How To Calculate Energy Savings In Buildings

Transcription

1 Use of energy statistics and surveys to set up indicators for energy efficient buildings UNEP SBCI Workshop October 2009, Singapore IEA, International Energy Agency, Jens Laustsen Senior Policy Analyst for Efficiency in Buildings

2 Examples on indicators Making a top down. Combining top down and bottom up. Some examples of advanced energy indicators.

3 General statistics Household Energy Use per Capita IEA, Energy Use in the New Millennium, 2007

4 Modelling on energy efficient buildings Development in the 9 regions Rest of World Hot Sanatory Water Cooling Ventilation Heating

5 Basis These are estimated based on IEA statistics Energy use in residential and commercial buildings in different energy types Oil, gas, district heating, renewable energy, electricity Different assumptions on development and building area, construction, etc. Values for kwh/m², kwh per person are calculated Could be done for each individual country If statistics such as requested are collected

6 Combining top down and bottom up!

7 Gebäudetüpen * Germany EFH Baualtersklassen vor vor Summe Anteil German Building Stock % A B C D E F G H I J Wohnfläche 81, , , , , , , , , ,208 1,739, Anz Wohnungen 916 1,707 2,010 1,915 2,274 1, ,055 1, , RH Wohnfläche 14,543 31,450 21,993 35,996 61,478 24,503 32,951 33,366 11, , Anz Wohnungen , MFH Wohnfläche 31, , , , , ,930 61, , ,740 24,267 1,025, Anz Wohnungen 462 1,501 2,034 1,912 2,210 1, ,712 2, , GMH Wohnfläche 31,549 10,160 38,936 47,501 46, , Anz Wohnungen , HH Wohnfläche 12,617 12,988 25, Anz Wohnungen MFH NBL Wohnfläche 14,324 24,418 38, Anz Wohnungen GMH NBL Wohnfläche 22,976 19,899 17,977 60, Anz Wohnungen , HH NBL Wohnfläche 16,823 4,230 21, Anz Wohnungen Wohnfläche 113, , , , , , , , , ,150 Anteil Anz Wohnungen 1,378 3,801 4,539 5,090 6,222 5,656 2,362 4,353 4,519 1,050 Anteil *) EFH = Einfamilienhaus, RH = Reihenhaus, MFH = Mehrfamilienhaus, GMH = großes Mehrfamilienhaus, HH = Hochhaus, NBL = neue Bundesländer 3,353,118 38,970 Source IWU Südwest and IWU, Germany Building typology..

8 German Building Stock Source: IWU Südwest and IWU, Energy Efficiency in Building Stock.

9 Examples on Savings Source: IWU Südwest and IWU, Energy Efficiency in Building Stock.

10 Case Germany Large data collection was done on building stock (typology) Many individual / typical buildings were assessed Both existing state and possible savings were assessed. Commercial buildings more complicated

