Lodi 206 72 35 %

Mantova 367 103 28 %

Milano 2.475 376 15 %

Pavia 480 37 8 %

Sondrio 242 65 27 %

Varese 736 150 20 %

Total 7.867 1.322 17 %

Table 4.7: Schools in Audit-GIS and total schools (Source: Fondazione Cariplo)

In the Table 4.7 is indicated the number of schools analyzed during the audit campaign per Province and their share in total public school buildings in Lombardy. For these schools are known location, volume and gross heated floor area, type of heating system, fuel type, number of students (as taken from Regional School Office), and heat consumption.

Figure 4.12: Average heat demand in the schools analysed in Audit-GIS (Source: Fondazione Cariplo).


From these data heat demand indicators were: kWh/m2, kWh/DD m2, kWh/DD student. It shows that about 90% of the buildings analyzed consumes more than 87 kWh/m2 year; according to the classification rules in the Lombardy Region (DGR VIII/8745), these buildings are in low energy efficient classes (energy classes D E F G) and only 9,5% appears to have good energy performance (energy classes A + and A). The following figure shows the average values of consumption and kWh/m2 and kWh/DD student. The estimated average consumption per unit of surface is 213,5 kWh/m2 year; the average consumption per student is 1.700 kWh/student year.

An information from the schools audits – not strictly of interest for BioEnerGIS, but interesting for energy planning – is that 18% of energy saving has been achieved through requalification measures of some of the schools audited.

From these energy data it’s possible to estimate the heat demand of all the schools in Lombardy. The schools buildings are about 5.300 (less than the number indicated in Table 4.7 because some schools are in the same building) and the overall estimated heat demand is about 2 millions MWh/y (165 ktoe). The average heat demand for school is 370 MWh/y.

The database from the Regional School Office includes information about the type of school, the number of students and address for each school. The address information allows to localize accurately each school and thus its heat demand (measured or estimated).

Figure 4.13: Heat demand for schools: detail of Sarnico, BG (Source: audits and estimations)


4.3.1.2 Hospitals and surgeries In hospitals energy is used for different uses in the form of electricity, heating and cooling.

The complexity of the energy system of each structure, as well as the volume of buildings is closely related to:

• health activities that take place in it; • geographical location (climate zones); • the type of building and of equipments.

Therefore, energy consumption can vary widely from hospital to hospital. The use of different energy sources leads also to different technological options and consequently to different operating costs and levels of emissions.

The largest share of energy consumption of a hospital is due to heat at low temperature (< 100 °C) required for space heating and to compensate heat losses arising from the need for ventilation. Large kitchens and laundry facilities are among the greatest energy consumption in hospitals. Another heat use, but at higher temperatures, is the disinfection and sterilization. While heating and air conditioning obtain energy from the heating system using hot water as heat carrier, the other users need higher temperature. For this reason, many hospitals are equipped with steam boilers at medium or high pressure, and sometimes with power much higher than the actual need.

In addition to Audit-GIS, in collaboration with the Regional Health Department of Lombardy, an ad hoc survey was conducted by sending questionnaires to all hospitals and local Health Departments.

The questionnaire asked, for each structure, an indication of location, volume and/or surface, number of beds, fuel consumption (in particular for heating), type of fuel (natural gas, diesel, etc.).

About 150 hospitals (about 65% of the total) responded to the survey, providing timely information on energy consumption and volume heated.


Figure 4.14: Energy consumption for heating (2008) in the hospitals from the questionnaire answers

For the calculation of energy demand of the remaining 35% hospitals, since for these the only available figure was the number of beds, it was first calculated the specific consumption in MWh/bed using data gathered with the survey conducted with the Lombardy Region Health Department. The average value is about 23 MWh/bed. This average value was fully comparable with the values indicated in the following table by FIRE (Italian Federation for Rational use of Energy) - ENEA (National Agency for New Technologies, Energy and Sustainable Economic Development), as reported in the survey presented at the Seminar on "Energy in hospitals", held by AICARR - Italian Association for Air Conditioning, Heating and Refrigeration in Milan (May 2009).

For the purposes of calculation of heat demand we used the specific value (26 MWh/bed) corresponding with the average value of the table. For some hospitals, for which it is not known the number of beds, the energy consumption was calculated according to the area, using the specific value in kWh/m2 corresponding to the average value of the table from FIRE - ENEA.

[MWh/bed] [kWh/m 2] [kWh/m 3]

Tot. heat electricity Tot. heat electricity heat electricity

Minimum 25 23 2,5 430 380 50 108,6 14,3

Maximum 30 29 3,5 535 465 70 132,9 20

Table 4.8: Reference values for energy consumption as a function of number of beds and surface in Italian

hospitals (FIRE - Italian Federation for Rational use of Energy, ENEA - National Agency for New

Technologies, Energy and Sustainable Economic Development)


For some structures (35%), for which it’s available the number of beds, the energy consumption was calculated on the basis of the surface, using the specific value in kWh/m2 corresponding to the average value of the FIRE-ENEA table.

Summing up the data obtained from the survey carried out in cooperation with the Health Department with the estimates made as described above, we obtain a total consumption of all health facilities amounting to approximately 1.500 GWh per year.

The distribution by energy carrier, with its incidence rate, it summarized in Table 4.9.

Table 4.9: Overall heat demand of hospitals (Source: survey and estimations)

Figure 4.15: Heat demand for hospitals and for medical offices (ASL) in Lombardy – detail of Saronno, VA (Source: survey and from estimations)

Fuel Heat demand (hospitals with

data from survey) [kWh/y]

Heat demand (hospitals with

data estimated)

[kWh/y]

Natural gas 988.102.765 80,7%

340.047.500

Diesel 13.397.573 1,1%

District heating 117.498.223 9,6%

Fuel oil 104.073.274 8,5%

Biomasses 739.400 0,1%

TOTAL 1.223.811.236 340.047.500

1.563.858.736


4.3.1.3 Pools, gyms and sport centres The evaluation of the heat demand of sports facilities has been drawn up from databases provided by the Sport and Young people General Department and by Fondazione Cariplo.

Sports facilities in the two databases are single gymnasiums, sports centres, sports halls, swimming pools, bowling alleys, soccer, football, tennis, golf club, equestrian centres, boating (for activities conducted outside the energy relate to the changing rooms for athletes and spaces for recreational activities). The structures of the regional database include both public facilities (some of which related to schools), parochial centres, and private structures.

The database of the Sport and Young people General Department includes about 2.300 buildings. It was built primarily for the purpose of recording the sports facilities on the Lombardy region in relation to the types of sports activities. The database doesn’t contain information on energy consumption or on the heated volume of facilities. Generally only the size of areas dedicated to each sport (length and width of fields, swimming pools, gyms, ...) is indicated.

Instead it is recorded the address, and that made possible the punctual geo-referencing of each centre.

Audit-GIS includes energy audits carried out on 413 sports facilities; from the audits are known thermal and electrical energy consumption and the heated surface and gross heated volume.

The total heat demand of the 413 sport facilities is 88.000 MWh (8 ktoe).

median

[kWh/m 2 y]

median

[kWh/m 2 DD y] median

[kWh/building]

Boules 108,12 0,04 78.195

Sports field 272,90 0,11 92.100

Sports centre 213,05 0,08 170.630

Sports facility 268,78 0,11 105.140

Sports hall 174,64 0,07 291.310

Gym 180,48 0,07 158.780

Changing room 186,60 0,08 153.412

Stadium 330,79 0,15 92.100

Pool 698,06 0,29 920.414

Overall 195,68 0,08 -

Table 4.10: Median value of heat demand per surface, per surface and degree-days and per building in

different typologies of sports facilities (Source: elaboration from Audit-GIS)

To estimate the energy demand of sport facilities not audited was carried out an analysis of the data from Audit-GIS, grouping structures per typology and calculating the median of the energy consumption. Given the size of the sample, the median values per surface and for the different typologies of facilities can be considered sufficiently indicative, except for the activities of boating, skiing and indoor riding centre, which are present in low numbers also in the regional database.

Unfortunately the information on the surface of the sports buildings included in the regional db are not related to the heated surface or to the overall surface of the building. The use of the average energy


consumption by typology of sports facility is not an high quality energy data, but, having a good quality location, and recognizing that sports facilities are an interesting final user for the implementation of district heating systems, it was decided however to include this estimate in calculating the total heat demand.

The head demand of the sports facilities in Lombardy is valued at 350.000 MWh/y (30 ktoe/year).

Figure 4.16: Energy consumption for heating in sport facilities in Lombardy – detail of Casalpusterlengo, LO (data from audits and from estimations)

4.3.1.4 Other public buildings As shown in Figure 4.9, Audit-GIS contains energy audits of public buildings with uses different from those described in previous chapters, including public buildings for offices (administrative buildings), libraries, recreation centres, residential and social welfare facilities.

A comprehensive regional database, including information on the localization and other data usable as proxies of the energy demand, of these typologies of buildings is available only for libraries . For them, combining data from two different db (ISTAT provides the surface of the libraries in each municipality in Lombardy; Lombardy Region has the list of public libraries with their addresses), the georeferenced area for each library could be obtained (or estimated, if there are more libraries in the same municipality)

Analyzing the audit of the 60 libraries surveyed, can be derived the median of the thermal energy consumption per unit of area and per degree day: 0,06 kWh/m2 dd.

