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Conditional Demand Analysis for Estimating Residential End-Use Load Profiles

Dennis J. Aigner, Cynts Sorooshian, and Pamela Kerwin

Year: 1984
Volume: Volume 5
Number: Number 3
DOI: 10.5547/ISSN0195-6574-EJ-Vol5-No3-6
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Abstract:
This paper reports some preliminary results from an ongoing study that uses regression methods to break down total household load into its constituent parts, each associated with a particular electricity-using end use or appliance. The data base used for this purpose consists of 15-minute integrated demand readings on a random sample of statistical control group customers from the Los Angeles Department of Water and Power TOD (time of day)-pricing experiment for the months of August 1978 (132 customers), 1979 (108 customers), and 1980 (80 customers). Twenty-four regression equations are fitted, each one aimed at explaining variation in the time-averaged load (averaged over days of the month) over customers as a function of temperature, house size, and binary indicator variables that indicate the presence or absence of each of the end uses of interest. This sort of method for extracting the individual contributions of end uses to total household load has become known as conditional demand analysis (Parti and Parti, 1981). The success of this method for isolating end-use loads statistically, without direct metering of the appliance, depends crucially on whether the ownership patterns of appliances are well mixed. For example, if (as in our sample) everyone owns at least one refrigerator, it will be impossible to isolate refrigerator load. Similarly,



Integrating Direct Metering and Conditional Demand Analysis for Estimating End-Use Loads

Robert Bartels and Denzil G. Fiebig

Year: 1990
Volume: Volume 11
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol11-No4-5
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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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