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

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.