Abstract
In this paper two methods are presented for analyzing load survey data. They are applied to data collected over a two-year period by Kansas City Power & Light (KCPL) for evaluation of the effectiveness of local control on reducing peak residential air-conditioning demand. The first method is the Wilcoxon rank-sum statistical approach, a very powerful method for comparing small data sets typical of those encountered in this load study. The second method is a simulation technique wherein the effect of local air-conditioner control on peak demand is estimated from measured uncontrolled demand data and the operating characteristics of the control device. The results obtained from both the methods for the KCPL study reported here are found to be close to one another.
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