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A Bayesian Integration of End-Use Metering and Conditional-Demand Analysis

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  • Hsiao, Cheng
  • Mountain, Dean C
  • Illman, Kathleen Ho

Abstract

Traditional methods of estimating kilowatt end uses load profiles may face very serious multicollinearity issues. In this article, a Bayesian framework is proposed to combine end uses monitoring information with the aggregate-load/appliance data to allow load researchers to derive more accurate load shapes. Two variants are suggested: the first one uses the raw end-use metered data to construct the prior means and variances; the second method uses actual end-use data to construct the priors of the parameters characterizing the behavior of end uses of specific appliances. From a prediction perspective, the Bayesian methods consistently outperform the predictions generated from conventional conditional-demand formulation.

Suggested Citation

  • 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.
    • Handle: RePEc:bes:jnlbes:v:13:y:1995:i:3:p:315-26
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