A bayesian integration of end-use metering and conditional-demand analysis

Traditional methods of estimating kilowatt end uses load profiles may face very serious mul- ticollinearlty 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 enduse 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.