OPTIMUS Final Report - santcugat.cat Final Report... · 2 PROJECT PERIODIC REPORT Grant Agreement...
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FP7/608703 OPTIMUS Final Report Part A: “ Final Publishable Summary Report”
Transcript of OPTIMUS Final Report - santcugat.cat Final Report... · 2 PROJECT PERIODIC REPORT Grant Agreement...
FP7/608703
OPTIMUS Final Report Part A: “ Final Publishable Summary Report”
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PROJECT PERIODIC REPORT
Grant Agreement number: 608703
Project acronym: OPTIMUS
Project title: OPTIMising the energy USe in cities with smart decision support system
Funding Scheme: FP7- ICT-2013.6.4
Period covered: from 1st October 2013 to 30th September 2016
Name, title and organisation of the scientific representative of the project's coordinator:
Prof. John Psarras, Project Coordinator, National Technical University of Athens
Tel: +30 210 7723551
Fax: +30 210 772 3550
E-mail: [email protected]
Project website address: http://www.optimus-smartcity.eu
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Contents
1 Executive Summary ....................................................................................................... 4
2 Context and Objectives .................................................................................................. 5
2.1 Context .................................................................................................................... 5
2.2 Objectives ............................................................................................................... 7
3 Main S&T Results / Foregrounds .................................................................................... 9
3.1 OPTIMUS SCEAF ................................................................................................... 9
3.2 OPTIMUS TRACKER .............................................................................................12
3.3 OPTIMUS DSS ......................................................................................................18
3.3.1 Data Capturing Modules ..................................................................................18
3.3.2 Thermal Comfort Validator ..............................................................................19
3.3.3 Semantic Framework ......................................................................................19
3.3.4 Prediction Models ............................................................................................21
3.3.5 Set of Inference Rules .....................................................................................22
3.3.6 DSS Engine ....................................................................................................26
3.3.7 OPTIMUS DSS Interfaces & Implementation Process .....................................26
3.3.8 Insights from the OPTIMUS DSS Pilot Implementation ...................................31
4 Impact ...........................................................................................................................34
5 Exploitation Strategy .....................................................................................................37
5.1 Expansion of the OPTIMUS Package .....................................................................37
5.2 Integrated Planning at the City Level ......................................................................37
5.3 Visualization of the City Level Approach in the DSS ...............................................38
5.4 Exploitation and Business Plan ..............................................................................39
6 Main Dissemination Activities ........................................................................................41
6.1 Achievements .........................................................................................................41
6.2 OPTIMUS Events ...................................................................................................42
6.3 Training Material ....................................................................................................44
6.4 OPTIMUS Market Brochure ....................................................................................46
7 Consortium ....................................................................................................................47
8 OPTIMUS Website & Social Media................................................................................48
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1 Executive Summary
Making Smart Energy Cities (SEC) a reality requires an intelligent and integrated assessment and
consideration of various data sets, as well as the development of energy systems which help to
understand the interconnections between them. OPTIMUS provides the following set of web-based
consulting tools for energy managers and energy consultancies, in order to make cities more
energy efficient and sustainable:
OPTIMUS SCEAF (Smart City Energy Assessment Framework): It provides a framework for
assessing the current performance of the city / building, by analysing three main pillars: “Political
Field of Action”, “Energy and Environmental Profile” and “Related Infrastructures and ICT”.
OPTIMUS TRACKER: It constitutes a web tool for the energy managers, in order to assess the
potential of the city / building for energy optimization and identify specific buildings where the
OPTIMUS DSS can be applied.
OPTIMUS DSS (Decision Support System): It is a web-based system which uses
multidisciplinary data from five different domains (weather conditions, buildings’ energy profiles,
occupants’ feedback, energy prices and energy production) to make predictions of the building
energy performance and help energy managers to adopt measures (namely short-term Action
Plans) to improve it.
OPTIMUS has been designed with the necessary degree of generalization, so as to be adapted by
both public and private sector organizations, with different characteristics, energy infrastructures,
needs, priorities and types of energy demand:
Public sector: The tools can support Signatories to the Covenant of Mayors (CoM), which want
to monitor and optimise energy use in their buildings so that they can effectively implement
Sustainable Energy Action Plans (SEAPs). OPTIMUS has been successfully applied in three
cities (Sant Cugat, Savona and Zaanstad), to help improving municipal buildings.
Private sector: The tools are generic enough as to be applied to other privately owned buildings
(e.g. private organizations with different types of buildings, who want to improve their energy
efficiency and therefore, their energy spending).
The key benefits of the OPTIMUS package are summarised below:
Monitoring and evaluating the performance of the city / building, in terms of energy efficiency.
Support of short term decision-making on energy planning, so as to reduce energy consumption,
CO2 emissions and energy cost.
Offering of an advanced and intelligent turn-key solution addressed to any municipality that has
as purpose to implement SEAPs.
Implementation of state-of-the-art ICT technologies and analytics for energy optimization.
The DSS implementation can achieve significant reduction of the energy consumption, CO2
emissions and energy cost (in some cases even beyond 20%), as well as approximately 10%
increase of renewable energy production. The DSS can increase the performance in the “OPTIMUS
Rating Chart” (through the OPTIMUS SCEAF) up to 2 classes from the 1st year of its implementation.
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2 Context and Objectives
2.1 Context
Rational
SEC, as a core pillar of the Smart Cities, constitute an emerging urban development strategy and
are expected to play a key role in the implementation of Europe 2020. Among the primary targets of
SEC is also the achievement of the 2030 climate and energy objectives, towards carbon neutral
cities and neighbourhoods. In the process of building the future Cities, Information and
Communication Technology (ICT) infrastructure are the key enabler.
Making SEC a reality requires an intelligent and integrated assessment and consideration of various
data sets, as well as the development of energy systems which help to understand the
interconnections between them. Monitoring and optimization of available energy data sources is
therefore a priority. This is particularly true for the building sector, which is responsible for 40% of
the EU’s energy consumption and 36% of its CO2 emissions. However, it is of common
understanding that achieving energy savings in buildings is a difficult and complex process.
In this respect, modelling and simulating energy systems can help to better understand how cities /
buildings work and how the various different domains interact among them, such as energy demand,
renewable energy systems and innovative generation technologies for local energy production,
energy and data infrastructures, etc. Models and datasets, however, typically cover one particular
field only and it is difficult to connect them across these boundaries.
Scope
OPTIMUS constitutes a subset of
SEC, offering a set of web-based
consulting tools for energy managers
and energy consultancies, in order to
make cities more energy efficient and
sustainable (Figure 1). The purpose is
to optimise the energy use in city’s
buildings (municipal and educational
buildings, buildings for entertainment
and sports facilities, hotels, etc.), taking
into consideration their interaction with
energy systems, such as renewable
energy production, smart district
heating and cooling grids through CHP
(Combined Heat and Power) and other
energy sources.
Through the successful combination of advanced ICT tools (OPTIMUS SCEAF, OPTIMUS
TRACKER, OPTIMUS DSS) and heterogeneous sources (meters, sensors, derived real-time data,
weather data and other external sources, etc.), OPTIMUS provides an integrated solution for energy
managers and energy consultancies, addressing specific questions as depicted in Figure 2.
Figure 1: OPTIMUS as a subset of the Smart Energy City
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The DSS is the core element of OPTIMUS and the most technologically advanced solution. It sits
on top of existing energy management systems, integrating five multidisciplinary data sources
(weather conditions, buildings’ energy profiles, occupants’ feedback, energy prices and energy
production), in order to propose short-term Action Plans for energy managers with the goal of
reducing energy consumption and cost.
Innovation
OPTIMUS provided new and innovative pathways in addressing climate and energy challenges by
incorporating novel methodological approaches and technologies that made OPTIMUS a pioneer in
three main lines (Figure 3):
1. Multidisciplinary Data Sources: The data capturing modules were designed and developed
to integrate data from five different domains: “Weather Forecasting”, “De-centralised Sensor
Based”, “Occupants’ Feedback”, “Energy Prices” and “Energy Production. In addition, the
OPTIMUS Thermal Comfort Validator (TCV) web application was developed to assess the
thermal comfort levels of the building's occupants.
2. Semantic Modelling of Data: For integrating the data from different domains, it was necessary
to implement a holistic interoperability solution using Semantic Web technologies. A data
integration process has been established, in order to fulfil the requirements and particularities of
the DSS architecture.
3. Energy Optimisation: The semantically modelled data is issued by the prediction models
(based on multiple linear regression and “grey box” models) and inference rules to derive the
short-term actions to improve the building’s energy performance.
Validation
The effectiveness of the proposed solutions has been verified through a substantial validation
phase in the following pilot sites: (a) Sant Cugat Town Hall and Theatre, in Spain; (b) Savona
Campus and Colombo-Pertini School, in Italy; (c) Zaanstad Town Hall, in The Netherlands.
