Abstract
Conditional demand analysis (CDA) is a statistical method for allocating the total household electricity load, during a period, into its constituent components, each associated with a particular electricity-using appliance or end-use. This is an indirect approach to the estimation of end-use demand and, quite naturally, it often generates imprecise estimates. One of the possible methods for improving these estimates involves the incorporation of data obtained by directly metering specific appliances. It is argued that an extremely natural approach to the use of this extra information follows directly from a reformulation of the standard CDA model into a random coefficient framework. Some new results on the possible efficiency gains from such an approach are developed. Illustrations based on an empirical study of New South Wales (NSW) households are also provided.
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