The average heat demand per Municipality is 38.000 kWh/y and the average heat demand per surface is about 200 kWh/m2.


Thanks to this specific consumption per unit of area and per degree day is then possible to estimate the heat demand of the about 1.300 public libraries in Lombardy: about 58.000 MWh/y (5 ktoe/y).

Figure 4.17: Energy consumption for heating in public administrative, recreation centers, libraries, residential and social welfare facilities in Lombardy– detail of Osnago, LC (data from audits and from

estimations)

Regarding the other typologies of public buildings, the audits report data on:

• 626 administrative buildings (106.300 MWh/y, 9 ktoe/y, 207 kWh/m2, 0,08 kWh/m2dd),

• 148 recreation centers (11.600 MWh/y, 1 ktoe/y, 148 kWh/m2, 0,06 kWh/m2dd),

• 64 residential and social welfare facilities (18.800 MWh/y, 2 ktoe/y, 245 kWh/m2, 0,09 kWh/m2dd).

The total heat demand corresponding to these 839 buildings amounts to about 137.000 MWh/y (12 ktoe/y).

Another source of measured data on energy consumption of public buildings is the Energy Management of the Lombardy Region.

Lombardy Region implemented the energy management as required by the Energy Action Plan adopted by DGR VIII/4916 regarding the use of energy in buildings, by the Law 10/91 “Rules for the implementation of the National Energy Plan in the field of rational use of energy, energy conservation and developing renewable sources of energy”, and by the Legislative Decree 195/2005 “Implementation of Directive 2003/4/EC on public access to environmental information”.

The monitoring and analysis of energy consumption of buildings and vehicles of Lombardy Region has been assigned to CESTEC.


Annually are collected data of thermal energy (in addition to electricity and fuel for transport) of the 19 institutional venues of Lombardy Region (STeR). The data are computerized and aggregated for the periodical report to FIRE of overall energy consumption of the institution.

The total heat demand corresponding to these 19 buildings amounts to about 12.000 MWh/y (1 ktoe/y). The buildings are located in all the Province capitals and in Legnano (MI), but the biggest are in Milan (Figure 4.18).

Figure 4.18: Share per municipalities of the heat demand in Lombardy Region venues (Source: Lombardy Region Energy Management, Cestec)

4.4 HEAT DEMAND IN THE TERTIARY SECTOR

4.4.1.1 Commercial end-users The shops in Italy are classified, as required by Legislative Decree 114/1998, in:

• “large sales structures” (sales area greater than 1.500 m2 in municipalities with less than 10.000 inhabitants and sales area greater than 2.500 m2 in municipalities with more than 10.000 inhabitants),

• “medium sales structures” (sales area between 150 m2 and 1.500 m2 in municipalities with less than 10.000 inhabitants and sales area between 250 m2 and 2.500 m2 in municipalities with more than 10.000 inhabitants),

• “neighbourhood shops” (sales area lower than 150 m2 in municipalities with less than 10.000 inhabitants and sales area of lower than 250 m2 in municipalities with more than 10.000 inhabitants).

In 2008 (data from Lombardy Region General Direction for Trade, Tourism and Services – Trade

Observatory) there were 18.937 alimentary shops plus 86.193 non alimentary shops within the neighbourhood shops, corresponding to a surface of about 6,5 millions m2.


However the only available information is the number of shops and the overall surface per municipality; no information on localization of these shops is available. Furthermore little-size shops are usually in the same buildings of houses.

For these reasons, the consumption of the neighbourhood shops were not considered for the purposes of BioEnerGIS survey.

The shops classified as “medium sales structures” are more than 8.100, with an overall surface of 5 million m2; the “large sales structures” in Lombardy are about 470, corresponding to a surface of 3,4 million m2. In this case the address and the surface for each shop are available.

Energy data of this typology of end users are obtained from a sample of medium-large shops in Lombardy; using an average value of 0,07 kWh/m2dd, the estimated total heat demand is 869.000 MWh/y (75 ktoe/y) for medium sales structures and 580.800 MWh/y (50 ktoe/y) for large sales structures.

Figure 4.19: Energy consumption for heating in medium and large sales structures (Source: audits and estimations)

4.5 HEAT DEMAND IN THE PRODUCTION SECTOR

Regarding the industrial sector, regional analyses of heat consumption for heating or hot water, or for other uses at the temperatures generally available by the district heating, are not available. Besides, there isn’t any database of enterprises in Lombardy with information on location and proxies usable to estimate the heat demand.


Energy demand in the industrial sector (split between companies under Emission Trading System or not) for each municipality of Lombardy and per energy carriers is instead available.

From the experience made in the analysis of use of solar thermal energy in industry and from audits, it’s possible identify areas of enterprises whose heat demand could be fulfilled by heat from district heating networks. We then proceeded to estimate the proportion of non-electric energy consumers useful to BioEnerGIS purpose for each municipality by the percentage of employees in enterprises listed in Table 4.11 on the total number of employees (ISTAT, 2001).

Industry

Alimentary industries

Wine and beer production

Laundry

Services for tourism

Services for pets

Galvanic industry

Cosmetic industry

Heating and hot water in all industries

Table 4.11: Industrial sectors whose heat demand could be fulfilled by heat from district heating networks

Figure 4.20: Heat demand (suitable for DHS) in industrial sector


4.6 SURVEY RESULTS

4.6.1 Residential sector

As described in chapter 4.2 the output of the residential data analysis gives kWh/y per municipality and per census tract (estimation), per fuel (estimation), in the period 2000-2008. Census tracts are georeferenced, so it’s possible to convert the amount of energy demand per census area into amount per cell (as required by BIOPOLE).

The overall residential energy demand suitable for DHS amounts to 70.000 GWh/y (6.000 ktoe/y).

The residential heat demand (suitable for DHS) per inhabitant in Lombardy is 7.300 kWh/inhabitant.

4.6.2 Public sector

Schools

The schools buildings in Lombardy are about 5.300; the average heat demand for school is 370 MWh/y. The overall estimated heat demand is about 2.000 GWh/y (165 ktoe/y).

Hospitals and Surgeries

Summing up the data obtained from the survey carried out in cooperation with the Health Department

of Lombardy Region with the estimates made using the specific value of 26 MWh/bed, total

consumption of all health facilities amounts to approximately 1.500 GWh per year (135 ktoe/y).

Pools, gyms and sport centres

The heat demand of the sports facilities in Lombardy is valued at 350 GWh/y (30 ktoe/y).

Libraries

It is possible to estimate the heat demand of the about 1300 public libraries in Lombardy in about 58 GWh/y (5 ktoe/y).

Other Public Buildings

The total heat demand corresponding to 858 public buildings of other typology amounts to about 150 GWh/y (13 ktoe/y).

4.6.3 Tertiary sector

The estimated total heat demand for medium sales structures (more than 8.100 stores) is 869 GWh/y (75 ktoe/y) and 580 GWh/y (50 ktoe/y) for large sales structures (about 470 devices).

4.6.4 Production sector

The estimated heat demand potential in the production sector for the uses satisfable by DHS amounts to 2.300 GWh/y (200 ktoe/y).


5 ASSESSMENT OF THE HEAT DEMAND IN NORTHERN IRELAND


Heat demand from final bioenergy users in the residential sector was assessed on the basis of data obtained from sources listed in Table 5.1. Heat demand was estimated for buildings in the residential sector by initially querying data from the OSNI Pointer point geographic dataset for all residential buildings: these data were then joined in a GIS to the base mapping dataset containing building footprint areas.

Data on average annual heat demand use in kWh for residential buildings were gained from the CIBSE data report (see Table 2.1) appended to the residential buildings dataset.

Due to the size of the residential building dataset, which was well in excess of 600.000 records, it was decided to aggregate the point dataset containing heat demand use of residential buildings into municipal Ward polygons.

This dataset was then gridded (cell of 500 m*500 m) for the BIOPOLE input database.

Table 5.1: Residential sector buildings mapped for heat demand use

As showed in Table 5.2 and in Figure 5.1, higher concentrations of heat demand occur in the East of the Northern Ireland region, centred around the urban centres of Belfast and in the urban belt South West of Belfast to Dungannon. High values are also around the other urban areas including Derry and Coleraine in the North West of the region, Enniskillen and Omagh in the South West and Ballymena in the North.

Building Type Heat demand (kWh/y) County

Residential

8.107.513.040 Antrim

1.686.577.452 Armagh

1.882.924.451 Derry

5.835.281.307 Down

1.672.586.113 Tyrone

932.345.330 Fermanagh

20.117.227.694 Northern Ireland

Table 5.2: Cumulative heat demand for residential sector buildings summarised at County level

Dataset Source Age of Data Resolution

Residential buildings OSNI (Pointer) 2010 Building level


Figure 5.1: Residential heat demand at Municipal Ward level


Heat demand was estimated for buildings in the public sector using the datasets listed in Table 3.3.

Locational information on Public Sector Buildings acquired from the Northern Ireland Statistical and

Research Agency and from the Northern Ireland Mapping Agency was used to locate buildings. These point datasets were then appended to the Northern Ireland Mapping Agency base mapping dataset containing building footprint areas.

Average annual heat consumption values in kWh per metre squared for different public sector building types (Table 2.1) were then appended to each building type allowing heat demands to be calculated.

These datasets containing the heat demand data for public sector buildings was then set for entry onto the BIOPOLE system.