Figure 2: Set of web-based consulting tools
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Figure 3: OPTIMUS DSS
Dissemination/Exploitation
The official launch of the OPTIMUS DSS took place within the framework of the 8th European
Conference on Sustainable Cities and Towns, which attracted 1,200 participants from all over
Europe. Exploitation of the OPTIMUS outputs have been supported by a number of communication
measures, including among others a complete revamp of the project website, with materials
designed to support the uptake of the tools (e.g. training material, videos, testimonials, journal
articles, factsheets, etc.). OPTIMUS market brochure as additional exploitation tool was developed
to support and sustain the uptake after the project lifetime. Moreover, the Consortium focused on
the creation of synergies with interested stakeholders towards OPTIMUS sustainability after the
project end. They are also committed to present, promote and exploit the developed OPTIMUS tools
beyond the lifetime of the project.
2.2 Objectives
The key Objectives (O) of the OPTIMUS project are the following (their interrelation is presented in
Figure 4):
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O.1. Development of the OPTIMUS approach, linking Smart Cities with energy optimization.
O.2. Development of a tool (Smart City Energy Assessment Framework), which will be used so as
to conduct a thorough analysis and assessment (applicable to different cities), of the ex-ante
and ex-post status of municipal buildings.
O.3. Collection, through a systematic way, of the requirements for the technological solutions, by
actively involving all end-users and stakeholders in this process.
O.4. Collecting, integrating and semantically modelling of data from different domains, types and
sources to understand the influential factors in the energy consumption of buildings.
O.5. Development of the OPTIMUS DSS. This implies the development of an inference engine
which embeds the necessary knowledge to propose energy optimization measures. The
OPTIMUS DSS is a decision support tool for energy optimization which operates in a rapid and
sustainable way.
O.6. Validation of the OPTIMUS approach, through real-life pilot cases in three municipalities and
by providing evidence of energy savings, total cost of operation, scalability of the solutions,
user's acceptance and benefits that accrue. OPTIMUS aims to achieve quantifiable and
significant reduction of energy consumption and CO2 emissions, through the application of the
proposed DSS.
O.7. Extraction of lessons which could be useful for later deployments of the system at other
municipalities.
O.8. Wide dissemination of the project outcomes and coordinated exploitation of results to optimise
impact, to share and promote project results through targeted dissemination activities using
appropriate media and tools and to outline an exploitation plan and the respective Service
Business Model.
Figure 4: Key Objectives (O) interrelation
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3 Main S&T Results / Foregrounds
OPTIMUS has evolved from the conceptual phase into a fully operative set of web-based tools
through an open and interactive approach, which is depicted in Figure 5. In general, the activities
have proceeded according to the work plan and as per DoW and have produced significant results
which are summarised in the coming paragraphs.
Figure 5: Multiscale evaluation
3.1 OPTIMUS SCEAF
OPTIMUS SCEAF (Smart City Energy Assessment Framework) Tool (http://sceaf.optimus-
smartcity.eu, http://optimus-smartcity.eu/solutions-sceaf) provides energy managers with a
framework for assessing the performance of the city / building, in terms of energy optimization, CO2
emissions reduction and energy cost minimisation. The main aim of the SCEAF is to direct “Smart
Cities” to energy optimization by highlighting the strengths, the vulnerabilities and the opportunities
arising given the existing energy strategy, environmental policy, municipal facilities and related
infrastructures of each city.
The added value of SCEAF is that it is an assessment tool that indicates underperforming sectors,
providing to the end-users an overview of the city / building performance per sector, in order to be
able to lead targeted energy Action Plans. Through the SCEAF, the ex-ante and ex-post status of a
Smart City, in relation to energy optimization issues can be assessed, in a coherent, transparent and
integrated way, compared with the “OPTIMUS” city, which is the city that achieves the best
performance in all proposed indicators.
Structure of the Framework
The framework consists of indicators that are structured on three major assessment axes:
Political Field of Action.
Energy & Environmental Profile.
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Related Infrastructures-Energy & ICT.
This is done given the importance of acquiring a complete view of the city’s behaviour beyond pure
energy performance measures, considering also its motivation in becoming “Smarter” with emphasis
on energy efficiency. Each axis is further subdivided into specific pillars, and each pillar is described
by one or more indicators (Figure 6). The indicators are either numerical measured by specific units
of measurement or qualitative, accompanied by a specific linguistic scale of assessment.
Based on the City Level SCEAF, a customised Municipal Building Level SCEAF was developed.
The set of indicators in the Municipal Building Level SCEAF were further customised and oriented
towards building characteristics, without deviating, however, from their original philosophy of
framework.
Further effort was dedicated in making the indicators as independent as possible from environmental
and operational conditions. For this purpose, values within calculations were normalised according
to Heating and Cooling Degree Days (HDD, CDD), as well as hours of operation of the municipal
buildings.
Figure 6: Structure of the framework (axes and pillars)
OPTIMUS Rating Chart
The SCEAF enables the main outcomes to be presented in the “OPTIMUS Rating Chart” that
supports the classification according to the rating resulted from the analysis, based on the following
linguistic term set:
S = {s0 = Insignificant (I), S1 = Very Low (VL), S2 = Low (L), S3 = Medium (M), S4 = High (H), S5 =
Very High (VH), S6 = OPTIMUS (O)}
Such configurations are presented in the following graphical layouts of Figure 7.
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Figure 7: OPTIMUS Rating Chart
The results are 2-tuple representations by Herrera et al.1, which mean that they are expression
composed by a linguistic term (e.g. Low, Medium, etc.) and a numeric value assessed in [-0.5, 0.5).
In this way, results can be presented transparently; are understood by the experts, since the SCEAF
main outputs are words; include also the numerical value that depicts the difference between the
numerical value and the index of the closest linguistic term (no loss of information is achieved in this
respect).
OPTIMUS SCEAF Tool
For the needs of visualization and better understanding, the OPTIMUS SCEAF Tool
(http://sceaf.optimus-smartcity.eu) was developed, based on the SCEAF philosophy. More
specifically, the OPTIMUS SCEAF Tool consists of three main pages, as follows:
In the first page (Home), a short description of the SCEAF and the developed tool purposes is
provided, as well as the ability of creating an account for a municipality or signing in, if an account
is already available.
The second page (SCEAF) includes the SCEAF questionnaire, where the user can import data
of municipal buildings for a given year to evaluate them.
Finally, in the third page (Submissions), the user can see the results of the assessment
calculated according to the SCEAF methodology, both for each individual building and for the
total of the municipal buildings, as well. Moreover, a comparison between the results of different
years is provided, giving this way the user the ability to observe the progress (ex-ante & ex-post
evaluation) and investigate whether the environmental and energy saving targets set have been
achieved.
1 Herrera F, L. Martınez L., Sanchez P.J. “Managing non-homogeneous information”, European Journal of Operational Research, 2005 166, pp. 115–132.
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Pilot Application
After collecting the data required for the ex-ante and ex-post evaluation of the three pilots, the
SCEAF questionnaire was filled using the OPTIMUS SCEAF Tool and indicators were calculated for
each pilot (Table 1).
Table 1: Final Scores per pilot (ex-ante and ex-post application)
Pilot
Sant Cugat
Town Hall
Sant Cugat
Theatre
Savona School
Savona Campus
Zaanstad Town Hall
Ex-ante L-0,35 (1.65)
VL-0,20 (0.80)
VL-0,04 (0.96)
VL+0,14 (1.14)
VL+0,21 (1.21)
Ex-post M-0,35 (2.65)
M-0,19 (2.80)
M-0,33 (2.67)
VL+0,32 (1.32)
M-0,21 (2.79)
Increase +1,00 +2,00 +1,71 +0,18 +1,58
A methodology was developed concerning the combination of 2 or more pilot buildings into one
SCEAF. The goal was to produce one single result for each city. This methodology particularly
applied to the Sant Cugat pilot site, where 2 buildings were available (Sant Cugat Town Hall and
Sant Cugat Theatre).
More details on the SCEAF structure and computations model can be found in the deliverable D1.2
“Smart City ex-post and ex-ante Assessment Framework”. The ex-ante and ex-post applications of
the OPTIMUS SCEAF Tool are available in D4.1 “Baseline Analysis Report” and D4.7 “Impact
Analysis Report”.
3.2 OPTIMUS TRACKER
OPTIMUS TRACKER (http:// tracker.optimus-smartcity.eu, http://optimus-smartcity.eu/solutions-
tracker) constitutes a web tool for the energy managers, in order to assess the potential of the city /
building for optimization and identify specific buildings where the OPTIMUS DSS can be applied.
