web-based method to generate specific energy consumption ... · Proceedings of the Fourth...

University Karlsruhe (TH) - Department of Architecture

Building Physics and Technical Building Services

web-based method to generate specific energyconsumption data for the evaluation and optimisationof building operation

Andreas Wagner, Mathias Wambsganß, Sabine Froehlich

project partners:

• German Railway Track Infrastructure (DB Netz AG), Frankfurt a. Main, Germany

• University of Karlsruhe, Department of Architecture, Building Physics and Technical Building Services

funding:

• 50 % German Ministry of Economy and Labour

• 50 % German Railway Track Infrastructure (DB Netz AG)

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Proceedings of the Fourth International Conference for Enhanced Building Operations, Paris, France, October 18-19, 2004



content

motivation and goals 1

analysed buildings 2

method 3

results 4

conclusions 5

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motivation• very high potential for the reduction of CO2

emissions in existing buildings

• insufficient knowledge about energy consumptionand specific data especially in large building stocks

• user complaints and energy consumption arerarely considered in building operation

• reduction of energy consumption and operation costsas well as ensuring a high work space quality

• development of an economical method to improvethe building operation

• usage of the internet: data transfer, data visualisation,user survey, communication, project documentation

goals

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content



method 3

results 4

conclusions 5

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A B C D E F G H I

year of construction 1998 1996 1996 1995 1996 1997

1970 restoration &

extension 2000 1997 1998

utilisation10-12 h/d

24 h/dadministrationcontrol rooms

administrationcontrol rooms

garage


garage





administration

garage

administration

garage

number of floors 7 6 9 9 6 6 6 16 6

number of basement floors thereof 1 1 2 1 1 0 1 2 2

gross volume [m³] 113.454 78.239 96.245 101.473 76.019 105.312 93.188 77.885 26.579

gross floor area [m²] 27.806 27.321 28.008 27.371 21.817 27.776 23.986 31.439 7.681

heated net floor area [m²] 24.647 20.962 19.349 23.128 18.251 24.304 21.134 20.883 4.551

number of working places 2001 750 718 666 649 692 869 590 735 150

analysed buildings DB Netz AG

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characteristics of the chosen buildings:

• all buildings built between 1995 and 2000

• leased office buildings(identical guidelines for building operation)

• high technical building equipment, partlyairconditioned and 24 h operation time(high electricity demand)

good comparability in age, size and use

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content



method 3

results 4

conclusions 5

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A B C D E F G H I

detailed analysis

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A C E G I

benchmarking

and selection

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1 2 3 4 5 6 7 8 9 10

optimisation

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A C E G I

rough analysis

benchmarking

and next selection

nextdetailed analysis

cooling

ventilation

lighting

server

user equipment

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data acquisition

• electricity meters

• heat meters

• gas meters

• water meters

• meters counting operating hours and starts

weekly readings

temporary measurements

• logger recordings of indoor climate data

• measurements of electricity consumptions

• measurements of illuminances

regular readings of the meters which are already installed inthe buildings - recorded by the buildings‘ technicians

short-time measurements on selected components of thetechnical equipment - by employees of the university

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A B C D E F G H IZähler insgesamt 18 45 45 16 47 17 15 33 35davon Unterzähler 14 40 30 13 41 13 13 28 30

davon Unterzähler Strom (ohne Blindstrom) 1 0 6 1 17 0 0 4 1davon einzelne Gebäudeteile Küche Küche

Kantine Kantine KantineTiefgarage VTs Tiefgarage

davon Unterzähler Kalt- oder Warmwasser 7 8 0 8 8 3 4 4 7davon einzelne Gebäudeteile Küche Küche Küche Küche Küche Küche

Büros BürosBetriebszentrale

Dach/Atrium Atrium DachAußenanlagen Außenanlage Außenanlage

davon Verbrauch für Anlagen Gesamtgebäude Warmwasser Warmwasser Warmwasser WarmwasserKälte Kälte

HeizungLüftung

BefeuchtenEnthärtung Enthärtung Enthärtung

davon Unterzähler Wärmemengen 6 20 14 0 5 2 7 2 15davon einzelne Gebäudeteile Küche Küche Küche Küche Küche

Kantine Kantine KantineFoyer Foyer

Leitwarte Leitwarte Büros/ Konferenz Büros Büro Büros/ KonferenzTechnik

Gewerbe davon Verbrauch für Anlagen Gesamtgebäude Heizung Heizung Heizung Heizung

Warmwasser Warmwasser WarmwasserKälte

davon Unterzähler Betriebsstunden/ -starts 0 12 4 4 8 8 2 12 7davon Verbrauch für Anlagen Gesamtgebäude Kälte Kälte Kälte Kälte Kälte Kälte Kälte Kälte

BefeuchtenLüftung

existing meters in the buildings

• number of meters 16 - 47 per building

• unequal distribution of meters in the buildings:

good distribution bad distribution - hot and cold water - electricity - consumption in staff cantine/ kitchen

• additional 13 -40 meters for monitoring the consumption of various building parts or technical equipment

• main meters for electricity, heat, gas, water

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GebäudedatenFlächenArbeitsplätze

ZählerdatenZählernummerBezeichnungMengeneinheitZählerortUmrechnungsfaktoren

ZählerständeOriginal-Zählerstände aller er-fassten Zähler im Gebäudezum Ablesetermin

VerbräucheVerbräuche aller Zähler für denAblesezeitraumG

ebäu

de 1





ebäu

de 1

Online-Eingabeformulare (HTML / PHP)