Data were also acquired from Department of Enterprise, Trade and Investments (DETINI) on the heat demand of buildings in the public sector in the region. Heat demand information was mapped at postcode level on the data acquired. Data acquired from DETINI can be seen in Table 5.3.

Dataset Source Age of data Resolution

Hospitals NISRA/ DETINI/OSNI (Pointer) 2010 Building level

Leisure centres NISRA/ DETINI/OSNI (Pointer) 2010 Building level

Libraries NISRA/ DETINI/OSNI (Pointer) 2010 Building level

Secondary schools NISRA/ DETINI/OSNI (Pointer) 2010 Building level

Primary schools NISRA/ DETINI/OSNI (Pointer) 2010 Building level



Grammar schools NISRA/ DETINI/OSNI (Pointer) 2010 Building level

Nursery schools DETINI 2010 Postcode level

Universities DETINI 2010 Postcode level

HE-FE colleges DETINI 2010 Postcode level

Community centres DETINI 2010 Postcode level

Offices DETINI/OSNI (Pointer) 2010 Postcode level

Industrial LE’s DETINI 2010 Postcode level

Industrial SME’s DETINI/OSNI (Pointer) 2010 Postcode level

Table 5.3: Public sector buildings mapped for heat demand use.


Heat demand was estimated for buildings in the tertiary sector using datasets that are listed in Table 3.3.

Buildings from the tertiary sector were queried from the Pointer dataset and are listed in Table 5.4 and then joined to the base mapping dataset containing building footprint areas.

As described in Chapter 2.1.2, information on average annual heat demand use per building type was then appended to the selected building polygons and heat demand was calculated based on building footprint area and average annual heat consumption use per metre squared per building type from the database acquired from CIBSE (see Table 2.1).

Data were also acquired from DETINI on the heat consumption of office buildings in the region. Data were acquired at office building postcode level and appended to the existing dataset for office buildings already created.

The input database for BIOPOLE DSS contains heat demand data for tertiary sector buildings as shapefile dataset.


Office Buildings DETINI / Pointer 2010 Building level

Retail Buildings Pointer 2010 Building level

Table 5.4: Tertiary sector buildings mapped for heat demand use


The survey on public and tertiary (commercial and service) sectors shows that higher heat demand

values are in the East of Northern Ireland centred around Belfast, and also in the North West around

the urban centre of Derry. Large concentrations of heat demand are also in the South of the region

centred around the urban centres of Enniskillen and Omagh, and in the North, centred around the

urban centres of Ballymena and Coleraine. This information is showed in Figure 5.2.

Figure 5.2: Tertiary sector buildings heat demand use


Heat demand was calculated for buildings in the production sector using datasets that are listed in Table 3.3. Buildings from the production sector were queried from Pointer dataset and then appended to the base mapping dataset containing building footprint areas.

Data on average annual heat consumption use in kWh per metre squared (see Chapter 2.1.2 and Table 2.1) were then appended to the base mapping data and heat demand was calculated for all buildings based on the footprint area of the buildings.

Data were also acquired from the DETINI on the energy consumption of Large Industrial Units and mapped at postcode level: a point dataset containing annual heat/energy demand was thus created for Industrial Large Enterprise Units.



Industrial Buildings Small &

Medium Enterprise (SME) DETINI/ Pointer 2010 Building level

Industrial Buildings Large

Enterprise (LE) DETINI/ Pointer 2010 Building level

Table 5.5: Production sector buildings mapped for heat demand use

This dataset containing the heat demand data for industrial buildings was then gridded for entry onto the BIOPOLE database.

Data was mainly attained from the DETINI via an external energy consultant. Small and medium enterprise buildings were mapped using data mainly supplied from the DETINI. Large concentrations were had in the south and in the middle of region, both at cement works who are large heat demand users. High levels of heat demand were also experienced for buildings ranging from Belfast to Dungannon in the East of the province as shown in Figure 5.3.

This dataset can be upgraded annually via the NISRA census website and via the DETINI website, which recently produced a report on heat consumption use.

Building Type Total Value (kWh) County

Industrial LE

Antrim

Armagh

1.529.274 Derry

Down

Tyrone

801.000.000 Fermanagh

Industrial SME

1.273.960 Antrim

622.210 Armagh

1.529.274 Derry

460.471 Down

1.476.788 Tyrone

141.869 Fermanagh

808.033.845 Northern Ireland

Table 3.7: Table listing accumulative heat demand for industrial LE and SME at County level


Figure 5.3: Industrial sector buildings mapped for heat demand use

5.5 SURVEY RESULTS


Heat demand was estimated for buildings in the residential sector. Point locations were averaged into Ward areas. Values ranged from 7 MWh to 266 MWh per Ward area. Concentrations were had in the urban areas and included Belfast, Derry, Coleraine, Enniskillen and a residential belt south west of Belfast. The shapefile has 166 polygons covering the municipalities in the Northern Ireland.

5.5.2 Public sector

Heat demand was estimated for a number of buildings in the public sector including Grammar schools, Primary schools, Secondary schools, Nursery schools, Universities, HE-FE Colleges, Education centres, Community centres and libraries. Concentrations were had around the main urban areas including the towns of Belfast, Derry, Ballymena, Coleraine, Omagh, Enniskillen, Portadown, Lurgan. Results were also acquired for the secondary and grammar schools with concentrations also around the urban areas listed above.

Heat demand was also estimated for leisure centres with concentrations again around the main urban areas listed above including Belfast and Derry: heat demands were again high in the range of 5 to 8 MWh.

Derry

Belfast


Heat demand was estimated for hospitals with concentrations in the urban areas of Coleraine, Enniskillen, Belfast and Antrim. Heat demands were high in the range of 4 to 11 MWh.

5.5.3 Industrial sector

Heat demand was estimated for buildings of large and small and medium enterprise using data supplied by NISRA and data supplied by an externally contracted energy consultant.

Values ranged from 2,5 MWh to 463 MWh with largest concentrations in the cement and chemical plants in the region.

Small and medium enterprise buildings were mapped using information supplied by an externally contracted energy consultant. Concentrations were had around the major urban areas and included buildings in Derry, Belfast, Ballymena and Cookstown.


Heat demand was estimated for buildings in the tertiary sector including office buildings. Information on offices was acquired from NISRA website on the locations of office buildings.

Heat demand was calculated and ranged from 21 kWh to 3,4 MWh with concentrations had around the main urban areas including Belfast, Derry, Ballymena, Cookstown, Enniskillen and Omagh.


6 ASSESSMENT OF THE HEAT DEMAND IN SLOVENIA

6.1 BACKGROUND

The selection of the buildings to be mapped is based on identification of potential heat demand users. As the dataset of buildings includes all buildings in Slovenia, buildings with existing district heating systems and buildings that generally don’t use energy for heating (such as water pumping stations, water storage, plant nursery, …) were excluded from mapping.

To include Slovenian regional characteristic into energy consumption estimations, data on average air temperature on different location in heating intensive months (January, February, November and December) was collected from Environmental Agency of Republic of Slovenia (see Chapter 6.1.2).

Before presenting in detail the assessment of the heat demand carried out under BioEnerGIS, in the following pages are shown the main energy figures for Slovenia.

6.1.1 Energy statistics

In 2008 consumption of liquid fuels increased by as much as 16% compared to the previous year. The

largest increase was recorded in consumption of diesel fuel in transport by 20%; consumption of diesel

fuel in Slovenia almost doubled in the last five years: this is due to rising passenger, and even more

merchandise road transport. In 2008, an increased consumption of fuel oil for household heating by is

recorded (10% in comparison to 2007). The reason may be found in a colder winter and long lasting

heating season. Average temperature in selected cities in Slovenia in the first three months of 2008,

were in fact for an average of 2 degrees lower than in 2007, and for as much as 4 degrees lower in

April. In 2008, the energy dependence of Slovenia reached 55%. Regarding energy supply, Slovenia is

totally dependent on imports of liquid and gaseous fuels. With domestic production, 77% of our need

for solid fuels is covered and almost all needs for renewable energy sources. However, solid fuels and

renewable energies together represent only 10% in final consumption of all energy in Slovenia.

Therefore, according to the steeper rise in consumption of liquid fuels, further growth of energy

dependence of Slovenia is expected in the coming years.


Figure 6.1: Final consumption of energy per sector in Slovenia (Source: Statistical office of the Republic of Slovenia, 2008; Institut Jožef Stefan).

The Table 6.1 lists the data collected on energy consumption for the period 2002 - 2007. The data are

arranged by sectors of consumers and calculated according to consumption data from 2000. The table

shows that the price of energy achieved a high level in 2002, and the highest in 2007. In the future,

further decline in intensity is to be expected.

2002 2003 2004 2005 2006 2007

End energy [ktoe/mio € 2000] 884 935 922 897 865 781

Industry and construction [ktoe/mio € 2000] 60 59 59 60 59 63

Traffic [ktoe/mio € 2000] 587 631 622 597 580 522

Households [ktoe/mio € 2000] 60 39 41 34 30 25

Services and agriculture [ktoe/mio € 2000] 177 206 200 206 196 171

Table 6.1: Change in energy intensity of the common end-use energy and by sector in the period 2002-

2007 (Source: Statistical office of the Republic of Slovenia, 2008)


Figure 6.2: Production of electricity from renewables, and aim to 2010 ReNEP and growth of electricity

production from RES and gross electricity use during the period 2000-2007 (Source: Statistical office of

the Republic of Slovenia, 2008).

