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Estimating End-use Demand: a Bayesian Approach

Author

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  • BAUWENS, L.
  • FIEBIG, D. G.
  • STEEL, M. F. J.

Abstract

Eliminating negative end-use or appliance consumption estimates and incorporating direct metering information into the process of generating these estimates, these are two important aspects of conditional demand analysis (CDA) that will be the focus of this paper. In both cases a Bayesian approach seems a natural way of proceeding. What needs to be invistigated is whether it is aslo a viable and effective approach. In addition, such a framework naturally lends itself to prediction. Our application involves the estimation of electrical appliance consumptions for a sample of Australian households. This application is designed to illustrate the viability of a full Bayesian analysis of the problem.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Bauwens, L. & Fiebig, D. G. & Steel, M. F. J., 1994. "Estimating End-use Demand: a Bayesian Approach," LIDAM Reprints CORE 1090, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1090
    Note: In : Journal of Business and Economic Statistics, 12, (2) 221-231, 1994
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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. 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).
    4. Bodil M. Larsen & Runa Nesbakken, 2003. "How to quantify household electricity end-use consumption," Discussion Papers 346, Statistics Norway, Research Department.
    5. 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.
    6. Zhang, Fan, 2011. "Distributional impact analysis of the energy price reform in Turkey," Policy Research Working Paper Series 5831, The World Bank.
    7. Fan Zhang, 2015. "Energy Price Reform and Household Welfare: The Case of Turkey," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    8. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    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. 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).
    12. 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.
    13. Brencic, Vera & Young, Denise, 2009. "Time-saving innovations, time allocation, and energy use: Evidence from Canadian households," Ecological Economics, Elsevier, vol. 68(11), pages 2859-2867, September.
    14. Brabec, Marek & Konár, Ondrej & Pelikán, Emil & Malý, Marek, 2008. "A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers," International Journal of Forecasting, Elsevier, vol. 24(4), pages 659-678.
    15. 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.
    16. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.

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