Providing information on energy consumption overall figures and selecting Action Plans that are
more suitable for application in the buildings, OPTIMUS TRACKER offers the opportunity to create
different scenarios of the DSS application. These scenarios can be compared in terms of the
expected impacts, through the calculation of the DSS indicators:
Reduction of energy consumption.
Reduction of CO2 Emissions.
Energy cost reduction.
Increase of RES production.
In this way, the energy manager can take the decision to plug in single buildings and/or buildings
connected to energy production and other energy systems.
Optimus (O)
Very High (VH)
High (H)
Medium (M)
Low (L)
Very Low (VL)
Insignificant (I)
Optimus Rating Scale
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Approach
The overall approach of the OPTIMUS TRACKER is presented in the following figure.
Figure 8: Overall Approach of the OPTIMUS TRACKER
A short presentation of the web tool’s procedure, step by step, is analysed in the following
paragraphs.
Step 1 – Energy Data Registration: The energy manager provides information on energy
consumption overall figures, the energy sources breakdown per use, as well as RES production
for each building or category (e.g. administration, education, sports facilities, entertainment,
etc.). Moreover, data related to energy prices per energy source and the corresponding emission
factors are provided.
Step 2 – Action Plans Selection: The following stage consists of the selection of eligible Action
Plans according to the building profiles. The expected range of each Action Plan’s impact on
different aspects of energy optimization is registered. The full potential is estimated from each
Action Plan, both empirically and through literature (Table 3).
Step 3 – DSS Indicators per Building/Category: Based on the data entry, the following
indicators can be calculated for each building (or category): “Reduction of Energy Consumption”,
“Reduction of CO2 Emissions”, “Energy Cost Reduction”, and “Increase of RES Production”.
Step 4 - Scaling-up at the Targets: The final step includes the aggregation of results per
building (or category), in order to derive outcomes at the level of municipal buildings as a whole.
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Table 3: Potential Impact of the Action Plans
Action Plan
Reduction of Energy Consumption Reference
Use MIN MAX
1 Scheduling and management of the occupancy
Cooling 5% 9% “The results showed that room reassignment could further enhance the energy use reduction by up to
4,4% for heating and 9% for cooling”, in page 120 2.
“~8-11% energy savings”, in pages 15 3. Heating 2% 4%
2 Scheduling the set point temperature
Cooling 5% 9% “For each degree rise in supply-air temperature set point, there is about 5% to 6% reduction in total
HVAC energy consumption, depending on climate”, in page 25 4.
“A reduction of 1 K in internal temperature will reduce the energy consumption by 6%”, in page 166 5.
“Energy savings using an adaptive comfort model was estimated as 10 ÷ 18% of the overall cooling
load”, in page 126 6.
Heating 5% 9%
3 Scheduling the on/off of the heating system
Heating 5% 10% “The replacement of existing fixed start time control with optimum start/stop control can generate 10%
energy savings for heating systems operating single shifts, in pages 1 and 4 7.
2 The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use. Avai lable at: http://ac.els-
cdn.com/S0378778814003028/1-s2.0-S0378778814003028-main.pdf?_tid=51b7fe5e-0868-11e6-816f-
00000aab0f6b&acdnat=1461315662_016cea4a324c31ad5710895a4a08875c. 3 A Method for Calculating Chilled Water and Steam Energy Savings Due to Occupancy Scheduling in Large Buildings with Only One Year of Data. Available at: https://save-
energy.unc.edu/Portals/2/Calculating%20Occupancy%20Schedule%20Savings.pdf?ver=2012-10-26-133759-960. 4 “Energy Savings Modeling of Standard Commercial Building Retuning Measures: Large Office Buildings”. Available at:
http://www.pnnl.gov/buildingretuning/documents/pnnl_21569.pdf. 5 Architecture - Comfort and Energy. Available at:
https://books.google.gr/books?id=i8BLNYekFZMC&pg=PA166&lpg=PA166&dq=heating+set+point+temperature+energy+consumption+reduction&source=bl&ots=2Mx8nBo29L&si
g=j3c8oxBh6g_fo_
IHyzx3P4H1vRo&hl=en&sa=X&ved=0ahUKEwimrZWkmffLAhXBLw8KHXgCDgoQ6AEIOTAG#v=onepage&q=heating%20set%20point%20temperature%20energy%20consumptio
n%20reduction&f=false 6 Impact of different thermal comfort models on zero energy residential buildings in hot climate. Available at: http://ac.els-cdn.com/S0378778815003886/1-s2.0-S0378778815003886-main.pdf?_tid=5e1488a0-086a-11e6-bc27-00000aacb362&acdnat=1461316542_731e7b3dbdc54f05bc0602985cdfd7bf 7 How to implement optimum start control. Available at:
https://www.carbontrust.com/media/131445/ctl035_how_to_implement_optimum_start_control.pdf
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4 Management of the air side economizer
Cooling 10% 20% “As much as 20% savings in electrical energy for cooling were possible with demand-controlled
ventilation”, in page 1 8.
“Comfort is largely enhanced without mechanical cooling and reaches usual criteria while impact on
energy demand is limited to 10% of heating demand, in pages 791 9.
Heating 5% 10%
Action Plan
Increase of RES Production Reference
MIN MAX
5 Scheduling the PV maintenance
3% 8% Empirically (based on the available data from the pilot cities)
Action Plan
Reduction of Energy Cost Reference
MIN MAX
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Scheduling the sale/consumption of the electricity produced through the PV system
5% 10% “The cost savings achieved by charging according to the price-optimal strategy was about 10-15%”,
in page 7 10.
“research shows that 20%–30% of building energy consumption can be saved through optimised
operation and management without changing the structure and hardware configuration of the
building energy supply system.”, in page 2 11.
“Energy costs with and without battery” (reductions between 7 and 10%), in Table II, page 249 12 7
Scheduling the operation of heating and electricity systems towards energy cost optimization
5% 10%
8 The Impact of Demand-Controlled and Economizer Ventilation Strategies on Energy Use in Buildings. Available at:
https://customer.honeywell.com/resources/techlit/TechLitDocuments/63-0000s/63-7063.pdf. 9 Impact of control rules on the efficiency of shading devices and free cooling for office buildings. Available at: http://ac.els-cdn.com/S0360132305003975/1-s2.0-
S0360132305003975-main.pdf?_tid=8446db5c-07d6-11e6-9691-00000aacb35d&acdnat=1461253040_766a24e1f4f8f1fa4fb15218f50b9bde 10 Price-Based Demand-Side Management for Reducing Peak Demand in Electrical Distribution Systems – With Examples from Gothenburg. Available at:
http://publications.lib.chalmers.se/records/fulltext/163330/local_163330.pdf. 11 Energy-Efficient Buildings Facilitated by Microgrid, IEEE Trans. Smart Grid. Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5628267. 12Economic Model Predictive Control for Building Energy Systems. Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5628267.
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Pilot Application
Two submissions per OPTIMUS pilot site at the OPTIMUS TRACKER have been made, each
representing a different scenario (Figures 9 and 10). The first uses the minimum potential impact of
the selected Action Plans and the second uses the maximum one. Data about the energy
consumption, the RES production, the energy prices and the use of different energy sources were
submitted, based on the Baseline Analysis Report (D4.1).
Figure 9: Sant Cugat baseline submissions
Figure 10: Sant Cugat submitted buildings
For each building, the following selection of Action Plans was made:
Sant Cugat Town Hall:
Scheduling the set-point temperature.
Scheduling the on/off of the heating system.
Management of the air side economizer.
Scheduling the PV maintenance.
Scheduling the sale/consumption of the electricity produced through the PV system.
Sant Cugat Theatre:
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Scheduling the set-point temperature.
Scheduling the on/off of the heating system.
Management of the air side economizer.
Savona Colombo-Pertini School:
Scheduling the set-point temperature.
Scheduling the on/off of the heating system.
Scheduling the PV maintenance.
Scheduling the sale/consumption of the electricity produced through the PV system.
Savona Campus:
Scheduling the PV maintenance.
Scheduling the operation of heating and electricity systems towards energy cost
optimization.
Zaanstad Town Hall:
Scheduling and management of the occupancy.
Scheduling the set point temperature.
Scheduling the on/off of the heating system.
After the energy data input and the Action Plan selection, the OPTIMUS TRACKER results were
calculated, as displayed in the figure below. More details on the OPTIMUS TRACKER can be found
in D4.1 “Baseline Analysis Report”.