The share of electricity produced from renewable energy sources in Slovenia in 2008 reached 26%.

Production of electricity from renewable energy sources is highly dependent on hydrological

conditions through each year. More than 90% of electricity from renewable energy sources is still

produced in hydroelectric plants. Utilization of landfill gas and biogas for energy purposes is

increasing quite slowly.


2000 2005 2006 2007 2008

Indigenous production (1.000 toe) 3.152 3.495 3.446 3.456 3.672

Total primary energy supply - TPES (1.000

toe) 6.487 7.307 7.318 7.336 7.749

Total final consumption - TFC (.1000 toe) 4.638 5.182 5.232 5.191 5.521

Energy dependency (%) 52 52,6 52 52,7 55,3

Energy efficiency (%) 71,5 70,9 71,5 70,8 71,2

Energy intensity - TPES/GDP (toe/mio € 2000) 351 330 312 293 299

Energy intensity - TFC/GDP (toe/mio € 2000) 251 234 223 207 213

Electricity consumption/GDP (MWh/mio €

2000) 577 581 567 533 500

TPES / capita (toe/cap.) 3.293 3.647 3,64 3.621 3.813

TFC / capita (toe/cap.) 2.354 2.587 2.602 2.562 2.717

Electricity consumption per capita (kWh/cap.) 5.413 6.425 6.615 6.584 6.369

Share of electricity from renewable sources in

total electricity production (%) 28,6 23,7 24,5 22,5 26,3

Share of electricity from renewable sources in

gross consumption of electricity (%) 31,7 24,2 24,4 22,1 29,1

Carbon intensity (t/toe) 2,3 2,3 2,3 2,3 -

Table 6.2: Energy Indicators, 2000, 2005 - 2008 (Source: Statistical office of the Republic of Slovenia, 2008)

Final energy consumption in Slovenia is still based mainly on fossil fuels, as illustrated in Figure 6.3.

Figure 6.3: Final energy consumption, 2008 (Source: Statistical office of the Republic of Slovenia, 2008)


Figure 6.4: Final energy supply (electric energy market not included), 2008 (Source: Statistical office of the Republic of Slovenia, 2008)

GWh

Gross production-TOTAL 16.398

Gross production-Hydroelectric power plants 4.018

Gross production-Thermal power plants 6.107

Gross production-Nuclear power plant 6.273

Net production-TOTAL 15.357

Net production-Hydroelectric power plants 3.959

Net production-Thermal power plants 5.425

Net production-Nuclear power plant 5.972

Import 6.218

Export 7.820

Losses in the network 809

Final consumption-TOTAL 12.945

Final consumption-Energy sector 219

Final consumption-Manufacturing and constr. 6.233

Final consumption-Transport 196

Final consumption-Households 3.182

Final consumption-Other consumers 3.115

Table 6.3: Balance of production and consumption of electricity (GWh), 2008 (Source: Statistical office of

the Republic of Slovenia, 2008)

6.1.1.1 CO2 emissions per capita

In 2003 Slovenia caused 19.8 million tons of CO2 equivalent GHG emissions. Most were carbon dioxide (81%), followed by emissions of methane (10%), nitrous oxide (8%) and F-gas (1%), which have a low level of emissions, but a large greenhouse potential. Most pollution is caused by energetics (with a turnover value) (80%), followed by agriculture (10%), industrial processes (6%) and waste (4%).


Each inhabitant of Slovenia contributes to the creation of almost 10 tons of CO2 equivalent per year on average. This exceeds the global average, which is 4 tons of CO2 equivalent per capita, and - what is much worse - dramatically overcome the level of greenhouse gas emissions that would be sustainable (average of 2 tons of CO2 equivalent per capita per year). In Slovenia, the allowable emissions CO2 per capita are exceeded. We have huge reserves in the households. Projections show rise tendency about 1,8% a year.

6.1.1.2 Energy saving

An analysis of final energy consumption by sectors allows assessment of progress in the field of energy efficiency and reducing energy use policy. Consumption of final energy of fossil origin has a direct impact on emissions of air pollution and greenhouse gases. Reducing final energy consumption is important both in terms of ensuring reliable energy supply, economic competitiveness, as well as in terms of reducing environmental impact through reduction of greenhouse gases emissions and air pollution.

There is a wide range of policies, affecting final energy consumption, grouped under the name Measures to increase energy efficiency. In Slovenia these measures are combined in the Resolution on the National Energy Program and for their impact on greenhouse gas emissions also in the Operational Program for reducing greenhouse gas emissions by 2012. In 2008 in the context of the implementation of the end-use efficiency and energy services (Directive 2006/32/EC) the first of the three action plans on energy efficiency was adopted. The action plan consists of a package of measures which will result in more efficient use of energy in all end-use sectors, with emphasis on the public sector which should set an example.

Savings resulting from actions is estimated at 4.261 GWh in the period 2008-2016. The Directive stipulates that Member States are to achieve energy savings of 9% of energy efficiency measures in nine years from 2008. The action plan for energy efficiency (COM 2006 (545)) sets a target of 20% energy savings depending on its anticipated use in 2020.

6.1.2 Climate coefficient by regions

As before mentioned, data on average air temperature on different location in heating intensive months

was collected from Environmental Agency of Republic of Slovenia (Table 6.4) to include such regional

characteristic into energy consumption estimations.

The data on average monthly temperature were compared to building project temperature (22° C) by

calculating the difference. The data for municipality Ljubljana (Osrednjeslovenska region) was used

for weighting – all the other data on temperature difference. Finally the average data for four months

period was calculated.


AVERAGE TEMPERATURE 2009

REGION MUNICIPALITY Meteorological Station JAN

needed

temp.

change

% FEB

needed

temp.

change

% NOV

needed

temp.

change

% DEC

needed

temp.

change

%

4 -month.

relative

change (%)

Goriška Nova Gorica Bilje 3,6 18,4 0,78 3,8 18,2 0,92 9,6 12,4 0,86 4,4 17,6 0,88 86,05

Savinjska Celje Celje -1,9 23,9 1,02 1,5 20,5 1,04 7,1 14,9 1,03 1,6 20,4 1,02 102,63

jugovzhodna Črnomelj Črnomelj, Dobliče -2,2 24,2 1,03 2,1 19,9 1,01 7,9 14,1 0,97 2,4 19,6 0,98 99,81

Notranjsko kraška Ilirska Bistrica Ilirska Bistrica -0,1 22,1 0,94 1,9 20,1 1,02 7,9 14,1 0,97 2,6 19,4 0,97 97,58

jugovzhodna Kočevje Kočevje -2,8 24,8 1,06 0,5 21,5 1,09 6,2 15,8 1,09 1,4 20,6 1,03 106,66

Gorenjska Radovljica Lesce -3,3 25,3 1,08 0,5 21,5 1,09 5,1 16,9 1,17 0,4 21,6 1,08 110,34

Osrednjeslovenska Ljubljana Ljubljana, Bežigrad -1,5 23,5 1,00 2,3 19,7 1,00 7,5 14,5 1,00 2 20 1,00 100,00

Podravska Maribor Maribor -1,5 23,5 1,00 1,9 20,1 1,02 6,8 15,2 1,05 1,9 20,1 1,01 101,84

Pomurska Murska Sobota Murska Sobota -1,8 23,8 1,01 1,8 20,2 1,03 6,2 15,8 1,09 1,5 20,5 1,03 103,82

jugovzhodna Novo mesto Novo mesto -1,7 23,7 1,01 2 20 1,02 7,4 14,6 1,01 2,1 19,9 1,00 100,64

obalno kraška Piran Portorož, Letališče 4,5 17,5 0,74 5,2 16,8 0,85 10,8 11,2 0,77 5,7 16,3 0,82 79,62

notranjsko kraška Postojna Postojna -1,3 23,3 0,99 1,2 20,8 1,06 7,2 14,8 1,02 1,8 20,2 1,01 101,95

Savinjska Rogaška Slatina Rogaška Slatina -2,1 24,1 1,03 1,8 20,2 1,03 6,6 15,4 1,06 1,7 20,3 1,02 103,20

Goriška region Vipava Slap pri Vipavi 3,8 18,2 0,77 4,5 17,5 0,89 9,6 12,4 0,86 4,5 17,5 0,88 84,82

koroška Slovenj Gradec Šmartno pri Slovenj Gradcu -2,8 24,8 1,06 0,4 21,6 1,10 5 17 1,17 -0,3 22,3 1,12 110,98

Savinjska Velenje Velenje -2,2 24,2 1,03 1,3 20,7 1,05 6,1 15,9 1,10 1,3 20,7 1,04 105,30

gorenjska Bohinj Bohinjska Češnjica -3,3 25,3 1,08 -0,3 22,3 1,13 4,6 17,4 1,20 0 22 1,10 112,71

spodnjeposavska Brežice Cerklje ob Krki -2,1 24,1 1,03 1,9 20,1 1,02 7,8 14,2 0,98 2,1 19,9 1,00 100,50

zasavska* no data - same as savinjska -1,9 23,9 1,02 1,5 20,5 1,04 7,1 14,9 1,03 1,6 20,4 1,02 102,63

Table 6.4: Average temperature in Slovenia, 2009 (Environmental Agency of Republic of Slovenia)



At the 2002 census there was 777.772 apartments in Slovenia. Number of households in individual houses was 379.519, number of households in multi-apartment buildings was 367.001, of which 131.104 in dwelling buildings, 235.897 in the three-or more apartment buildings. Based on the analysis made, which was carried out by sample surveys and measurements, the characteristics of energy consumption in households can be defined. The survey was conducted in four cities area from different geographical areas. Randomly selected respondents reported on the method and sources of energy for space heating and hot water, the average annual electricity consumption, installed devices for heating purposes (electric water heater, stove, electric thermal storage), and their daily operating hours. The number of apartments, included in the survey is relatively small, but the received data is justifiable since different apartments are similarly equipped. Survey on space heating does not contain the data of individual energy consumption from different energy sources. The calculations are supported by the calculated data on energy use of electric thermal storage and by the Statistical Yearbook of energy economy (Project Slovenija znižuje CO2 – CO2

reduction in Slovenia). In the coldest months (January, February and December) 48% of the total consumption of electricity for space heating purposes is used (Statistical Office of the Republic of Slovenia), while the remaining 52% is consumed during the transitional period (usually from 15 October to 15 April). Obtained from the survey data, an estimation of energy consumption for space heating of all households was given, which is shown in the next picture. The transfer of surveys and measurement of the total number of households gives an average power consumption of 3.713 kWh/y, which is 2,7% less compared to the statistical data (3.815 kWh/y). The results are not time defined but represent the usage of power class in this period.

