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End-use metering in 400 Swedish households

End-use metering in 400 Swedish households. Peter Bennich. Purpose. Improved prognoses of energy use and policy instruments for increased energy efficiency requires better resolution of the energy statistics Example of questions at issue:

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End-use metering in 400 Swedish households

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  1. End-use metering in 400 Swedish households Peter Bennich

  2. Purpose • Improved prognoses of energy use and policy instruments for increased energy efficiency requires better resolution of the energy statistics • Example of questions at issue: • Why does not the domestic electricity use decrease despite the fact that e.g. white goods become increasingly more energy efficient? • How large is the contribution from standby-consumption? • What constitutes peak loads, when do they occur (the grid perspective)? • More precise: three basic questions: • How does the distribution of apparatus really look like in different types of households? • How energy efficient are these apparatus? • How does the user patterns look like?

  3. Measurements • Domestic end use in 200 detached houses and 200 apartments, plus common area of 20 residential buildings (elevators, washing room, etc • One year: 40 (evenly distributed) • One month: 360 (evenly distributed) • Geografic spread limited to lake Mälardalen, plus some referense objects in Kiruna and Malmö Kiruna Stockholm + Region Lake Mälardalen Malmö

  4. Selection of households • Selection by Statistics Sweden: 600 letters plus questionnaires per round. • Selection from the Swedish ”building register” • Answers from roughly 1 out of 4 (150 per round) • Installers contact these households directly • Not all households will be measured in the end, ca 100 • Can still use all questionnaires for mapping of certain characteristica. Total nr: more than 2000 • Additional option: water measurements as well

  5. Questionnaire information • Locus type: city, small city; country side • House or apartment • Number of rooms • Area • Type of heating system • Family structure: • Number of people • Age (note: Birth year!) • Gender • Income • Number and model of cold appliances and TV’s

  6. Additional information at installation: • Distribution of appliances per room • Distribution of lamps and lamp types per room • Nominal standby power of appliances (measurement)

  7. Additional studies: • Water measurement in 8 households at tap level (1 – 10 min data, one month) • Water measurements in (max) 50 households: incoming cold water and hot water. (10 min data, one month) • Behaviour study of lighting: interviews of 8 households • Behaviour study of the other uses: ”Cooking”, ”Entertainment”, ”Cleaning”, etc. Interviews and/or diarys; 14 households • Harmonic containts of incadescent light, CFL’s and LED’s: per lamp and per household (lab study) • Measurements and interviews of 2 households before and after replacement of lights and white goods to the best available (not started) • Heat contribution from appliances and lighting (lab study) (not started)

  8. Actual measurements Many loads (especially light sources)... Easily over 60 in a house (35 – 45 light sources)

  9. Measurements (2) Så mycket som möjligt mäts i elcentralen – den totala elförbrukningen; spis, tvättmaskin, etc.

  10. Measurements (3) Öriga apparater (TV, PC, etc) mäts genom seriekopplade mätare placerade mellan apparaten och vägguttaget.

  11. Measurements (4) Ljuskällor mäts indirekt: ljus-sensorer mäter när lamporna är av eller på. Information om motsvarande ljuskällors effekt gör det då möjligt att beräkna energin: energi = effekt*tid

  12. Measurements (5) Ifall det förekommer en blandning av fasta installationer och lösa apparater matade från samma säkring: Mät i punkterna 1, 2 och 3, då erhålls lastpunkten 4 (handdukshängaren) som differensen mellan 1 (säkringen) och lasterna (2+3).

  13. Measurements (6) • Measure as much as possible at the switch board (especially 3 phase installations), including total incoming electricity • All other appliances are measured with a serial power meter connected at the outlets • Lamps are measured with light sensors. Nominal power is written down. • We also measure ventilation, water heating, circulation pumps and heating (direct, water, heat pumps) whenever possible • Temperature inside and outside is also measured • Time resolved data, 10 min rms-average on an appliance level. I.e., load curves for invidual appliances • Goal: try to minimise the ”Not followed” part to be < 10 %. Easy for apartments, not so easy for houses…

  14. Preliminary results

  15. 7% 20% 7% 9% 11% 3% 24% 2% 9% 5% 25% 2% 2% 6% 5% 6% 10% 2% 21% 7% 4% 13% Relative distributions Cold appl Lighting Cooking Dish Apartments Wash, dry Stereo, radio TV DVD + VCR etc Houses PC site Others Not meas.

  16. Some observations: • Largespread of the results: • Houses: from 2000 to 7000 kWh/yr • Average ca 5100 +- 750 (15%) not definitive value! • Apartments: from 1000 to 5000 kWh/yr • Average ca 3000 +- 450 (15%) not definitive value! • The composition and the other socio-ekonomical factors are important • Lighting is the largest load • Cold appliances comes second • Entertainment electronics (TV, PC etc) comes on third place

  17. Some observations (2): • Increase in houses according to SCB (Statistics Sweden): • 1970: ca 4 000 kWh/yr2005: ca 6 000 kWh/yr • The absolute level of the values from 2005 may be doubted… but a redistribution has definitely occured: • Study from 1994: The domestic electric consumption in houses ca 5 000 kWh/yr • Cold appliances then largest, at least 30 % • Lighting approx as today, ca 20 % • Entertainment electronics much less, way below 20 %

  18. Is resolving of households into subcategories important? (The household perspective.) Table 2: One example of subcategories for households in houses and apartments.

