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