Figure 6.5: Consumption of various energy sources for heating purposes in households (Source: Statistical Office of the Republic of Slovenia)


The highest density of electric thermal storages (ETS) system is in the area of Koper Littoral, where winters are milder than in the rest of Slovenia. ETS systems for building spaces heating are primarily used for modernization of old buildings. In Slovenia, more than 31% of all housing was built by 1945. Such apartments in the vast majority did not have central heating installed. ETS systems are also commonly used as an additional heating during the transitional period. Since the usage of ETS is linked to night-time off-peak, it gives a very positive effect on the electricity industry and represents no noticeable limits to the users.

According to the yearbooks (Statistical Office of the Republic of Slovenia, Institut Jožef Stefan) in recent years the number of homes equipped with central heating greatly has increased. According to statistics of 1983, 41% of apartments were equipped with central heating; according to the census in 1991 the share reached the total of 63%. Significant increase in natural gas consumption for space heating shows many advantages of this energy source from other sources. The high efficiency of modern gas appliances, environmentally friendly combustion and competitive price are the most important advantages of gaseous fuels for heating homes and domestic hot water.

Evaluation of potential changes in daily chart of electricity consumption based on survey results

The daily graphs of electricity consumption for heating purposes in households differ through periods of the year mainly because of the usage of ETS systems, operating (overlook minimal use during the summer season) only in the heating and in the transitional season. That is why the daily charts are made for heating, transitional and summer season. The vast majority of ETS users pay attention only to use electricity during off-peak times. Off-peaks in winter start at 22pm and lasts until 6am, and in the afternoon beginning at 13pm, and ends at the 16pm. In this way, these users positively affect the daily load diagrams of the system.

The most frequent method of heating hot water for domestic usage in household in Slovenia are electric boilers (according to the survey, 77% of households). Most households use boilers with higher tariff system. Most of them are operational continuously, maximum power is reached in the evening, when consumption of hot water in households reaches the highest levels. In summer, higher consumption of power for heating of hot water is to be expected, for the water is not being heated by other means, and in winter higher electricity consumption is the result of lower initial temperature of water. During the peak of the total power system load, the boilers loads are within the limits of 18 MW (in the morning) to 137 MW in the evening (Figures 2, 3 and 4). By introducing a low tariff optimally insulated boilers, which, according to Recknagel, Sprenger, Schramek “Taschenbuch fur Heizung + Klimatechnik”, 2002, are the cheapest and most efficient hot water-supply system that could significantly reduce the peak load (of course this is only justified in the new installation or replacement of obsolete boilers). Stoves have the highest consumption in the afternoon, when most households prepare food. Their consumption stays about the same through all the seasons. Due to excessive restrictions of users, we cannot move its operating times from the time of load peaks to the off-peak times, except by using other energy source (natural gas), which depends on the global policy of ensuring the energy needs in Slovenia. According to the energy efficiency programs, which tend to reduce energy consumption through more efficient devices, the consumption of electricity for preparation of food can be visibly reduced: with modern dishes (with increased pressure system) 20% to 60% of power can be saved and at the same time the cooking time can be reduced for at least two-thirds; well-insulated hot plates


fitted with a thermostat use lower share of energy (up to 20%) for the preparation of food than ordinary heating panels; furthermore the preparation of food in a microwave saves up to 80% of energy (but the vast majority of households reject quick preparation of food in a microwave).

Figure 6.6: The daily chart for household consumption in the winter period in Slovenia

Figure 6.7: The daily chart for household consumption in spring/autumn period in Slovenia

Figure 6.8: The daily chart for household consumption in summer period in Slovenia

6.2.1 Average heat demand in residential sector

To calculate the energy consumption for residential buildings, information was acquired from the Slovenian Ministry and included kWh/y per municipality/census area (estimation), per fuel (estimation), surface of building, number of apartments/number of building per census area. This information was used to calculate the heat demand for all residential buildings in the region.


Buildings were mapped using the data on average consumption in kWh per m2 collected from Gradbeni Inštitut ZRMK d.o.o., which differentiates average energy consumption regarding the time period in which the building was constructed:

• buildings constructed before 1979: 200 kWh/m2*y,

• buildings constructed between 1971 and 1980: 280 kWh/m2*y,

• buildings constructed after 1980: 125 kWh/m2*y.

This information was used to map residential buildings for the region.


Following the same methodology as in the analysis of households, we have tried to get a rough estimate of energy consumption in each group. These groups of consumers include catering, trade, health, education, public lighting and others. The remaining general consumption is not divided further. The assumption was that the main consumers of energy for heating purposes are catering industry, education and health. Based on surveys and statistical yearbook the next graph can be drawn.

Figure 6.9: Consumption of various energy sources for heating purposes in wide usage

By investing in energy efficiency significant savings in energy use and reduce costs can be achieved in the wide use group. In particular, energy efficient buildings are commercially viable. Significant energy savings in space heating can be achieved with improved thermal insulation and more efficient sealing of windows. In addition, in larger buildings energy management systems are installed to improve energy efficiency and thereby reduce energy costs and maintenance. The system includes an intelligent control of heating and cooling, which regulates the desired temperature in individual rooms, in accordance with the selected mode of operation. Exclusion of heating or cooling of unused rooms is available, which allows additional savings particularly on weekends. For each area there is information available about the current set and actual temperature, room occupancy, etc..


6.3.1 Administrative buildings and schools and other educational buildings

The data on given buildings were acquired from Gradbeni Inštitut ZRMK d.o.o. The average energy consumption in Slovenia for selected building types is estimated:

- Administrative buildings: 205 kWh/m2*y

- Schools and other educational buildings: 165 kWh/m2*y

Figure 6.10: Specific Average, Target and Bad practice Energy Consumption for schools and public buildings, Slovenia (Gradbeni Inšitut ZRMK d.o.o)

6.3.2 Hospitals and medical buildings

For hospitals and other similar buildings the data were combined of:

- energy use by hospital bed: 20 MWh/bed*y (from: Recknagel, Sprenger, Schramek

“Taschenbuch fur Heizung + Klimatechnik” 2002 – page 1019);

- data for bed area: 59,7 m2/bed (from: Univerza na primorskem, Fakulteta za manegement,

diplomska naloga “Strategija energetske učinkovitosti bolnišnice”; Apotekar)

6.3.3 Churches and places of religious worship

For churches and other places of religious worship: 3,75 kW/m2*y (from: Recknagel, Sprenger,

Schramek “Taschenbuch fur Heizung + Klimatechnik” 2002 – page 1032).

6.3.4 Fire houses

For fire houses the data (consumption in kWh per m2 per year) was collected from four different Local Energy Concepts (LEK) and combined into an average value:

- LEK for municipality Kanal ob Soči: 100 kW/m2*y

- LEK for municipality Oplotnica: 39 kW/m2*y

- LEK for municipality Slovenske Konjice: 58 kW/m2*y

- LEK for municipality Brda: 11 kW/m2*y


6.3.5 Sport objects

For sport objects the data were collected and combined into an average value. Average emergent use for heating is 85.000 m3 of gas (propane) and average heating area is 4.000 m2.

- Energy use: 500 kW/m2*y


As for heat demand in the public sector, the survey have pointed out the following average values:

6.4.1 Shopping facilities

The data on energy consumption estimation for shopping facilities and other similar types of buildings were taken from a project made at the Faculty of mechanical engineering in Ljubljana (Energetski

preračuni – N. Lojevec).


6.4.2 Fair halls

The energy consumption estimation for fair halls and other similar types of buildings was calculated on using the data given by Gospodarsko Rastavišče in municipality Ljubljana which uses energy for heating 650 MWh and the heating area is 13.000 m2.



Energy consumption in industry represents 28,7% of total final energy consumption. Such high share is conditioned with the structure of Slovenian industry in which consumption of black and colored metallurgy, reaches 42% of energy consumption in industry.

Except for electricity, other energy sources are mainly used for heating purposes. This consumption includes the consumption of furnaces, dryers, heaters, boilers and similar appliances and devices for heat recovery in industry.