Figure 11: Sant Cugat results (minimum)
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3.3 OPTIMUS DSS
Based on real-time data monitored (weather conditions, buildings’ energy profiles, occupants’
feedback, energy prices and energy production) and predicted data produced by the prediction
models, OPTIMUS DSS (http://OPTIMUS-smartcity.eu/solutions-dss) generates Action Plans for the
energy managers based on a series of inference rules. A total of seven (7) Action Plans, supported
by nine (9) inference rules, are provided by the DSS, ready to accommodate energy managers willing
to plug - in their buildings. OPTIMUS DSS combines a series of components, namely the five “Data
Capturing Modules”, “Semantic Framework”, “DSS Engine” and “DSS Interface” (Figure 12).
The overall architecture of the OPTIMUS DSS is presented in deliverable D2.1.
Figure 12: “Data driven” decision support system
3.3.1 Data Capturing Modules
These are modules that capture data from the sources and send it to the semantic framework. A
module has been developed to gather data from each source (weather conditions, buildings’ energy
profiles, feedback provided by occupants, energy prices and energy production). More specifically,
the data captured by each module are the following:
Weather forecasting: Data regarding forecast weather conditions as well as weather data from
control units.
De-centralised sensor-based: Data regarding energy and environmental performance, mainly
through sensors.
Occupants’ feedback: Data from building occupants acquired through the TCV application or
social media, regarding comfort aspects.
Energy prices: Data regarding energy prices from the day-ahead market.
Renewable energy production: Data regarding the production of energy from any renewable
energy sources.
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Each module has been developed using different technologies. For example, the weather forecasting
module has been developed as a Java application, the same as the energy prices module, while the
renewable energy module has been developed as a Python application.
More details on the data capturing modules can be
found in deliverables D2.2-D2.6.
3.3.2 Thermal Comfort Validator
The OPTIMUS Thermal Comfort Validator (TCV,
http://validator.optimus-smartcity.eu) is a web
application designed to detect the thermal comfort
levels of the building's occupants, in order to be fed
into OPTIMUS DSS generate more suitable
suggestions (Figure 14).
Accessible via computers or smartphones, TCV
provides an online questionnaire, where building
occupants are requested to answer a short series of
questions, regarding their perception of
temperature, wind and sunlight indoors. Their
answers are analysed and aggregated, in order to
derive a general trend. TCV is available in four
different languages, English, Italian, Catalan and
Dutch. A specific TCV web app for the Sant Cugat
Theatre and a flyer with a QR code have been
developed in order to disseminate appropriately the
tool in the theatre (Figure 13).
The TCV application is used in parallel to the
DSS. The output is fed into the DSS which in turn,
based on this information together with the overall
goal to reduce energy consumption, can make
set-point temperature proposals.
More details on the TCV web app can be found in
deliverable D2.4.
Figure 14: Flyer with a QR code to disseminate TCV web app in Sant Cugat Theatre
3.3.3 Semantic Framework
It consists on the communication system, based on Semantic Web technologies, which facilitates
the transferring of data from the distributed sources and the subsequent contextualization of the raw
data in specific contexts. The semantic framework is based on the publish-and-subscribe
communication pattern which has been implemented with the Ztreamy system, a semantic service
Figure 13: Flyer with a QR code to disseminate TCV web app in Sant Cugat Theatre
20
which processes the data with the purpose of contextualizing them, and the Virtuoso triple-store as
a data repository. The semantic service has been implemented as a Python application.
More details on the semantic framework can be found in D3.1 “Published data in an open data
portal”.
Semantic Integration Process
The data integration process is based on Semantic Web technologies and it encompasses four steps
(Figure 15): “Data Translation”, “Data Communication”, “Data Contextualization” and “Data Storage”.
Since an ontology is used for producing the RDF data and for giving context, the data integration is
guaranteed.
Figure 15. Semantic data integration process
OPTIMUS Ontology
The OPTIMUS ontology represents a shared conceptualization of a building in operation, created
with the purpose of improving its energy efficiency. It contains the terms and attributes to describe
regions, cities, neighbourhoods, buildings, building partitions, systems and metering devices,
indicators such as energy consumption and CO2 emission, as well as climate and socio-economic
factors. The ontology models the static (e.g. building and technical systems features) and the
dynamic (e.g. metering) characteristics of a building and their context (e.g. climate conditions and
energy costs). The OPTIMUS ontology is based on two already existing ontologies: Urban Energy
ontology13 and Semantic Sensor Network ontology14.
The OPTIMUS ontology has been coded in OWL language using the ClickOn ontology editor15. This
editor provides a user-friendly interface which facilitates the ontology building process. The interface
of the ClickOn editor is composed of two simultaneous views of an ontology: one to edit the taxonomy
of concepts (e.g. family of sensors), and a second one to edit the aggregation relations (e.g. sensor
output). At the time of writing, the OPTIMUS ontology at is composed of 74 terms and 33 relations.
Semantic Framework
The semantic framework developed within the OPTIMUS project is composed of a publish-and-
subscribe system and a Semantic Service (Figure 16). The purpose of these tools is to integrate
13 http://semanco-tools.eu/urban-enery-ontology 14 http://www.w3.org/2005/Incubator/ssn/ssnx/ssn 15 http://semanco-tools.eu/click-on
21
data from different sources and domains.
Ztreamy server: The chosen publish-and-subscribe system is Ztreamy16. The Ztreamy server
is a Web service which receives data from the publishers through a list of streams previously
setup in a configuration file. The publishers (that is the data capturing modules developed in
WP2) send the data using HTTP calls.
Semantic Service: The Semantic Service has been developed in Python. The Semantic
Service is a subscriber that receives data from a Ztreamy server. The goal of the Semantic
Service is to contextualise the data sent by the data capturing modules. Like the server, the
service reads a configuration file which contains the list of streams to be listened and the
parameters needed for contextualizing the input triples. The contextualization parameters of the
configuration file are used to fill the RDF template used to contextualise the input data.
Figure 16. Implemented integration methods based on pub/sub systems
3.3.4 Prediction Models
Four (4) prediction models have been developed for forecasting the behaviour of renewable energy
production, energy consumption, indoor temperature and energy prices. The prediction models
connect the semantically integrated data with the inference rules. They take as input both, historical
and monitored data from the semantic framework in order to forecast the building behaviour.
Forecasted data are then used by the inference rules to suggest Action Plans. The prediction models
have been published as Web services using RapidAnalytics17 which is an open source suitable for
data mining solutions. A detailed description of the prediction models is provided in D3.2 “Analysis
tools to process data and inference rules”.
16 http://www.ztreamy.org/ 17 http://sourceforge.net/projects/rapidanalytics/
22
Table 4: Prediction Models
Prediction Models Technology
Energy production R script
Energy consumption R script
Indoor air temperature Rapidminer
Energy prices PHP script
3.3.5 Set of Inference Rules
Seven (7) Action Plans were structured based on a set of inference rules, using them in order to
derive suggestions for the energy managers to optimise the building’s performance taking into
account energy consumption, energy cost, carbon emissions, renewables production and thermal
comfort (Table 5). The Action Plans refer to the energy optimization in buildings taking into
consideration their interaction with energy systems, they can foster efficient management of energy
flows at a broader level, integrating energy demand, generation and data/energy infrastructures.
Table 5: Description of the Action Plans
Action Plans Description
En
erg
y
co
nsu
mp
tio
n
En
erg
y c
os
t
CO
2
em
issio
n
RE
S
pro
du
cti
on
Th
erm
al
co
mfo
rt
AP1
Scheduling and
management of
the occupancy
It aims at the reduction of the building
energy consumption by changing the
location of building occupants. This way, a
minimum number of thermal zones can be
used and the consumption can be reduced
by turning off the heating/cooling system in
the unoccupied zones.
AP2
Scheduling the
set-point
temperature
Based on the application of two inference
rules, this Action Plan is aimed to adjust the
indoor temperature set-point by taking into
consideration, respectively, thermal comfort
as submitted by the building users (using
the TCV web application), and the adaptive
comfort concept. The target is to optimise
energy use, while maintaining comfort
levels in accepted ranges.
23
AP3
Scheduling the
ON/OFF of the
heating system
Based on three inference rules, it aims at
the optimization of the boost time of the
heating system taking into account the
forecasting of the indoor air temperature
and the occupancy levels of the building.
AP4
Management of
the air side
economizer
It involves scheduling of the amount of
outdoor air to be used for cooling the indoor
environment, in order to reduce or eliminate
the need for mechanical cooling when
favourable conditions occur, using air-side
economizer technology.
AP5
Scheduling the
photovoltaic (PV)
maintenance
It aims at the detection of the need for
maintenance of the PV system, alerting the
user to check if corrective actions are
necessary. This facilitates the identification
of PV malfunctioning.
AP6
Scheduling the
sale/consumption
of the electricity
produced
through the PV
system
Optimization of selling/self-consumption of
electricity produced by a PV system
considering different scenarios of energy
market (green strategy, finance strategy
and peak strategy).