11 Existing buildings in Denmark Enfam. Række % Stueh. Etage. U < 0,2 0,2 <= U < 0,3 0,3 <= U < 0,4 0,4 <= U < 0,5 0,5 <= U < 0,6 0,6 <= U < 0,7 0,7 <= U < 0,8 0,8 <= U < 0,9 0,9 <= U < 1,0 1,0 <= U < 1,1 1,1 <= U < 1,2 1,2 <= U < 1,3 1,3 <= U < 1,4 1,4 <= U < 1,5 1,5 <= U < 1,6 1,6 <= U < 1,7 1,7 <= U < 1,8 1,8 <= U < 1,9 1,9 <= U < 2,0 2,0 <= U < 2,3 2,3 <= U < 2,6 2,6 <= U < 3, Before 1930 Enfam. Række Stueh. Etage Enfam. One family Række Row house Stueh. Farms Etage. Multifamily U values in roof construction today ( ) dependent on the year of construction and building type Enfam. Række Stueh. Etage ,5 <= U < 0,6 0,6 <= U < 0,7 0,7 <= U < 0,8 0,8 <= U < 0,9 0,9 <= U < 1,0 1,0 <= U < 1,1 1,1 <= U < 1,2 1,2 <= U < 1,3 1,3 <= U < 1,4 1,4 <= U < 1,5 1,5 <= U < 1,6 1,6 <= U < 1,7 1,7 <= U < 1,8 1,8 <= U < 1,9 1,9 <= U < 2,0 2,0 <= U < 2,3 2,3 <= U < 2,6 2,6 <= U < 3,1 0,5 <= U < 0,6 0,6 <= U < 0,7 0,7 <= U < 0,8 0,8 <= U < 0,9 0,9 <= U < 1,0 1,0 <= U < 1,1 1,1 <= U < 1,2 1,2 <= U < 1,3 1,3 <= U < 1,4 1,4 <= U < 1,5 1,5 <= U < 1,6 1,6 <= U < 1,7 1,7 <= U < 1,8 1,8 <= U < 1,9 1,9 <= U < 2,0 2,0 <= U < 2,3 2,3 <= U < 2,6 2,6 <= U < 3,1 0,4 <= U < 0,5 0,4 <= U < 0,5 0,3 <= U < 0,4 0,3 <= U < 0,4 0,2 <= U < 0,3 0,2 <= U < 0,3 U < 0,2 U < 0, Enfam. Række Stueh. Etage Enfam. Række 5 0 U < 0,2 0,2 <= U < 0,3 0,3 <= U < 0,4 0,4 <= U < 0,5 0,5 <= U < 0,6 0,6 <= U < 0,7 0,7 <= U < 0,8 0,8 <= U < 0,9 0,9 <= U < 1,0 1,0 <= U < 1,1 1,1 <= U < 1,2 1,2 <= U < 1,3 1,3 <= U < 1,4 1,4 <= U < 1,5 1,5 <= U < 1,6 1,6 <= U < 1,7 1,7 <= U < 1,8 1,8 <= U < 1,9 1,9 <= U < 2,0 2,0 <= U < 2,3 2,3 <= U < 2,6 2,6 <= U < 3,1 % % % % Stueh. Etage Enfam. Række 10 0 % Stueh. Etage. Source: Energy Savings in Existing Buildings (Heating), DBUR 2003 U < 0,2 0,2 <= U < 0,3 0,3 <= U < 0,4 0,4 <= U < 0,5 0,5 <= U < 0,6 0,6 <= U < 0,7 0,7 <= U < 0,8 0,8 <= U < 0,9 0,9 <= U < 1,0 1,0 <= U < 1,1 1,1 <= U < 1,2 1,2 <= U < 1,3 1,3 <= U < 1,4 1,4 <= U < 1,5 1,5 <= U < 1,6 1,6 <= U < 1,7 1,7 <= U < 1,8 1,8 <= U < 1,9 1,9 <= U < 2,0 2,0 <= U < 2,3 2,3 <= U < 2,6 2,6 <= U < 3,1

12 Example on u-value profile Tag 1,00 0,90 0, Before 1950 Tag 1,00 0,90 0, ,70 0,70 Ventilation 0,60 0,50 0,40 0,30 0,20 0,10 0,00 Gulv+fundament Parcelhuse,EM Rækkehuse,EM Ventilation 0,60 0,50 0,40 0,30 0,20 0,10 0,00 Gulv+fundament Parcelhuse,EM Rækkehuse,EM Stuehuse,EM Stuehuse,EM Etageboliger,EM Etageboliger,EM Vinduer Ydervægge Ventilation Vinduer Tag 1,00 0,90 0,80 0,70 0,60 0,50 0,40 0,30 Ydervægge Gulv+fundament Ventilation Tag 1,00 0,90 0,80 0,70 0,60 0,50 0,40 0, Gulv+fundament 0,20 0,20 0,10 0,10 0,00 0,00 Parcelhuse,EM Parcelhuse,EM Rækkehuse,EM Rækkehuse,EM Stuehuse,EM Stuehuse,EM Etageboliger,EM Etageboliger,EM Vinduer Ydervægge Vinduer Ydervægge Source: Energy Savings in Existing Buildings (Heating), DBUR 2003

13 Energy profile existing buildings GJ per 100 m² per year 90,0 80,0 70,0 60,0 50,0 40,0 30,0 20,0 10,0 0,0 Loss of energy Before Year of construction Supply of energy Roof External walls Floor foundation Windows Ventilation Hot Water Energy profile for buildings based on year of construction calculated per 100 m² for small residential buildings Calculated based on energy certification of 200,000 buildings GJ per 100 m² per year 90,0 80,0 70,0 60,0 50,0 40,0 30,0 20,0 10,0 0,0 Before Year of construction Sun Internal heat gains Heating Source: Energy Savings in Existing Buildings (Heating), DBUR 2003 Source: Energy Savings in Existing Buildings (Heating), DBUR 2003