Industry field has been split into several branches. The data were obtained based on the data of the questionnaire, carried out in companies with annual electricity consumption of 5.000 MWh/y at least and based on some partial measurements. Consumption of companies that responded to our questionnaire, represents more than 40% of energy consumption in whole Slovenian industry. On questionnaire basis, we established an annual consumption of various energy sources in the company, the share of electricity consumption for heating purposes, installed electrical appliances for heating purposes and possible adaptation of business daily load diagram of the system, depending on the operating hours of heat consumption and economic viability.

The largest consumer of all energy sources for heat acquiring is the black metallurgy (21,8%), followed by non-metallic ores processing plants (17%) and production of base chemical products (8%) and the production and processing of paper (8%).

Black metallurgy has even a bigger share in consumption of electricity consumption for heating purposes (53,42%), followed by processing of metals (11,3%), processing of non-metallic ores


(10,8%) and production of electrical machinery and devices (7,47%). All other industries consume 17% of total electricity consumption for heating purposes.

Main characteristics of electricity consumption in the industry, obtained on the basis questionnaire include the following information:

• 36% of industrial firms adjusted the total consumption of electricity to load diagram of the system,

• 39% of companies have the ability to adapt the heat consumers of electricity to load diagram of the system,

• 23% of companies have already adjusted the heat consumers to the load diagram of the system.

The biggest consumers of electricity for heating purposes in industry are electric furnaces. These are designed to support different techniques and in most cases, customers can adjust their operation. This is reflected in the daily charts, where the power differs every hour, especially depending on tariff. Electricity market in Slovenia is separated into three seasons which are subject to different tariff lines for the sale of electricity. The higher, winter price of energy is about 30% higher than in the middle season. In summer season it is 30% lower than in the middle season. Even during the day there are three tariffs and customers are trying to reduce the energy consumption during the high tariff as much as the process and the device itself make it possible, all in the frame of general laws of economics.

6.6 SURVEY RESULTS


The overall heat demand in the residential sector in Slovenia has been assessed in 14.300 GWh/y (1.200 ktoe/y), equivalent to 7.150 kWh/inhabitant.

6.6.2 Public sector

For hospitals values that were used included 20 MWh/bed*y energy use by hospital bed, with heat

demand for bed areas using - 59,7 m2/bed area.

Values for Fire Houses that were used included LEK for municipality Kanal ob Soči – 100 kW/m2*y,

LEK municipality Oplotnica – 39 kW/m2*y, LEK municipality Slovenske Konjice – 58 kW/m2*y,

LEK municipality Brda – 11 kW/m2*y.

Heat demands for sporting objects were calculated using 500 kW/m2*y.

The heat demand of administrative buildings was estimated using the following values: administrative

buildings – 205 kW/m2*y; schools and other educational buildings – 165 kW/m2*y.


Heat demand was calculated for buildings in the tertiary sector using data for shopping facilities buildings and Fair halls.

Heat demand for shopping facility buildings were estimated using the following value energy use – 150 kW/m2*y .

The overall heat demand in the public and tertiary sectors has been assessed in 5.200 GWh/y (450 ktoe/y).



Heat demand was estimated (excluding not thermal uses and high temperature uses) per Municipalities, allocated on the industrial areas.

The overall heat demand in the industry sector, suitable for DHS, has been assessed in about 7 GWh/y (1 ktoe/y).


7 ASSESSMENT OF THE HEAT DEMAND IN WALLONIA

7.1 BACKGROUND

7.1.1 Cartography on buildings

The main sources of cartographical information on buildings in Wallonia are the PICC and PLI

databases (see Chapter 3.4).

PICC database was chosen as the main baseline material for the assessment of heat demand for

buildings. This one are showed on the Cartographical portal of Wallonia. A preview is available on

this geographical portal.

PICC is a topographical survey of all elements which appear on air photos of partial territory of

Wallonia. Topographical database, orthophotos, streets, hydrograph, etc. are accessible but download

is on restricted access. Buildings and others elements are located with accuracy on x, y and z

coordinates estimated to 25 cm.

PICC code Category PICC code Category

204 House 418 Police station

205 Industry 419 Farm

207 Water tower 422 Building under construction

214 Church, place of worship 423 Residential building

305 Filling station 424 Train station

410 Building 425 Bus station

411 Cultural 426 Museum

412 School 427 Department store

413 Civil service 440 Sport complex

414 Town hall 465 Cemetery

415 Hospital 490 Camping

416 Post office 650 Airport and airfield

417 Fire station 418 Police station

Table 7.1: PICC category codes for each building type

Every building is represented as a polygon. In the table of attribute, a category code is given to each

polygon according to the list here above (Table 7.1).


PICC code Category

207 Water tower

465 Cemetery

490 Camping

Table 7.2: PICC codes and categories not relevant

Some of them were not selected (Table 7.2) because of the heat consumption they generate are

deemed not relevant for the purpose of BioEnerGIS.

The distribution of each category by heat demand sector will be defined in following sections.

PICC technical information is divided into 45 blocs, which are divided into several files type. For

buildings, the files of the greatest importance are the “POL” files. Each bloc can be divided in several

POL files. Each ones have been exported into Excel format in order to join to each building heat

consumption depending on its heat demand sector.

PICC data are based on aerial photography so surfaces on map integrate also surface under gutter. The

different floor of the building cannot be distinguished.

Nevertheless the PICC does not cover the entire regional territory (Figure 7.1).

Figure 7.1: Progress of coverage of PICC database (Source: http://cartopro2.wallonie.be)

Even if the PICC is going to cover the entire regional territory, in 2010 the data in some areas have

still not been validated. The areas where the data are still not definitively validated are highlighted in

blue, yellow and green.

The alternative option where there is no PICC coverage, was to use PLI. PICC and PLI for the area out

of PICC coverage have been added up as to provide a map covering the entire regional territory

(Figure 7.2).


Figure 7.2: PLI and PICC coordination

The disadvantage of PLI is that there is no distinction between different building categories. As a

result, for the areas where PLI was used, a coefficient has been used without distinction between the

sectors. This coefficient is a mean between coefficients used for PICC for the four sectors.

For civil users, the same coefficients have been used than for PICC: mean of every LEPUR

coefficients.

For public buildings and services and commercial, mean of every PICC’s coefficients have been used.

For industry, the same coefficient has been used than for PICC: ICEDD - Institut de Conseil et

d'Etudes en Développement Durable / Institute of research and consulting in sustainable development

coefficient, as explained further.

An example follows (Table 7.3), for a municipality:

Sector PICC PLI’s mean: components

PLI

coefficient

Civil users LEPUR Mean of LEPUR

Mean of every PLI’s mean components

Public buildings Depending on

category of public buildings

Mean of PICC

Services and commercial

Depending on category of services and commercial buildings

Mean of PICC

Industry ICEDD ICEDD

Civil users LEPUR Mean of LEPUR

Table 7.3: PLI's coefficient

PICC

PLI

Final

Database

No PICC

coverage

Building NOT

categorized


The normal equivalent heating degree days 16,5 °C / 16,5 °C was chosen as heating degree days in

order to normalize consumption (to be able to compare results of different geographical regions). This

choice was driven by for several reasons:

• the limit 16,5 °C as inside temperature comfort is generally recognized as a reference by

different institution;

• normal heating degree days are used to be able to compare between years. In fact these ones

are based on a mean on 30 years, so they are not be influenced by climate;

• equivalent degree days are used to consider building inertia;

Global data are only available for the municipality of Uccle. Data are also available on Eurostat but

they are less accurate. They are available on web for consultation and downloading.

7.1.2 Energy carriers

There is no digitalized map of the Wallonia natural gas distribution network. In Wallonia the

distribution of natural gas is ensured by 9 inter-municipality companies, each company covering a

certain area of the region (Figure 7.3). The website of all gas distribution companies has been

consulted as to determine which municipalities are served by natural gas. Although municipality area

are usually not entirely served by natural gas (except for urban municipalities), the municipalities

partly served by gas were considered as entirely connected to gas. As a result, natural gas was

considered as the only energy vector in those municipalities, whereas oil is considered as the only

energy vector in the municipality not served by natural gas.

For municipalities entirely served by gas a performance coefficient of 0,92 was used, while a

coefficient of 0,90 was used for oil boiler efficiency.

Figure 7.3: Natural gas distribution network



The assessment and mapping of heat demand from households are based on PICC database and based on data extracted of PLI for area not covered by PICC database, as outlined in the Chapter 7.1. For this heat demand sector, three PICC building categories have been used (Table 7.4):

PICC code Category Heat demand coefficient

(kWh/m², PICC)

Heat demand coefficient

(kWh/m², PLI)

204 House LEPUR LEPUR

423 Residential

building LEPUR LEPUR

419 Farm LEPUR LEPUR

Table 7.4: PICC codes and categories selected for civil users

The PICC and PLI database provide the shape of every building along with its area.

It is almost impossible to found precise heat demand coefficient expressed with building surface. ICEDD has calculated these coefficients for a house or an apartment but not according to surface; on the other hand ICEDD has estimated a mean surface by house and by apartment in Wallonia, so ratios could be expressed in kWh/m². However this ratio is very inaccurate.

We got several estimations from an expert and some heat consumption data from a document of the Confederation Construction of Wallonia. Theses ones are also inaccurate because it is only a mean based on experience of these actors: that is not a scientific study about influence of building construction date, number of facades, insulation, etc.