AP7
Scheduling the
operation of
heating and
electricity
It minimises the energy cost of the
building(s) by optimizing simultaneously the
operating schedule of the heating (CHP &
boilers) and electricity systems (grid, PV
plant & batteries) for the upcoming week.
Thus, the AP firstly specifies based on the
season (winter/summer) the schedule of the
heating/cooling systems and then suggests
when the energy generated should be used,
24
systems towards
energy cost
optimization
stored or sold in order to minimise energy
cost or even make a surplus. The outcome
of the energy demand and RES prediction
models, as well as weather and energy
prices forecasts, are exploited in order to
optimise the energy flows from/to the grid
and the batteries and minimise energy cost
based on load shifting and peak shaving
techniques.
The classification of the DSS suggested Action Plans in correspondence with inference rules is
presented in Figure 17.
Figure 17: Classification of the DSS suggested Action Plans with the corresponding inference rules
Inference rules were developed in the form of energy models, either in excel files or in R
programming language. The inference rules have been implemented as a Symfony PHP web
application (see deliverable D3.3 “Inference engine integrated in the management environments”).
It should be noted that OPTIMUS DSS is characterised by a combination of advanced technologies
25
that enables integration of multiple domains.
Τhe Action Plans are categorised, according to their applicability, to buildings and/or block of
buildings, with some of them allowing more comfort, functionality, and flexibility through
integration of energy generation and storage systems (“Sustainable Districts & Built
Environment” Domain).
Moreover, some of the Action Plans enable the interconnection of energy infrastructures and
new technologies (“Integrated Infrastructures & Processes across Energy and ICT”
Domain).
Table 6 presents the interrelationship between Action Plans and SEC domains. This table shows the
eligibility of Action Plans with regard to the available data and equipment in the selected buildings.
Table 6: Interrelationship between Action Plans and Smart Energy Cities Domains
Action Plans of the
OPTIMUS DSS
Domains
Sustainable Districts & Built
Environment
Integrated Infrastructures &
Processes across Energy and ICT
Bu
ild
ing
Blo
ck o
f
bu
ild
ing
s
En
erg
y
Gen
era
tio
n
Sto
rag
e
Syste
ms
Th
erm
al
Lo
ad
s
Sm
art
Mete
rin
g -
Sen
so
rs
Mo
bile
Devic
es
Pre
dic
tive
Op
era
tio
n
Sh
ifti
ng
Lo
ad
s
AP1
Scheduling and
management of the
occupancy
AP2 Scheduling the set-
point temperature
AP3
Scheduling the
ON/OFF of the heating
system
AP4 Management of the air
side economizer
AP5
Scheduling the
photovoltaic (PV)
maintenance
AP6
Scheduling the
sale/consumption of
the electricity produced
through the PV system
AP7
Scheduling the
operation of heating
and electricity systems
towards energy cost
optimization
OP
TIO
NA
L
OP
TIO
NA
L
26
3.3.6 DSS Engine
The goal of the DSS engine is to propose Action Plans to the end user. To do so, the inference rules
have to be fed with predicted, real-time and static data. The DSS engine is composed of prediction
models (implemented as RapidAnalytics processes and R scripts), inference rules, and a MariaDB
database to store the results (Figure 18).
Figure 18. Data flow from monitored and predicted data to the Action Plans
The prediction models are invoked every day at night for each variable required by an Action Plan.
The predictions have a time horizon of seven days. In this way, each day, a prediction for the
following seven days is carried out. Action Plans are pre-calculated just after the predictions have
been carried out. In this way, users can visualise the output of the Action Plans and the monitored
data with a short time response. Action Plans use the data from the last prediction. Figure 19 displays
how the prediction and Action Plans calculations works.
Figure 19: Predicted data and Action Plans calculation
3.3.7 OPTIMUS DSS Interfaces & Implementation Process
Interfaces
Figure 20 shows the updated sitemap of the two DSS environments, for end-users and for
administrators. The blue boxes correspond to the end-user environment and the green boxes for the
management environment. If the profile of the user is “administrator” then it is possible to access the
management environment. Otherwise the access is restricted to the end-user environment.
27
Figure 20: OPTIMUS DSS sitemap
The OPTIMUS DSS incorporates the results from the OPTIMUS TRACKER tool, namely the targets
and the potential impact of the DSS for the participating pilot cities. In addition, there it includes
information about the buildings in which the DSS is installed (city dashboard), Action Plans, historical
data, weekly reports and user activity per building (Figures 21-23).
Figure 21: City Dashboard
28
Figure 22: Historical Data
Figure 23: An example of an enhanced interface of an Action Plan
The latest version of the OPTIMUS DSS includes virtual sensors, that is, non-physical sensors
whose data is obtained from existing sensors. Virtual sensors integrate and transform data from
existing sensors.
29
A GitHub repository has been created to maintain the source code of the OPTIMUS DSS
(https://github.com/epu-ntua/optimusdss). Three release branches have been created, one per pilot:
release-zaanstad, release-santcugat and release-savona.
Installation and Configuration
Following the installation and configuration of the OPTIMUS DSS and the training of the users, the
selected Action Plans will be fully operational to be applied to the buildings. In practice, the DSS will
be used by the energy managers of the city / buildings. Training has (at least) a twofold meaning in
the adoption of a DSS:
Training of the people who will use the DSS and follow up with the suggested actions;
Training of the people who will benefit from the DSS or will impact on the scenarios that the DSS
will face.
Implementation
The implementation of OPTIMUS DSS in the pilot cities is a cyclic process spanning over time in
which various actors carry out specific actions interacting with each other (Figure 24):
Figure 24: Participants in the OPTIMUS DSS cyclic workflow
OPTIMUS Team
These are the designers of the DSS who have created the prediction models and inference rules for
the actions plans to be deployed. At the start of the implementation process, the OPTIMUS team
configures the DSS to meet the specific requirements of the pilot city. This is done by means of the
DSS interfaces which are available to the user “administrator” (Figure 25).
30
Figure 25: Configuration of the DSS for a pilot case (Sant Cugat Town Hall)
DSS User
This is the technician in the pilot city in charge of interacting with the DSS on a daily basis, accepting
or rejecting the recommendations of the DSS (Figure 26). The technician is in contact with the
Occupants of the buildings and receives their feedback after the application of the measures
recommended by the DSS. The DSS User reports to the Energy Manager the effects of applying the
DSS measures. The communication between both can occur outside (e.g. email, face-to-face
communication) or inside the DSS via the reporting tool.
Figure 26: Action Plans’ Selection
Energy Manager
This is person responsible for the management of the buildings owned by the municipality. As such,
he/she has the capacity to set-up the overall strategy to achieve the energy efficiency levels set-up
at the city level. The Energy Manager reports to the DSS Team the changes that are necessary to
adapt the DSS implementation to the strategic goals set-up by the municipality (Figure 27).
31
Figure 27: Weekly Report
The final goal of the communication of the different participating actors over time is to contribute to
adjust the prediction models to the actual building performance, by reporting the incongruences
observed in the predictions and providing explanations for the decisions adopted with regard to the
acceptance or rejection of the DSS recommendations.
3.3.8 Insights from the OPTIMUS DSS Pilot Implementation
Some insights per Action Plan during the pilot implementation of the Action Plans to the three cities
are summarised in the table below (more details can be found in the deliverable D4.6 “Evaluation
Report”).
Table 7: Insights per Action Plan during the Pilot Implementation
Action Plans Insights
AP1: Scheduling
and management
of the occupancy
Zaanstad Town Hall: This action plan was selected for Zaanstad taking
into consideration that the employees do not have fixed working desks but
can choose the place where they want to work. OPTIMUS DSS receives
data from the building and transform them into real action plans, namely
which part of the building can be left empty on specific days. During the
implementation of the occupancy action plan significant reduction of
energy consumption was achieved.
AP2: Scheduling
the set-point
temperature
Zaanstad Town Hall: The DSS user of the Zaanstad Town Hall decided
that the scheduling of the set point temperature can be carried out every
day. The DSS user informed the technical end user to implement the
32
action plans on the following days till further notice. It has never led to
complaints from the occupants of the building.
AP3: Scheduling
the ON/OFF of the
heating system
Sant Cugat Town Hall: The action plan concerns the optimisation of the
boost time of the heating system. The winter 2016 was unusually hot in
Sant Cugat and therefore the model based on a grey box approach that
was validated using the heating season 2015, could not be properly
applied. However, through simulations it was demonstrated that the
management of the start/stop of the heating system according to the
model implemented in the DSS can potentially reduce, even if slightly, the
energy consumption for space heating.
