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Integrating Direct Metering and Conditional Demand Analysis for Estimating End-Use Loads

Author

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  • Bartels, R.
  • Fiebig, D.

    (Tilburg University, School of Economics and Management)

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.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Bartels, R. & Fiebig, D., 1990. "Integrating Direct Metering and Conditional Demand Analysis for Estimating End-Use Loads," Other publications TiSEM eeac5253-bbf9-4ae2-bf14-e, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:eeac5253-bbf9-4ae2-bf14-e27a7a884b4d
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    Cited by:

    1. Robert Bartels & Denzil G. Fiebig & Michael H. Plumb, 1996. "Gas or Electricity, which is Cheaper? An Econometric Approach with Application to Australian Expenditure Data," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 33-58.
    2. Bodil M. Larsen & Runa Nesbakken, 2003. "How to quantify household electricity end-use consumption," Discussion Papers 346, Statistics Norway, Research Department.
    3. 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.
    4. Shigeru Matsumoto, 2015. "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Proceedings of International Academic Conferences 3105452, International Institute of Social and Economic Sciences.
    5. Hannah Goozee, 2017. "Energy, poverty and development: a primer for the Sustainable Development Goals," Working Papers 156, International Policy Centre for Inclusive Growth.
    6. 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).
    7. 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).
    8. Hannah Goozee, 2017. "Energy, Poverty and Development: A Primer for the Sustainable Development Goals," Working Papers id:11933, eSocialSciences.
    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, Australia 125050, Australian Agricultural and Resource Economics Society.
    11. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    12. Mattias Vesterberg & Chandra Kiran B. Krishnamurthy, 2016. "Residential End-use Electricity Demand: Implications for Real Time Pricing in Sweden," The Energy Journal, , vol. 37(4), pages 141-164, October.
    13. James B. McDonald & Richard A. Michelfelder & Panayiotis Theodossiou, 2009. "Robust Regression Estimation Methods and Intercept Bias: A Capital Asset Pricing Model Application," Multinational Finance Journal, Multinational Finance Journal, vol. 13(3-4), pages 293-321, September.
    14. 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.
    15. Papineau, Maya & Yassin, Kareman & Newsham, Guy & Brice, Sarah, 2021. "Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    16. 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.
    17. 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.
    18. Hanne Marit Dalen & Bodil M. Larsen, 2013. "Residential end-use electricity demand. Development over time," Discussion Papers 736, Statistics Norway, Research Department.
    19. Robert Bartels & Denzil G. Fiebig, 2000. "Residential End-Use Electricity Demand: Results from a Designed Experiment," The Energy Journal, , vol. 21(2), pages 51-81, April.
    20. 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.
    21. 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.
    22. Robert Bartels & Denzil G. Fiebig & Daehoon Nahm, 1996. "Regional End‐Use Gas Demand in Australia," The Economic Record, The Economic Society of Australia, vol. 72(219), pages 319-331, December.
    23. 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.

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