A study (a two year project) is at present realized in Liege about these influences, and, even if it’s not yet published, the project leader of this study has agreed to share the methodology and the database.

CRAW has obtained heat demand coefficient specific by municipality.

LEPUR has used normal equivalent heating degree days 15 °C /15 °C and it has extrapolated them for all Wallonia, with a precision of 500 m x 500 m. Unfortunately these data aren’t available within the Project, so normal equivalent heating degree days 16,5 °C / 16,5 °C have been used in this survey.

The LEPUR coefficient used take into account isolation, age of building, common ownership degree, climate, etc., so these are probably the most precise and scientific heat demand coefficient available until now for Wallonia.

The Table 7.5 shows the structure of the table of attribute of the dataset for civil users.

For column title normalized heat demand, data are the same that in the column heat demand, because

LEPUR coefficients integrate already the normal equivalent heating degree days, as explained before.


Table 7.5: Structure of database for heat demand from civil users


The assessment and mapping of heat demand from public buildings are based on PICC database and on data extracted of PLI for area not covered by PICC database, as outlined in the introduction.

Based on PICC, three PICC referential categories have been selected follows (Table 7.6):

PICC

code Category


(kWh/m², PICC)


(kWh/m², PLI)

413 Civil service 158

158

414 Town hall 158

415 Hospital 189

416 Post office 158

417 Fire station 158

418 Police station 158

411 Cultural 155

412 School 129

Table 7.6: PICC codes and categories selected for public properties sector

Based on PICC public buildings could be located on map. For each ones, the ICEDD mean could be applied for each typical building (based on their area, their floors, etc.). So an estimation of energy consumption by typical buildings and, for some data, by fuel type could be established.

For national service buildings, town halls, post offices, fire and police stations, the coefficient public

department of 189 kWh/m² was used.

For hospital and cultural buildings, a coefficient of 155 kWh/m² from ICEDD report, was used.

For school, there is no precision in PICC about category (private, public, community, etc.), whereas ICEDD report provides a different coefficient for several types of schools (community school, municipality school and private school): the coefficient chosen is 129 kWh/m² and corresponds to the mean of the three ICEDD coefficients (respectively 127, 158 and 103 kWh/m²).


ones.

Some buildings (like retirement home, etc.) specifically identified in the PICC database have no specific code in PICC. When these buildings have been found a specific coefficient has been applied, like follow (Table 7.7):

Table 7.7: Heat demand coefficient for non categorized buildings of PICC

The Table 7.8 shows the structure of the table of attribute of the dataset for public buildings.

Table 7.8: Database structure of heat demand of public buildings


The assessment and mapping of heat demand from service and commercial activity buildings are based on PICC database and on data extracted of PLI for area not covered by PICC database.

Based on the PICC, the services and commercial activities buildings could be located on map. The ICEDD coefficient could be used for each building category (e.g. based on their area, number of

PICC

code Category


(kWh/m², PICC)


(kWh/m², PLI)

410 Building 111

151

427 Department store Depending on area

440 Sport complex 158

424 Train station 158

425 Bus station 158

426 Museum 155

214 Church, place of worship 155

305 Filling station 111

650 Airport and airfield 93


floors). Thus, an estimation of specific energy consumption for each building categories and, for some data, by fuel type could be established.

Table 7.9: PICC codes and categories selected for tertiary sector

For building, the coefficient “private department” has been selected.

For department store, coefficient depends on the area of the building (Table 7.10):

Area Heat demand coefficient

(kWh/m², PICC)


(kWh/m², PLI)

< 400 m² 576

258 400 to 2.500 m² 119

> 2500 m² 79

Table 7.10: Coefficients of heat demand for department store

Coefficients of 281 kWh/m² and 143 kWh/m² has been used for supermarkets and hypermarkets, respectively.

For train and bus station, buildings are labelled as public department: a coefficient of 158 kWh/m² has been used.

Museums and churches are labelled as cultural: a coefficient as 155 kWh/m² has been used.

It has been considered that filling station has similar heat demand than private department,

111 kWh/m², and airport and airfield, as a store of every area, so 93 kWh/m².

The Table 7.11 shows the structure of the table of attribute of the dataset for public buildings.

PICC

code Category


(kWh/m², PICC)


(kWh/m², PLI)

410 Building 111

151

427 Department store Depending on area

440 Sport complex 158

424 Train station 158

425 Bus station 158

426 Museum 155

214 Church, place of worship 155

305 Filling station 111

650 Airport and airfield 93


Table 7.11: Database structure of heat demand from services and commercial activity buildings


The assessment and mapping of heat demand from industry has been divided into two components:

− the heat demand from industrial buildings (energy demand for the only purpose of heating the buildings);

− the heat demand from the industrial processes. The assessment methodology and the baseline information were different for these two components. Two separate datasets have been produced: the dataset on heat demand from industrial buildings is a polygon shapefile, while the dataset on heat demand from industrial processes is a point shapefile.

7.5.1 Heat demand from industrial buildings

The heat demand from industrial buildings was assessed by using the polygons holding the reference #205 “Industry” of PICC and using PLI where there is no PICC coverage.

Heat demand from industrial buildings has been given by applying a coefficient to industrial building surface. The coefficient used was taken from ICEDD report on the 2007 energy balance for Wallonia. Based on this report, a coefficient of 93 kWh/m² has been chosen, for PICC and PLI. It corresponds to the heat demand for department store. Final heat demand is the result of the multiplication of the surface of each polygon by the ICEDD ratio.

The Table 7.12 shows the structure of the table of attribute of the dataset for industrial buildings.


Table 7.12: Database structure of heat demand from industrial buildings

7.5.2 Heat demand from industrial processes

As previously mentioned, this subsector is a point shapefile, in which each point represent an industry. Its structure is different from others sectors. Selection of relevant companies has been made based on NACE codes. Sectors have been chosen on the basis of ICEDD report on energy balance of industry in Wallonia (2007). Main heat demand of industry sectors is described in this report. NACE codes selection is described follows (Table 7.13).


NACE Activity Number of

companies

Reference

companies

Referential

percentage

0220 Logging 36 1 0,26

105 Manufacture of dairy products 40 3 0,62

1081 Manufacture of sugar 8 4 0,62

1105 Manufacture of beer 17 1 0,62

132 Weaving of textiles 8 1 0,16

139 Manufacture of other textiles 72 1 0,16

171 Manufacture of pulp, paper and paperboard 5 1 0,26

172 Manufacture of articles of paper and paperboard 91 2 0,26

201

Manufacture of basic chemicals, fertilisers and

nitrogen compounds, plastics and synthetic rubber in

primary forms

48 3

0,52

202 Manufacture of pesticides and other agrochemical

products 5 1

0,52

205 Manufacture of other chemical products 66 1 0,52

212 Manufacture of pharmaceutical preparations 16 1 0,52

231 Manufacture of glass and glass products 39 10 0,36/0,4

233 Manufacture of clay building materials 5 2 0,73

234 Manufacture of other porcelain and ceramic products 6 1 0,73

235 Manufacture of cement, lime and plaster 18 6 0,45

236 Manufacture of articles of concrete, cement and

plaster 109

2 0,26

241 Manufacture of basic iron and steel and of ferro-alloys 18 8 0,36

243 Manufacture of other products of first processing of

steel 24

1 0,36

244 Manufacture of basic precious and other non-ferrous

metals 34

2 0,38

245 Casting of metals 24 4 0,28

TOTAL 689 56

Table 7.13: NACE codes selection based on ICEDD report

Data on all companies referred as belonging to one of these sectors have been extracted from the SPW Enterprises Database. A total of 689 companies have been selected based on these NACE codes. The information extracted are:

− the complete address;

− the NACE code;

− the annual turnover (when available);


− the company capital (when available);

− the number of employees.

The data from the Wallonia Energy Atlas have been used to attribute a heat demand to these companies. These data are structured as follow (Table 7.14).

Name Municipality Sector Margin of consumption (toe/year)

XYZ Bertrix Other food 2.500 to 5.000

ZYX Angleur Paper Over 100.000

Table 7.14: Energy Atlas of Wallonia

As shown in Table 7.14, the heat demand is estimation in margin form. Every margin types appear in the Table 7.15 follows.

Margin of consumption (toe/year) Consumption (toe/year)

< 2.500 2.500

2.500 – 5.000 3.750

5.000 – 10.000 7.500

10.000 – 25.000 17.500

25.000 – 50.000 37.500

50.000 – 100.000 75.000

> 100.000 100.000

Table 7.15: Margin of heat demand

Furthermore, heat demand is global, not divided by energy vector.

Another source has been chosen: sectoral agreements have been contracted between Wallonian authorities and main sectors of industry in Wallonia. In these contracts, sectors have to commit themselves to reduce GHG emissions by several measures (audits, specific materials, etc.). Wallonia authorities grant financial and administrative advantages in return. Person in charge of these sectors have been contacted to share their information about their heat demand. Some of them have sent their heat demand but without precision so it was impossible to use these data. Only one sector, chemistry industry, has given relevant data.

These two sources are the ones for the heat demand from industrial process sector.

For companies common to the SPW Enterprises Database and the Wallonia Energy Atlas, heat consumption has been estimated by the mean of two limits of margin available for this company on the Energy Atlas of Wallonia. For example, for the XYZ company, its consumption is 3.750 toe/y and 100.000 toe/y for the ZYX company. These consumptions have been converted into kWh/year (Table 7.16).