Plausibilitätskontrolle (Perl)





ebäu

de 1





ebäu

de 1

Datenbank (MySQL)





ebäu

de 1




VerbräucheVerbräuche aller Zähler für denAblesezeitraum

Geb

äude

1

Negative VerbräucheSchreibfehlerZählerwechselGroße Verbrauchsänderungen (dynamisch)

Passwortgeschütze, individuelle Eingabeformulare für jedes Gebäude

Berechnung von Flächen- und Arbeitsplatz-spezifischen Verbräuchen für Gebäude-vergleiche etc. (Software: Perl, Grace)

Automatische Generierung von Protokollseitenmit Diagrammen der Verbrauchswerte der letzenMonate für die Gebäudemanager und als Mail anfbta-Mitarbeiter. (Software: Perl, Grace)

Erstellung von Webseiten mit z.T. anonymisierten Diagrammen zur Darstellung von Ergebnissen.Teilweise automatisierte Aufbereitung der Daten durch Perl-Skripte für manuelle Auswertung mit diverser Software.

Datenverarbeitung

Zusammengefasste spezifische Verbräuche für: Wärme Strom Wasser

Ene

rgie

Verarbeitung der Daten

weekly meter readings

data management

data processing and visualisation via internet

online input form HTML/ PHP)

plausibility check (Perl)

data base (MySQL)

data processing

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relationship between users' response and building operation ?

survey on work space quality with questionaire via intranet

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content



method 3

results 4

conclusions 5

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182

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87 90

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112 12

1

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A B C D E F G H I arithm. Mittel

spec

ific

year

ly e

nerg

y en

d co

nsum

ptio

n [k

Wh/

m²y

]

specific electricity energy consumption

specific heating energy consumption

buildings with additional technical usage (control room) without

arithmetic mean

consumption related to the heated net floor area; data measured one full year: 02-2001 to 02-2002

specific yearly energy consumption of electricity and heat

arith. mean

limit for heating energy demand: 95 kWh/m²y *for buildings with an average building compactness of 0,95 [building envelope/volume]

* according to the German building code of 1995

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0

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F-01 M-01 A-01 M-01 J-01 J-01 A-01 S-01 O-01 N-01 D-01 J-02 F-02

spec

ific

daily

ele

ctric

ity c

onsu

mpt

ion

[Wh

/ m² d

]

building A

building B

building C

building D

building E

building F

building G

building H

building I

timeline

factor of 4,6

consumption related to the heated net floor area; data measured weekly for one full year: 02-2001 to 02-2002

mean daily specific electricity consumption

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F-01 M-01 A-01 M-01 J-01 J-01 A-01 S-01 O-01 N-01 D-01 J-02 F-02

spec

ific

daily

hea

t con

sum

ptio

n [W

h / m

² d]

building A

building B

building C

building D

building E

building F

building G

building H

building I

consumption related to the heated net floor area; data measured weekly for onefull year: 02-2001 to 02-2002; not temperature-adjusted

mean daily specific heat consumption

building A: 200 Wh / m²d in summer

--> 8 Watt heating power per m² !

--> building B in winter needs only 50% more heatingenergy consumption than building A in summer

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547

479

751

430

545

174

467

262

270

436

158

367

192

254

109

44

108

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161

112

121 17

8

138

133

108

63

0

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900

A B C D E F G H I arith.Mittel DBHamm

CH 99 ages 99

spec

ific

prim

ary

ener

gy c

onsu

mpt

ion

[kW

h/m

²y]

specific primary heat consumption

specific primary electricity consumption

buildings with additional technical usage ( t l )

without

consumption related to the heated net floor area; data measured one full year: 02-2001 to 02-2002

specific yearly primary energy consumption of electricity and heat

ref.1 ref.3

reference 1: low-energy officebuilding DB Netz AG Hamm

(research project Solarbau: Monitor)

reference 2: mean value of 100registered office buildings of astudy in Switzerland in 1999

reference 3: arithmetic mean valueof a recent study in Germany of

about 1600 office buildings

references 1, 2 and 3arith. mean

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801

588

795

538

704

335

579

383

448

574

9,3

7,9

10,0

9,9

6,8

8,1

2,3

8,2

7,6 7,9

0

100

200

300

400

500

600

700

800

900

A B C D E F G H I arith.Mittel

spec

ific

prim

ary

ener

gy c

onsu

mpt

ion

[kW

h/m

² y]

0,0

2,0

4,0

6,0

8,0

10,0

12,0

14,0

16,0

18,0

spec

ific

wat

er c

onsu

mpt

ion

[ l /

wor

k pl

ace

and

year

]

specific primary energy consumption(electricity and heat)

specific water consumption

buildings with additional technical usage (control room) without

energy consumption related to the heated net floor area, water consumption per work place; data measured onefull year 02-2001 to 02-2002

specific yearly primary energy and specific yearly water consumption

arith. mean

saving potential of the 3 buildings with highest consumption:

yearly primary energy 580 kWh/m²y

yearly operation costs 400 000 €(assuming 7,5 €-Cent for electricity, 3 €-Cent for heat)

A EC F

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content



method 3

results 4

conclusions 5

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web-based method to generate specific energyconsumption data for the evaluation and optimisationof office building operation

conclusions:• fast generation of reliable specific building data (energy, water, areas)

• no investment costs for data acquisition or specialised software packages

• high automatisation: web-based monitoring and data management via data base

• optimisation targets: technical equipment, building operation and user behaviour

goal: application on large building stocks

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contact: University of Karlsruhe

• [email protected]• www.fbta.uni-karlsruhe.de

internet www.enerkenn.de(in german language only)

web-based method to generate specific energyconsumption data for evaluation and optimisationof office building operation

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ESL-IC-04-10-29


web-based method to generate specific energy consumption ... · Proceedings of the Fourth...

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