14 Heating saving potentials existing buildings in Denmark TJ Detached houses Terraced houses Farm houses Blocks of flats Source: Energy Savings in Existing Buildings (Heating), DBUR 2003

15 Case Denmark In the case Denmark this was possible because. A large central register on all buildings and apartments (BBR) Includes information on each single building and apartment Including size, type, year of construction, improvement, number of toilets, type of materials, heating system / form Data from energy certification was collected systematically since 1997 Includes u-values, areas, windows, efficiency figures Includes calculation results (all buildings calculated) and metered consumptions Proposals for savings, costs estimated by consultants More than 100,000 datasets was used

16 Similar modeling can be used in smaller scale Existing data resources should be used to a maximum Level of detail could be different in each country, depending on possibilities Is in both cases an excellent tool for setting up policies for energy efficiency in building stock

17 Setting energy demands New buildings max energy consumption Also for existing buildings

18 Energy performance for new buildings Maximum demands for new buildings based on energy performance kwh per m² per year

19 Better than new! Energy standard refurbishment kwh7(m²a) Germany Demands refurbishment Demands new Minus 30 % Minus 50 % Primary losses Losses in system Hot sanitory water Energy demand 0 Existing buildings EnEV Sanierung EvEV New Buildings EnEV minus 30% EnEV minus 50% Passive house Factor 10 Very Best practice Renovation in Germany Source: DENA Besser als ein Neubau

20 Better than new! High-rise: Changing the View IEA / EuroAce 2006 Energy standard refurbishment 300 Germany 250 Demands refurbishment kwh7(m²a) 200 Demands new Primary losses Losses in system 150 Very Best practice Renovation in Germany Factor 10 Hot sanitory water Minus 30 % Energy demand St. Petersburg, Russia 100 Minus 50 % Factor ho us e 50 % P as si ve in us m E ne V E ne V m in us 30 % B ui ld in gs un g N ew S an ie r E ve V E ne V E xi st in g bu ild in gs 0 Very Best practice Renovation in Germany 28 kwh/m² 28 kwh/m² 22 kwh/m² 44 kwh/m² 21 kwh/m² 35 kwh/m² - 91 % - 88 % - 89 % - 83 % - 96 % - 90 % Source: DENA Besser als ein Neubau

21 A multi step approach! A picture of buildings can be built up Top Down or Bottom Up Depends on the actual state of country or jurisdiction. Will also depend of type of building and purpose! Picture will be more and more detailed ,000 10, ,000 1,000,000 A few values in kwh / CO2 in average.. Different values residential / commercial. Types, year of construction and use.. Detailed information on end use etc..... Information on some general buildings Information on all important types. All types and accurate typology. You Crawl -you walk -you run and meet!

22 Sustainable Buildings Network The G8 plus G5 five countries have decided to set up a Sustainable Buildings network. IEA supports this work. Will be a networks of networks working to bridge gap between networks and policy makers. Biennial conference with G8+G5 high level / G20? Global scope - work with internet based information. Provide best practice examples / recommendations. Work for coordination and increased collaboration. Capacity buildings and information share. Policy advice based on technical knowledge. Working groups on specific topics between networks. Could maybe support such a framework.

23 SBN Structure IPEEC Policy Committee AnotherIPEEC Task Group SBN Network AnotherIPEEC Task Group SBN Advisory Board SBN Members, networks, int ernational organisations, industry SBN Members, networks, international organisations, industry SBN subtask IPEEC Executive Committee SBN Steering Committee SBN subtask SBN Members only (IPEEC Members that decide to join SBN) SBN subtask SBN subtask SBN Secretariat (by the IEA)

24 Conclusion Both a bottom up and a top down approach is needed Top down can start with few uncertain values for few variables Bottom up can start with few samples and uncertain typology By development the data size grows and typology improves 1 Top Down 5 Model Typology 1, Top Down Model ,000 Bottom Up Model Same Typology 1,000,000 Need same level of description Use existing structures Not too detailed now if you ask me! You crawl, you walk, you run

25 Conclusions from Paris In Paris we already decided: To start with a few values based on residential and commercial To increase towards different types (step 2) To increase typology, include age and construction type (step 3) To use the Köppen Classification for climate To collect information on m², persons and units To use a matrix: Year of construction and different types would vary by country and different steps