AP4: Management
of the air side
economizer
Sant Cugat Town Hall: The economizer action plan gives the opportunity
for the use of the outdoor conditions to precool the building when the
outdoor condition are in properly conditions. This kind of suggestions are
highly appreciated in the Sant Cugat Town Hall due to its potentiality to
save energy for cooling. The action plan suggests different schedules in
order to apply total or partial free cooling depending on the indoor and
outdoor conditions. For instance, in summer time the suggestion was to
supply outdoor air without treatment from 5 am till 8 am, in order to precool
the building since the outdoor temperature was low enough.
AP5: Scheduling
the photovoltaic
(PV) maintenance
Savona Campus: OPTIMUS DSS correctly issued warnings for the PV
system in the Savona Campus. For instance, during the week 20-24/6 the
local control panel of the PV inverter was out of order (kept resetting) and
this made the inverter go off-line and back on-line from time to time,
resulting in a loss of production. In this respect, UNIGE contacted the
inverter maintenance service to solve this malfunction.
AP6: Scheduling
the sale/
consumption of the
electricity
produced through
the PV system
Savona School: OPTIMUS DSS suggests for the week ahead a
procedure which allows both to improve the exploitation of solar energy
maximizing the self-consumption of electricity produced by PV on-site and
to take advantage from the selling of the energy surplus considering the
energy prices. The green strategy implemented in Savona school can
contribute to maximise the use the daily amount of energy from renewable
sources. The data driven models of energy generation by PV and total
electrical energy demand proved to be robust to predict the surplus of
energy on the basis of the historical performance data. This rule was
highly appreciated in Savona school where electrical loads associated to
the computer laboratory were selected as the most suitable shiftable loads
for exploiting the potentialities of AP6.
AP7: Scheduling
the operation of
heating and
electricity systems
towards energy
cost optimization
Savona Campus: As far as the scheduling of sources is concerned, the
power profile computed by the OPTIMUS DSS is imposed to the Energy
Management System (EMS) of Savona Campus. For instance, the
electricity storage system operates following the scheduling suggested by
the DSS. In this respect, UNIGE specifies the "fixed scheduling" operating
mode in the input file for the unit commitment module of the Campus EMS,
33
copy the DSS scheduling in the same input file and run the unit
commitment algorithm.
The insights per pilot city are summarised below:
Sant Cugat, Spain: To be a pilot city has had a lot of positive benefits, not only in terms of future
energy savings but in terms of changing the behaviour of the people involved in the energy
management of buildings. OPTIMUS has highlighted that Sant Cugat must collaborate much
more with the IT department than they used to (one of the most challenging tasks of the project
has been the interoperability). From now on it is important to have a wider approach of what
energy management of building means; there are many technicians involved with different tasks
and all of them need to optimise the energy use. The way the city manages the park is through
a public private partnership that allows making investments in order to reduce the energy
consumption and take advantage of the installations. In the coming weeks, Sant Cugat has to
put the service out to a tender in order to have the new contract ready till next July. OPTIMUS
tool will be mandatory for the whole park of public buildings included in the tender. There
are more than sixty buildings that will be included; schools, sports pavilions, cultural and
administrative buildings. On the other hand, another key aspect has been the engagement of
the users through the TCV; they have modified the way they perceive changes and are more
engaged with the management.
Savona, Italy: OPTIMUS is one of the tools that will help the municipality of Savona fulfil its
vision towards becoming a Smart City. Through OPTIMUS DSS the city can finally achieve
energy savings and become more environmental friendly. The experience gained with the
Campus will help the Municipality in evaluating and planning the integration of renewable
resources and/or cogeneration plants in different districts of the city and in a number of areas
where refurbishment projects are under consideration or already completed. The information
and operational experience of both the School and the Campus will be exploited as guidelines
in the use of the web application developed in the context of the project, to assess the impact at
city level of various possible scaling up options of the current infrastructure.
Zaanstad, the Netherlands: OPTIMUS DSS contributes to the implementation of the objectives
of the Climate Program of Zaanstad. Indeed, Zaanstad can better match the supply with the
demand and reduce energy in an innovative way through the DSS. By working with OPTIMUS,
it became clear that there are a lot of departments with different interests and field of action. All
those different departments had a role or were connected in a way with OPTIMUS. In this
respect, Zaanstad is now reshaping the organisation to avoid split incentives on the subject of
energy savings.
The degree of generalization of the OPTIMUS DSS makes this advanced tool adaptable to cities
with different features regarding, for example, types of buildings, energy infrastructures and energy
demand. All these aspects open more opportunities and offer greater business potential in the
market for a DSS as the one implemented by OPTIMUS project (more details can be found in D5.10
“Exploitation Planning and Service Business Model”.
34
4 Impact
The set-up and the use of the OPTIMUS DSS have different kinds of potential impacts. According to
the methodology defined in the deliverable D4.7, the OPTIMUS DSS directly affects the following
performance fields:
Energy consumption.
Renewable energy production.
Energy cost.
CO2 emission.
Thermal comfort.
In the following table, the impact is analysed for each Action Plan.
Table 8: Impact of the Action Plans
Action Plan Impact
AP1. Scheduling and
management of the
occupancy
Whenever a possibility of relocating the building occupants exists,
the resulting turning off of the technical systems of the empty building
zone/s gives the possibility of reducing the energy consumption of
the building. The scheduling and management of the occupancy has
been applied in Zaanstad Town Hall and it has been calculated that
the monthly average delivered electricity, and consequently the CO2
emission and the energy cost, are reduced by approximately 20%.
This Action Plan has also impact on the behaviour of the building
occupants that are the main actors involved. The Action Plan also
has impact on the occupants’ productivity and comfort.
AP2. Scheduling the set-
point temperature
The scheduling of the set point temperature depends both on
adaptive comfort evaluation and on the feedback from the occupants
through the TCV web app. If the set point temperature suggested
according the adaptive comfort concept is not accepted by the
occupants, it may be modified taking into account the thermal
sensation of the occupants who give feedback to the DSS. The
impact related to this rule includes both the building energy
consumption and the thermal comfort of the occupants.
AP3. Scheduling the
on/off of the heating
system
The impact of suggesting the optimal start and stop of the heating
system is higher if no energy management system is already
installed in the building. In the pilots, the calculated reduction of the
energy consumption for the space heating related to this Action Plan
does not exceed 4% because an algorithm for the optimal start and
stop is already integrated in the system.
AP4. Management of the
air side economizer
Regarding the use of the air side economizer, if the climatic condition
are favourable, the impact turned out to be around 20% for the pilot
of Sant Cugat.
35
AP5. Scheduling the PV
maintenance
The Action Plan related to the production of renewable energy, like
the maintenance of the PV, may have an impact till 11% depending
on how often the faults occur.
AP6. Scheduling the
sale / consumption of
the electricity produced
through the PV system Both Action Plan 6 and 7 are related to the possibility of shifting loads
and therefore the impact is strictly related to the presence of shiftable
loads. The possibility of shifting loads in Savona school is limited and
the electricity produced though PV usually lower than its energy
demand.
AP7. Scheduling the
operation of heating and
electricity systems
towards energy cost
optimization
The DSS implementation can achieve significant reduction of the energy consumption, CO2
emissions and energy cost (in some cases even beyond 20%), as well as approximately 10%
increase of renewable energy production. The results strongly depend on the current status of the
building, as well as the Action Plans which will be implemented.
Table 9: Impact of the DSS per pilot site
Pilot Site
Delivered energy
[electricity]
%
Delivered energy [natural gas]
%
CO2 emission
%
Energy cost
%
RE consumed
%
Savona Campus -4.0 -2.8 13.3
Savona School -5.3 -7.8 -7.3 10.2
Sant Cugat Town Hall -29.4 -29.4 -29.4 7.4
Sant Cugat Theatre -51.4 -39.0 -47.0 -48.0
Zaanstad Town Hall -23.5 -23.5 -23.5
The OPTIMUS DSS has also not quantifiable impacts, such as those related to social aspects. In
fact, the DSS is not only targeted at the building energy manager but also at the building occupants
(employees, students etc.), who are asked to change their behaviour or to at least to actively
participate in the building management by informing the energy manager about the faults or
malfunctions of the building.
The thermal comfort of the occupants may have impacts related on the social aspects. From the end
users point of view, the DSS has improved through the implementation of the TCV application the
way the complaints or suggestions of the users are provided. After its introduction, the users seem
to be far more engaged due to the fact that they feel part of the whole decision process. The
possibility given to the occupants to send their feedback (through the TCV web app) may increase
their participation and therefore make them more conscious about the management of the building.
Moreover, the possibility of increasing the occupants’ participation may also affect their satisfaction
and therefore their productivity.