Name Municipality Sector Margin of consumption

(toe/year)

Consumption

(toe/year)

Consumption

(kWh/year)

XYZ Bertrix Other food 2.500 to 5.000 3.750 43.604.651

ZYX Angleur Paper Over 100.000 10.0000 1.162.790.698

Table 7.16: Consumption (kWh/year) for companies common between Energy Atlas of Wallonia and CD

Enterprises

34 companies of Energy Atlas were not in the CD Enterprises listing. They have not been classified with other companies but their margins of consumption have been estimated in the same way than below. So 34 companies have to be added to 689 enterprises selected on the basis of ICEDD report. Total of companies is following (Table 7.17):

Source Number of companies selected

ICEDD report 689

Energy atlas of Wallonia 34

TOTAL 723

Table 7.17: Total number of companies selected

Number of common companies between ICEDD report and Energy Atlas is above (Table 13): these ones are the referential companies to estimate consumption of other companies as explained below.

For each company of NACE code selected, turn over and work force have been extracted, company by company, from CD Enterprises, also for referential companies. For each referential consumption of NACE code, means of referential consumption have been calculated, with means of turnover and work force corresponding. So, for each NACE code, there are at least a referential company, so a referential consumption, and a turnover and a work force of reference. For other companies of this NACE code, an extrapolation has been done on consumption, in proportion of turnover and work force of referential companies.

To locate all companies, addresses have been converted into X,Y; for companies of chemistry sector included into the sectoral agreement, address corresponds to the centre of municipality to ensure confidentiality. Furthermore, these companies have been consolidated to be less identifiable.

To convert heat demand in normalized heat demand, normal equivalent heating degree days 16,5/16,5 have been used as explained previously.

Primary energy has been obtained on the basis of a yield of 92% for gas boiler and 90% for fuel boiler. Furthermore, based on reports of sectoral agreements, heat demand has been divided in accordance with the results describe in this report. The percentage of each sector is above (Table 7.13).

The two main energy vectors are electricity and gas. Only gas has been selected, so the referential percentage corresponds to the gas. Nevertheless for NACE code 231 (manufacture of glass and glass products) heavy fuels and gas are the largest energy vectors used. 36 % of consumption is constituted by gas, and 40 % by heavy fuels: these two vectors have been selected.

The Table 7.18 shows the structure of the table of attribute of the dataset for industrial processes.


Table 7.18: Database structure of heat demand from industrial processes

7.6 SURVEY RESULTS


The assessment and mapping of heat demand from households are based on PICC and PLI databases and on the heat demand coefficient specific by municipality.

The overall heat demand in the residential sector has been assessed in 66.414 GWh/y (5.710 ktoe/y), equivalent to 19.500 kWh/inhabitant.

7.6.2 Public sector

Also the assessment of heat demand from public sector are based on PICC and PLI buildings area.

For national service buildings, town halls, post offices, fire and police stations, the coefficient public

department of 189 kWh/m² was used. For hospital and cultural buildings, a coefficient of 155 kWh/m² from ICEDD report, was used. For school, there is no precision in PICC about school category (private, public, community, etc.) whereas the ICEDD report provides a different coefficient for several types of schools (community school, municipality school and private school).


The assessment and mapping of heat demand from service and commercial activity buildings are based on PICC database and on data extracted of PLI for area not covered by PICC database.

The overall heat demand in the public and tertiary sectors has been assessed in 4.091 GWh/y (352 ktoe/y).


As described in chapter 7.5, the assessment and mapping of heat demand from industry has been divided up into two components:

− The heat demand from industrial buildings (energy demand for the only purpose of heating the buildings)

− The heat demand from the industrial processes. The overall heat demand in the industry sector, suitable for DHS, in Wallonia has been assessed in about 2.250 GWh/y (193 ktoe/y).


SOURCES AND LITERATURE

Sources and literature – Lombardy

SIRENA Regional Informative System for Environment and Energy of Lombardy Region, 2010 – Lombardy Region, CESTEC – http://sirena.cestec.eu/sirena/index.jsp.

CESI Ricerca SSG Sistemi di Generazione – Omar Perego, Marco Marciandi – Studi di fattibilità di applicazioni cogenerative, stato e prospettive della micro-cogenerazione e stima del potenziale del teleriscaldamento – Feasibility studies for cogeneration applications, status and prospects of micro-cogeneration and estimation of the potential of district heating – Report number 08005779 – 2009.

CESI Ricerca ING Ingegneria, GAME SVALTER – Tranquillo Magnelli, Eros Tassi – Report number A4517488, A5022994 – 2005.

Ecoheatcool Project (IEE project 2005 -2006) – Coordinator: Euroheat&Power Recommendation and project reports.

Fondazione Cariplo “Audit energetico degli edifici dei Comuni piccoli e medi 2006-2008 – Energy Audit in public buildings of small and medium Municipalities, 2006 – 2008”.

AICARR (Italian Association of air conditioning, heating and cooling) “Energia negli ospedali: problematiche e soluzioni di ottimizzazione delle risorse - Energy in hospitals: problems and solutions, resource optimization”, 2009.

CRESME (Center for Economic Research, Social, and Market for the Construction Sector and the Territory) “Il mercato delle costruzioni e le prospettive degli impianti termici e di condizionamento – The construction market and the outlook for heating and air conditioning”, 2010.

“Biomass Use in Brianza” Project No TREN/04/FP6EN/S07.30974/503177, Coordinator: Comitato Termoelettrico Italiano.

Sources and Literature – Northern Ireland

Energy Efficiency in Buildings – CIBSE, Chartered Institute of Building Services Engineers, January 2004.

Jones P G., Turner R N., Browne D W J. and Illingworth P J. Jan. 2004. Energy Benchmarks for Public Sector Buildings in Northern Ireland.

Jones P G., Bond M. and Grigg P F. Energy Benchmarks for Public Sector Buildings – CIBSE National Conference 1999.

Introduction to Energy Efficiency Series (DETR) 1994.


Liddiard R, Wright A and Marjanovic-Halburd L. A review of Non-Domestic Energy Benchmarks and Benchmarking Methodologies.

Nijjar J S., Fung A S., Hughes L & Taherian H. District Heating System Design for Rural Nova Scotian Communities using Building Simulation and Energy Usage Database. 2009.

Sources and Literature – Slovenia

Statistical office of the republic of Slovenia – www.stat.si

Project “Slovenija znižuje CO2” – www.slovenija-co2.si

Statistical office of the Republic of Slovenia, 2008; Institut Jožef Stefan.

Source: Statistical Office of Slovenia, Census of Population, Households and Housing 2002

Surveying and Mapping Authority of Slovenia, Real Estate Register.

Institut Jožef Stefan – Efficient energy center (for residential buildings in order of year of construction) - Image 1.

GI-ZRMK; Number of heating energy for schools and administrative buildings – image 2.

Recknagel, Sprenger, Schramek / Taschenbuch fur Heizung + Klimatechnik 2002;

Univerza na primorskem, Fakulteta za menegement, diplomska naloga / Strategija energetske učinkovitosti bolnišnice; Albin Apotekar

Gradbeni Inšitut ZRMK d.o.o. – www.gi-zrmk.si

http://www.gi-zrmk.si/oddelki/bivokolje/bench/default.htm.

Sources and Literature – Wallonia

SPW – Service Public de Wallonie, Direction générale opérationnelle « Aménagement du territoire, Logement, Patrimoine et Energie ». PICC (Projet Informatique de Cartographie Continue). [Informatique ressource]. SPW, 2010. 3 CD-ROM.

SPW – Service Public de Wallonie, Direction générale opérationnelle « Aménagement du territoire, Logement, Patrimoine et Energie ». PLI V07 (Plan de Localisation Informatique). [Informatique ressource]. SPW, 2008. 4 CD-ROM.

SPW – Service Public de Wallonie, Direction générale opérationnelle « Économie, Emploi et Recherche ». Banques de données Entreprises. [Informatique ressource]. SPW, 2010. 1 CD-ROM.


SPW – Service Public de Wallonie, Direction générale opérationnelle « Aménagement du territoire, Logement, Patrimoine et Energie ». PICC, état d’avancement. [On line]. SPW. [Consulted : 10 July 2010]. http://cartopro2.wallonie.be/avancements/

Fédération du gaz naturel. Les Degrés-jours. [On line]. Fédération du gaz naturel. [Consulted : 15 July 2010]. http://www.gaznaturel.be/consommateurs/le-gaz-naturel/nouvelles-et-publications/degres-jours

SPW – Service Public de Wallonie. Schéma de développement de l’espace régional. [On line]. SPW. [Consulted : 10 November 2010]. http://sder.wallonie.be/ICEDD/CAP-atlasWallonie2006/pages/atlas.asp?txt=fig&type=map

ICEDD – Institut de Conseil et d’Etudes en Développement Durable. Bilan Energétique de la Région wallonne. [On line]. ICEDD. [Consulted : 08 July 2010]. http://energie.wallonie.be/fr/2007.html?IDC=6578

TELLER , Jacques, DUJARDIN, Sébastien, LARBEEUW, France-Laure et al. Structuration du territoire pour répondre aux objectifs de réduction des émissions de gaz à effet de serre – Note de travail. CPDT – LEPUR – ULG - Conférence Permanente du Développement Territorial. Octobre 2010. 166 p.

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