Demand forecasting in power distribution systems using nonparametric probability density estimation

Abstract

Customer demand data are required by power flow programs to accurately simulate the behavior of electric distribution systems. At present, economic constraints limit widespread customer monitoring, resulting in a need to forecast these demands for distribution system analysis. This paper presents the application of nonparametric probability density estimation to the problem of customer demand forecasting using information readily available at most utilities. The method utilizes demand survey information, including weather conditions, to build a probabilistic demand model that expresses both the random nature of demand and its temperature dependence. The paper describes a procedure for developing such a model and its application for demand forecasting based on customer energy usage and outside temperature.