More than 440 occupants in Sant Cugat Town Hall, 300-500 occupants in Sant Cugat Theatre, 200-
36
1,200 occupants in Savona Campus, 20-500 occupants in Savona School and 700 occupants in
Zaanstad Town Hall interacted with the OPTIMUS DSS. In addition, more than 590 feedbacks have
been received through the TCV web app.
Other than the specific pilot, the possibility of shifting loads may have a significant impact both on
the renewable energy production (and consequently on CO2 emission and energy cost) and on the
social aspects related to the building occupants. The load shifting is also connected to the possibility
of modifying the habits of the occupants as to improve the performance of the building. This Action
Plan has the effect to increase the awareness of the occupants on how their behaviour affects the
building performance.
A total of 79 persons were working in the OPTIMUS project, including scientific coordinators (4%),
work package leaders (11%), experienced researches (39%), PhD students (6%) and other
workforce (39%). In addition to existing staff, 4 additional researchers were recruited by 2 partners
in order to meet the requirements of the project. In total, the 28% of the workforce was female, while
the 72% was male. A positive work environment of mutual respect was built within the consortium.
The buildings’ occupants were actively participating in the pilot implementation phase, including
among them children in the schools, staff in municipal buildings, etc. Working sessions with students
and school pupils were organised, for example, the “dissemination days” in the Savona School to
get the support of the building’s occupants to use the TCV tool. A substantial amount of training
material was distributed among the staff of the pilot cities participating in the implementation of the
OPTIMUS DSS, such as tutorials, presentations and videos.
Moreover, some partners have already declared their interest to continue using the DSS, after the
end of the project, which demonstrates the value of the innovative technologies developed.
A number of stakeholders were engaged during the OPTIMUS events (conferences and training
workshops), such as ESCOs, energy conservation companies, building management expert,
consultants, etc. Training and dissemination material (PPTs, tutorial, videos, factsheets and market
brochure) contributed to make these stakeholders more aware, more knowledgeable of how the
building works, more aware of the need to save energy, etc. It should be also noted the increase of
awareness among the people involved in the implementation of the DSS regarding energy costs
reduction.
There has been a continuous dissemination of the project activities throughout all the project lifetime.
Press releases, media briefing, brochures, coverage in national and international press, website
announcements and special events were just some of the ways that the work of OPTIMUS was
disseminated. Moreover, 44 external events attended by project partners reaching out to 10,700
relevant stakeholders, incl. a high share of local authorities. The research outputs of the project have
been disseminated through 27 publications both in peer-reviewed journals and in conference
proceedings by project partners.
The representatives of the local administrators and policy makers were strongly engaged throughout
the whole project development. A number of meetings took place regarding the requirements capture
phase, the design of mock-ups, the implementation of the DSS on their sites, the workshops
organized in each city, and the overall collaboration in the dissemination (brochures, video
interviews, etc.). In this respect, the output of the project is expected to affect and assist bodies at
local, national, European or even international level, by helping municipalities and whole cities
become smarter.
37
5 Exploitation Strategy
5.1 Expansion of the OPTIMUS Package
Although OPTIMUS gives particular emphasis on the municipal building sector, the overall approach
can be applied to different types of buildings, such as buildings for entertainment and sports facilities,
hotels, etc. OPTIMUS package has, by design, the necessary degree of generalization, so as to be
adapted to additional buildings of the participating cities, as well as additional cities outside the
consortium with different characteristics, energy infrastructures, needs, priorities and types of energy
demand.
Figure 28: Upscaling of the OPTIMUS Approach
An example of the upscaling of OPTIMUS approach is provided in Figure 28. More specifically,
based on the available data and infrastructure, the energy managers of the city / buildings can decide
to plug in:
Single buildings (APs 1-4).
Buildings connected to energy production (APs 5-6).
Buildings connected to energy production and other energy systems (AP 7).
Moreover, the developed approach gives the opportunity for the development of new Action Plans
that will integrate additional energy systems and domains (priority areas) of the SEC, such as
“Sustainable Urban Mobility” (e.g. optimal charging scheduling of the electrical vehicles, etc.).
5.2 Integrated Planning at the City Level
OPTIMUS package of consulting tools is an advanced and intelligent turn-key solution, addressed
to any SEC that has as purpose to implement sustainable energy Action Plans (e.g. the cities that
have signed the Covenant of Mayors initiative) and has need to systematically support and monitor
the implementation of those Action Plans.
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While the OPTIMUS DSS is not explicitly intended to provide direct decision support in the longer
term strategic planning of the cities’ energy systems, it can inform and thereby indirectly support
these planning processes through the provision of invaluable data and system knowledge. The very
comprehensive data sets on energy profiles and system optimisation in the DSS can provide an
essential and solid foundation for a robust analysis of future planning scenarios and options.
5.3 Visualization of the City Level Approach in the DSS
The connections between the energy assessment at the city level carried out with the SCEAF and
the TRACKER and the optimization conducted in the DSS is reflected in the interfaces. Figure 29
shows the entry screen of the DSS, in which the results of the assessment carried out by the
TRACKER set-up the baseline for the four indicators (energy consumption, carbon emissions,
energy costs and production of renewable energy) to be improved through the optimization of the
buildings.
Figure 29. DSS Targets: Results of the Tracker integrated in the DSS interface
In the screen shown in Figure 30, the user can see the aggregated values of the indicators for all the
buildings of the city which are being optimised through the recommendations of the DSS and check
if they are approaching the values set by the TRACKER.
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Figure 30. DSS City Dashboard: Aggregated values of buildings
5.4 Exploitation and Business Plan
The strategy for the use and exploitation of the project results beyond the project framework and
lifetime has been defined in the deliverable D5.10 “Exploitation Planning and Service Business
Model”. It presents the exploitation plans, IPR issues and the Service Business Model of the
OPTIMUS project.
On the one hand, the document provides a roadmap to allow the highest possible exploitation of the
results of the project, addressing the evolution from a research to a market scale dimension. On the
other hand, a Service Business Model has been developed for the commercialisation of the
OPTIMUS DSS, its modules and all the other related outputs of the project. More specifically, the
following actions were taken into consideration:
The results of the project were identified and characterised; the ownership of the knowledge
generated during the project, related to each result, was also specified by the partners.
The OPTIMUS DSS and its commercialisation strategy was defined; the reference market was
analysed and the business model was developed, clearly describing the potential customer of
the system, the distribution and promotion channels, and the foreseen revenues and costs.
The exploitation agreement for the commercialisation of the OPTIMUS integrated solution was
defined.
It should be noted that for the implementation of the OPTIMUS DSS, there are two possibilities:
Basic Version: to pay a fixed price, including the necessary customization of the data capturing
modules (weather conditions, buildings’ energy profiles, feedback provided by occupants,
energy prices and energy production) and the creation / calibration of the prediction models
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(energy consumption / production, etc.). Through these two actions, the DSS will be tailored to
the specific features of the target facility;
Premium Version: to pay a fixed price for the basic version plus an additional variable price for
the calibration of the action plans.
In addition, a Business Plan Development (BPD) session took place during the 6th Project Meeting
in Athens, Greece (20th of September 2016), within the framework of the Support Services for
Exploitation of Research Results (SSERR).
OPTIMUS consortium is trying to exploit possible synergies towards OPTIMUS sustainability after
the project end. Some of the OPTIMUS exploitation activities within the project’s lifetime are the
following:
The City of Athens has expressed its interest in OPTIMUS DSS installation for the energy
management of selected municipal buildings, such as the town hall and other office buildings,
swimming pool, etc.;
A Greek company is investigating the possibility to upgrade an available database of Greek
municipal buildings (DATABUILD), integrating OPTIMUS DSS for real-time monitoring and
energy management;
Masdar Institute is currently developing the project “Demand Side Management Optimization for
achieving 100% RE in Building Micro-Grids” and it is investigating the appropriate modules that
have been developed within the framework of the OPTIMUS project.
It is examined the integration of the OPTIMUS DSS with the smart home solutions that the
Transversal Business International company is currently developing together with other
companies. Transversal Business International is distributor and co-developer of the PowerIN
House system, a smart home automation energy management system that can run entirely on
alternative or hybrid energy and can be integrated in any new or existing dwelling.
Discussions with utilities, energy providers and other related key actors took place during the
Public Power Cooperation (the biggest electric power company in Greece) Workshop on Smart
Grids (19th of October 2016, Athens, Greece) and the 6th Workshop on Smart Grids “Cooperation
of the Hellenic Electricity Distribution Network Operator with Greek Universities” (6 April 2016,
Athens, Greece).
Individual promotion of OPTIMUS towards industrial players (Siemens, Veolia, E.ON etc.).
