A Bayesian Approach to Combining Conditional Demand and Engineering Models of Electricity Usage

Load forecasting models employed in the electric utility industry have become increas ingly dependent upon information about the electricity used by indivi dual appliances (i.e., end uses). Currently, information on appliance usage is obtained from two fundamentally different sources: (1) engi neering estimates and (2) conditional demand estimates. Bayesian anal ysis provides the means by which these two sources can be formally co mbined. Observed usage data (via the conditional demand approach) are used to modify a set of prior beliefs (the engineering approach), transforming them into a posterior distribution that describes appliance usage patterns and reflects the evidence provided by both approaches. Coauthors are Joseph A. Herriges, Kenneth E. Train, and Robert J. Windle.