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How to quantify household electricity end-use consumption

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Information about total electricity consumption is available for most households. However, the electricity consumption related to different end uses, e.g. space heating, water heating, lighting and services from household appliances are usually not metered. Metering data are very costly to achieve, and in this paper we study two methods for end-use estimation, which can be applied on household data for appliance holdings, demographic and economic variables. The first method is the engineering model which has been used to calculate the so far only documented Norwegian end-use results applied on data from a Norwegian energy survey. The second method is an econometric conditional demand model applied on data from the same survey. We compare the numerical results from the two models and give some recommendations regarding choice of end-use approach and what questions to implement in household surveys designed to disaggregate electricity consumption.

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  • Bodil M. Larsen & Runa Nesbakken, 2003. "How to quantify household electricity end-use consumption," Discussion Papers 346, Statistics Norway, Research Department.
  • Handle: RePEc:ssb:dispap:346
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    1. Hsiao, Cheng & Mountain, Dean C & Illman, Kathleen Ho, 1995. "A Bayesian Integration of End-Use Metering and Conditional-Demand Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 315-326, July.
    2. Robert Bartels & G. Fiebig, 1990. "Integrating Direct Metering and Conditional Demand Analysis for Estimating End-Use Loads," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 79-98.
    3. Bauwens, Luc & Fiebig, Denzil G & Steel, Mark F J, 1994. "Estimating End-Use Demand: A Bayesian Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 221-231, April.
    4. Dennis J. Aigner & Cynts Sorooshian & Pamela Kerwin, 1984. "Conditional Demand Analysis for Estimating Residential End-Use Load Profiles," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 81-98.
    5. Robert Bartels & Denzil G. Fiebig, 2000. "Residential End-Use Electricity Demand: Results from a Designed Experiment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 51-81.
    6. Sanchez, Marla C & Koomey, Jonathan G & Moezzi, Mithra M & Meier, Alan & Huber, Wolfgang, 1998. "Miscellaneous electricity in US homes: Historical decomposition and future trends," Energy Policy, Elsevier, vol. 26(8), pages 585-593, July.
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    More about this item

    Keywords

    Electricity end-use consumption; econometric conditional demand model; engineering model.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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