A Greek supplier of Carlo Gavazzi equipment came in contact with NTUA, aiming at the combination
and interconnection of advanced Information and Communications Technology (ICT) tools
(OPTIMUS DSS), smart automation systems (Dupline) and smart technologies and equipment
(smart meters, sensors, etc.). In this context, Carlo Gavazzi equipment was placed across the
premises of the NTUA lab (costs covered with NTUA own resources) and a pilot connection to the
OPTIMUS DSS was implemented. Moreover, a pilot connection of the OPTIMUS DSS with the
installed equipment in the building of the Regulatory Authority for Energy (RAE) of Greece was
made. The RAE building is the first in Greece to have an ISO 50001 standard installed (February
2014), based on verification procedure for the energy savings.
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6 Main Dissemination Activities
6.1 Achievements
Raising awareness of energy issues in public buildings faced by local authorities, the project
development, its outcomes and realisation of OPTIMUS DSS tool
Due to dissemination 14,025 sessions and 10,119 unique users from EU countries have
accessed the OPTIMUS website to explore the OPTIMUS solution package.
OPTIMUS press and media activities resulted in e- and printed publications with in total well
above 1 million circulations.
Total of 44 external events attended by project partners reaching out to 10,700 relevant
stakeholders, incl. a high share of local authorities.
KPIs on event participation with relevant target audience exceeded. Partners continue to present
and promote OPTIMUS beyond project lifetime at e.g. Sustainable Buildings 2016 (600
attendees) or Local Renewables 2016 (162 attendees).
27 published articles both in scientific journals and in conference proceedings by project
partners.
The OPTIMUS e-newsletters were disseminated annually in November 2014, November 2015
and April 2016. A special edition was sent out in October 2016 with a summary of final outcomes
and products.
Transferring methodological and technical know-how beyond the consortium
Stand and session at strategic events with high impact and relevance to transfer technical know-
how and including training elements such as the European Sustainable Cities and Towns
Conference with 1,200 participants or the Smart City event in Amsterdam with 200 participants.
Strong link and cooperation with the Covenant of Mayors Initiative (Secretariat, supporting tools
and Signatories) including e.g. a presentation of OPTIMUS at a CoM expert workshop with
OPTIMUS stakeholders from various European countries and using relevant technical and
policy mailing lists to transfer knowledge to more than 2,000 stakeholders of the OPTIMUS
target audience in Europe.
OPTIMUS results (solutions, factsheets, findings) permanently integrated into the Covenant
capaCITY Training platform which was built and is used as an online training platform to support
learning and advanced local authorities and their stakeholders (including trainers) to develop
and improve Sustainable Energy Action Plans (SEAPs).
Outlining the business benefits by specialised on the potential energy consumption reduction that
may result for cities from the adoption of the proposed solution
Highlight of benefits and business strategy discussions and sessions in all three OPTIMUS
training workshops and the OPTIMUS Final Conference with key experts including the
OPTIMUS Special Interest Group.
Comprehensive exploitation plan with detailed benefits (and costs) developed for each specific
target group and user of the OPTIMUS solution package which reflects and incorporated public
procurement potentials and requirements in order to facilitate the uptake of the tools in local
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authorities in Europe.
OPTIMUS market brochure as additional exploitation tool developed to support and sustain the
uptake after the project lifetime.
Website revamp to emphasise the potential energy reduction and display information on the
OPTIMUS products in a clear and accessible way, including a tutorial and testimonials from the
pilot cities.
6.2 OPTIMUS Events
OPTIMUS Final Conference
The OPTIMUS Final Conference “OPTIMUS Technological Solutions for Real-time Monitoring
and Energy Management in Smart Cities” took place on the 21st of September 2016 in Athens,
Greece, hosted by NTUA and co-organised by ICLEI Europe. The purpose of this Conference was
to present new trends in Smart Energy Cities, giving particular emphasis on the innovative solutions
of the OPTIMUS project, the tools produced and how these can be applied by energy or facility
managers from cities and organisations from across Europe.
Figure 31: OPTIMUS Final Conference, 21st of September 2016 in Athens, Greece
Training Workshops
A series of workshops were held during the project in the three case study cities, in order to engage
and train the relevant target groups on the full appliance of the OPTIMUS solution package.
Training Workshop in Sant Cugat, 31st of May 2016.
Training Workshop in Zaanstad, 7th of June 2016.
Training Workshop in Savona, 6th of July 2016.
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Figure 32: OPTIMUS Training Workshops
Smart City Event in Amsterdam
Representatives from the pilot cities of OPTIMUS participated in the Bootcamp "Smart Energy" and
presented the OPTIMUS DSS, on Thursday, 9th of June 2016, during the international Smart City
Event in Amsterdam.
Figure 33: Smart City Event, 9th June 2016, Amsterdam, the Netherlands
Special Sessions
The official launch of the OPTIMUS DSS took place on the 27th of April 2016, at the Breakout Session
"Efficient Cities", within the framework of the 8th European Conference on Sustainable Cities and
Towns, which attracted 1,200 participants from all over Europe on 27-29 April in Bilbao. An
OPTIMUS stand supported the dissemination of the tools and the recruitment for the up-coming
workshops and newsletter subscriptions.
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Figure 34: Official Launch of the OPTIMUS DSS, 27th of April 2016, Bilbao, Spain
Special Sessions took place within the framework of the International Conference on Information,
Intelligence, Systems and Applications (IISA 2014, 2015 and 2016). The Special Sessions
brought the opportunity for researchers to present state-of-the-art, as well as exchange experience
and ideas about energy management services and energy use optimization in Smart Cities.
Other Events
As face-to-face communication is the most effective way of communication, members of the project
consortium have attended several events to reach relevant target groups. It should be noted that
partners are committed to present, promote and exploit the developed OPTIMUS solution package
beyond the lifetime of the project.
Figure 35: Events Attendance from OPTIMUS Partner
6.3 Training Material
In order to facilitate and sustain the continued use of the tool after the project ends, the following
forms of material which can be accessed online and housed on the product website were produced
at the end of the project (http://optimus-smartcity.eu/training-material). These are designed to
support users by taking them through how to use the OPTIMUS Decision Support System (DSS)
and other tools step-by-step:
PPTs: They include: (a) a general overview of how the DSS works, the DSS interfaces and
actions to take in response to DSS outputs; (b) OPTIMUS SCEAF and TRACKER operation; (c)
Introduction to the Thermal Comfort Validator (TCV) web app.
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Tutorial: A tutorial document was developed, in order to guide the users step-by-step in the
OPTIMUS DSS procedures and functionalities.
Videos: An online video tutorial was produced with reference to the PPTs
(https://www.youtube.com/watch?v=SJZv-RBX3VM). The duration is 10 minutes. Moreover, a
short video for the presentation of the OPTIMUS project was produced
(https://www.youtube.com/watch?v=wJJ6cdiFXVQ).
Factsheets: A series of factsheets have been produced and were updated at the end of the
project to provide more detailed information on the most important aspects of the OPTIMUS
solution package. They include: (a) Inference rules; (b) Prediction Models; (c) OPTIMUS DSS;
(d) SCEAF; (e) web-based environments (http://optimus-smartcity.eu/optimus-factsheets).
Final results brochure: An OPTIMUS results brochure was produced and printed as additional
exploitation tool to support and sustain the uptake after the project lifetime. The six-page
marketing brochure reflects the main results, outputs and benefits of the final products. 3.000
copies were printed and distributed between consortium partners. Moreover, the brochure is
disseminated electronically through all available communication channels.
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6.4 OPTIMUS Market Brochure
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7 Consortium
No Name Short Name Country
1 NATIONAL TECHNICAL UNIVERSITY OF ATHENS NTUA Greece
2 FUNDACIO PRIVADA UNIVERSITAT I TECNOLOGIA
FUNITEC Spain
3 ICLEI EUROPEAN SECRETARIAT GMBH ICLEI EUROPE Germany
4 FUNDACION TECNALIA RESEARCH & INNOVATION
TECNALIA Spain
5 POLITECNICO DI TORINO POLITO Italy
6 D'APPOLONIA SPA D'APPOLONIA SPA Italy
7 UNIVERSITA DEGLI STUDI DI GENOVA UNIGE Italy
8 COMUNE DI SAVONA SAVONA Italy
9 SENSE ONE TECHNOLOGIES SOLUTIONS SENSE ONE Greece
10 GEMEENTE ZAANSTAD ZAANSTAD Netherlands
11 AJUNTAMIENTO DE SANT CUGAT DEL VALLES SANT CUGAT Spain
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8 OPTIMUS Website & Social Media
Project Website URL: http://optimus-smartcity.eu/
City energy managers and facility / building managers, technicians working on a Sustainable Energy
Action Plans and Smart City solutions, Covenant of Mayors supporters and coordinators, energy
agencies, energy providers, companies, energy/IT experts, researchers and all interested
stakeholders are invited to join the OPTIMUS social media activities at:
