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 Bayesia n framework is proposed to combine end uses monitoring information wit h the aggregate-load/appliance data to allow load researchers to deriv e more accurate load shapes. Two variants are suggested: The first one uses the raw end-use metered data to construct the prior means and va riances. The second method uses actual end-use data to construct the p riors of the parameters characterizing the behavior of end uses of spe cific appliances. From a prediction perspective, the Bayesian methods consistently outperform the predictions generated from conventional co nditional-demand formulation.