  19. Resolving households into subcategories is important!

  20. Load curves: more detailed information on user patterns Apartments, all households, one year

  21. Loadcurves (2)Houses, all households, week day

  22. STEM ENERTECH Light Average hourly load curve Example:Lighting in houses Weekdays 400 350 300 250 200 150 100 Week day 50 0 [00,01[ [01,02[ [02,03[ [03,04[ [04,05[ [05,06[ [06,07[ [07,08[ [08,09[ [09,10[ [10,11[ [11,12[ [12,13[ [13,14[ [14,15[ [15,16[ [16,17[ [17,18[ [18,19[ [19,20[ [20,21[ [21,22[ [22,23[ [23,24[ Hour All houses STEM ENERTECH Light Average hourly load curve Holidays 400 350 300 Holiday 250 200 150 100 50 0 [00,01[ [01,02[ [02,03[ [03,04[ [04,05[ [05,06[ [06,07[ [07,08[ [08,09[ [09,10[ [10,11[ [11,12[ [12,13[ [13,14[ [14,15[ [15,16[ [16,17[ [17,18[ [18,19[ [19,20[ [20,21[ [21,22[ [22,23[ [23,24[ Hour

  23. Lighting (2): distribution of light sources STEM ENERTECH Light Distribution of the light sources per type of bulbs for the houses 100% Inc Halogen (trafo) Halogen (230 V) Fluor. Light strips CFL 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% All houses Houses, family w kids Houses, couple w/o kids

  24. Lighting (3): large number of light sources… Number of lamps in houses 60 50 Number of lamps in all households 40 30 20 60 10 0 50 1-20 21-40 41-60 61-80 81-100 40 30 20 10 Number of lamps in apartments 0 60 1-20 21-40 41-60 61-80 81-100 50 40 30 20 10 Installed power: average 1 – 2 kW 0 1-20 21-40 41-60 61-80 81-100

  25. STEM ENERTECH Light Average hourly load curve Weekdays 400 350 300 250 200 150 100 50 0 [00,01[ [01,02[ [02,03[ [03,04[ [04,05[ [05,06[ [06,07[ [07,08[ [08,09[ [09,10[ [10,11[ [11,12[ [12,13[ [13,14[ [14,15[ [15,16[ [16,17[ [17,18[ [18,19[ [19,20[ [20,21[ [21,22[ [22,23[ [23,24[ Hour All houses STEM ENERTECH Behaviour study of lighting: explains the data more…

  26. Old couple, a day in October 2005. Use per room:

  27. But what do they do? Outdoor lighting turns on by timer She: prepares food He: works in the cellar Breakfast w light candles Siesta?

  28. Another example: Stand-by vs operational (1) TV: Rel low standby, high operational

  29. Stand-by vs operational (2) VCR, DVD etc: Rel high standby, low op

  30. Stand-by vs operational (3) PC + related eq: Rel high standby and high op

  31. Stand-by (4). Another view: relative energy use STEM ENERTECH Energy contribution of the different consumption states per function All Houses 100% 80% 60% Contribution (%) 40% 20% 0% Audio site Computer site Cooking Dishwasher TV Visual site excl.TV Washing/Drying Standby consumption On-mode consumption

  32. Stand-by (5). Yet another view – relative time in use 6.0 h 7.5 h

  33. Number of freezers in houses

  34. Information and entertainment 4 or 5 TV 2% 3 TV 13% 1 TV 47% 2 TV 38% TV:s in the homes Trend: increasing number of apparatus and electricity use No PC 3 or more PC:s 9% 8% 2 PC 22% 1 PC 61% PC:s in the homes

  35. Why..?From ”gathering around the fire” - communal use - to parallell, individual, use

  36. In some cases… gathering around the ”cyber-fire”! Individual double use

  37. Communal use: two or more family members use an appliance together (e.g. watching TV together) Use for common goals: one member uses appliances that serves many members (e.g. cooking the family dinner) Serial use: the same appliance is used at different times by different members (e.g. the tea-kettel) Parallell use: the same type of appliances are used at the same time by different members in different places in the dwelling (e.g. TV or PC) Trend towards more individual use – add patterns like: Individual simultaneous use (e.g. cooking and listening to the radio) Individual by-turn use (e.g. alternating between TV and PC without switching off the appliance not in use for the moment) Individual double use (e.g. two or more appliances must be turned on at the same time to achieve the desired function). System of different user patterns

  38. Conclusions from the behaviour study: • The interplay between household members is crucial: • Competition and/or negotiation of common resources • Is more and more solved by adding more resources • Tendency from communal use to individual use • Ex: All must have their own set of PC, broadband, TV, stereo etc. The electricity use increase even more...

  39. Finally, methodology issues: • Optimum between data collection and statistical methods seem to be an open question… • STEM-data will be processed by researchers in order to try to scale up to national level. May be difficult… 400 is not enough for all the detailed descriptions we want to achieve (different household types– not one typical household!) • REMODECE • Nordic informal group • IEA-IA on Energy efficient end-use equipment: Annex on benchmarking • IEA workshop on Energy indicators

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