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Integrating Direct Metering And Conditional Demand Analysis Fr Estimating End-Use Loads

Author

Listed:
  • BARTELS, R.
  • FIEBIG, D.G.

Abstract

Conditional demand analysis (CDA) is a statistical method for allocating the total household electricity load during a period, into its constituent components, each associated with a particular electricity-using appliance or end-use. This is an indirect approach to the estimation of end-use demand and, quite naturally, it often generates imprecise estimates. One of the possible methods for improving these estimates involves the incorporation of data obtained by directly metering specific appliances. It is argued that an extremely natural approach to the use of this extra information follows directly from a reformulation of the standard CDA model into a random coefficient framework Some new results on the possible efficiency gains from such an approach are developed. Illustrations based on an empirical study of New South Wales (NSW) households are also provided.
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Suggested Citation

  • Bartels, R. & Fiebig, D.G., 1990. "Integrating Direct Metering And Conditional Demand Analysis Fr Estimating End-Use Loads," Papers 9056, Tilburg - Center for Economic Research.
  • Handle: RePEc:fth:tilbur:9056
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    Cited by:

    1. Hanne Marit Dalen & Bodil M. Larsen, 2013. "Residential end-use electricity demand. Development over time," Discussion Papers 736, Statistics Norway, Research Department.
    2. Hannah Goozee, 2017. "Energy, Poverty and Development: A Primer for the Sustainable Development Goals," Working Papers id:11933, eSocialSciences.
    3. Bodil M. Larsen & Runa Nesbakken, 2003. "How to quantify household electricity end-use consumption," Discussion Papers 346, Statistics Norway, Research Department.
    4. Bartels, Robert & Fiebig, Denzil G., 1995. "Optimal design in end-use metering experiments," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 39(3), pages 305-309.
    5. Shigeru Matsumoto, "undated". "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Working Papers e98, Tokyo Center for Economic Research.
    6. Narayan, Paresh Kumar & Smyth, Russell, 2005. "The residential demand for electricity in Australia: an application of the bounds testing approach to cointegration," Energy Policy, Elsevier, vol. 33(4), pages 467-474, March.
    7. Hannah Goozee, 2017. "Energy, poverty and development: a primer for the Sustainable Development Goals," Working Papers 156, International Policy Centre for Inclusive Growth.
    8. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    9. Muhammad Akmal & David I. Stern, 2001. "Residential energy demand in Australia: an application of dynamic OLS," Working Papers in Ecological Economics 0104, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program.
    10. Muhammad, Akmal, 2002. "The structure of consumer energy demand in Australia: an application of a dynamic almost ideal demand system," 2002 Conference (46th), February 13-15, 2002, Canberra 125050, Australian Agricultural and Resource Economics Society.
    11. Matsumoto, Shigeru, 2016. "How do household characteristics affect appliance usage? Application of conditional demand analysis to Japanese household data," Energy Policy, Elsevier, vol. 94(C), pages 214-223.
    12. Hanne Marit Dalen and Bodil M. Larsen, 2015. "Residential End-use Electricity Demand: Development over Time," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    13. Mattias Vesterberg and Chandra Kiran B. Krishnamurthy, 2016. "Residential End-use Electricity Demand: Implications for Real Time Pricing in Sweden," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    14. Muhammad Akmal & David I. Stern, 2001. "The structure of Australian residential energy demand," Working Papers in Ecological Economics 0101, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program.
    15. Beccali, M. & Cellura, M. & Lo Brano, V. & Marvuglia, A., 2008. "Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(8), pages 2040-2065, October.
    16. Larsen, Bodil Merethe & Nesbakken, Runa, 2004. "Household electricity end-use consumption: results from econometric and engineering models," Energy Economics, Elsevier, vol. 26(2), pages 179-200, March.

    More about this item

    Keywords

    estimator ; demand ; electricity;

    JEL classification:

    • F0 - International Economics - - General

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