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

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Listed:
  • Hsiao, C.
  • Mountain, D.C.
  • Ho, C.F.

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

Suggested Citation

  • Hsiao, C. & Mountain, D.C. & Ho, C.F., 1994. "A Bayesian Integration of End-Use Metering and Conditional Demand Analysis," Papers 9411, Southern California - Department of Economics.
    • Handle: RePEc:fth:socaec:9411
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    Keywords

    econometrics;

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