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How can an increased technical energy efficiency lead to increased energy consumption? Answers from an in-depth metering

• f the electricity demand in 400

Swedish households

Peter Bennich Carlos Lopes Egil Öfverholm (corresponding author) Zinaida Kadic The Swedish energy agency

Contents

• Background
• Methodology

– Measurements – Socio-economic data – Behaviour studies

• Results
• Discussions

Purpose

• A better resolution of the energy statistics is needed:

– To improve the statistics and prognoses of the energy use – As a basis when discussing and analysing policy instruments for increased energy efficiency

• 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?

Malmö Kiruna Stockholm + Region Lake Mälardalen

• Domestic end use in 200

detached houses and 200 apartments.

• Geografic spread limited to

lake Mälardalen, plus some referense objects in Kiruna and Malmö

Selection of households

Basic information

• Enquiries filled in by the households in combination with

inspection done by the installers

• House or apartment
• Type of heating system
• Locus type: city, small city; country side
• Family structure:

– Number of people – Age – Gender

• Income
• Distribution of apparatus (including lights):

– Type and model – Placement in the different rooms

Measurements

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

Measurements (cont)

• Measure as much as possible at the switch board (especially 3

phase installations), including total incoming electricity

• All other appliances were measured with a serial power meter

connected at the outlets

• Lamps were measured with light sensors. Nominal power was

written down.

• We also measured ventilation, water heating, circulation pumps

and heating (direct, water, heat pumps) whenever possible

• Temperature inside and outside was 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…

Additional studies

• Water measurements in 10 households at tap level (1 – 10 min

data, one month)

• Water measurements in ca 40 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, a report published soon)

• Heat contribution from appliances and lighting (lab study) (not

started)

Load curves: detailed information on user patterns

Aggregated results (preliminary)

Houses, all households [kWh/yr] Apartments, all households [kWh/yr] Fridge and freezers 790 720 Lighting 950 630 Cooking 390 390 Dish washers 220 120 Wash and dry 300 210 AudioVisual 430 270 PC and related eq. 410 270 Others 540 60 Not measured 130 330 Sum 4160 3000

But the spread is large: ex lighting

1994 -> 2008: Decreased consumption?

Comparison between Statistics Sweden and the measurements, for houses.

Comfort heating explains the missing part

Observations

Measured data actually suggests decreased domestic electricity use in houses. (Apartments: not that big difference.) This was not catched by the enquires of Statistics Sweden A redistribution of the loads has occured:

• Lighting is the largest load: 1994 it was second
• Cold appliances comes second: 1994 it was the largest
• Entertainment electronics (TV, PC etc) comes on third place: has

increased a lot since 1994!

• The use of comfort heating is increasing as well

Explanations

Combination of the technical development and the change in behaviour:

• Cold appliances: increase in energy efficiency
• Entertainment electronics:
• Random efficiency (pre-ecodesign era)
• Increased (individual) use (explained elsewhere)
• Comfort heating is an example of ”new” appliances

that are added

Discussion

The data collected are of three kinds:

• Enquiry based socio-economic data
• Measured data
• Behaviour (anecdotic?) data

All three are important to understand the trends and rationalities behind the domestic use. Important to find cost-effective but yet reliable combinations of methods yilding this type of data in the future

Some considerations

Choice of methodology:

• Enquires

Pro: large nr of households; reasonable size of datasets, statistically sound, cheap, easy to administrate Con: sometimes wrong answers. E.g: possession and use of light sources; use of TV, white goods etc

• Measurements:

Pro: objective data (in principle), time resolved data Con: small nr of households, time-consuming, expensive

• Behaviour studies:

Pro: catch anecdotic information; give deeper insights to the rationality behind the use of appliances Con: even smaller nr of households, time-consuming Seasonnality effects can play an important role – is not straight forward to go from monthly to annual data

Some considerations (cont)

• Difficult to do proper statistical analyses, especially when scaling

up to national (or international) level: In Sweden there are roughly

• 2.4 millions apartments
• 1.8 millions detached houses

No information how to relate that to the distribution of household sizes (next slide)

• Use of measures can be tricky and hide trends:
• Total consumtion (national level, all households) [kWh/yr]
• Normalisation regarding to
• household [kWh/yr, hh]
• surface area [kWh/yr, m2]
• nr of persons [kWh/yr, person]

Change of the household composition over time

[x 1000 persons] [Year]

Finally

All data will be stored in a database, public available The final report from Enertech, France, is soon ready Other analyses will be performed later Check our website for more information: www.energimyndigheten.se

Extra… Different user patterns

• 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).

Main observations

The interplay between household members is crucial:

• Competition and/or negotiation of common resources
• Tendency from communal use to individual use
• Home electronics: solved by adding resources (all must have their
• wn set of PC, broadband, TV, stereo etc.)
• Cooking: solved by more and more serial cooking instead of

common cooking The electricity use increase even more...

• Implies increased use of electricity – but it depends also on

the technology used.