Demand forecasting in power distribution systems using nonparametric probability density estimation
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.
- Research Organization:
- Univ. of Texas, Arlington, TX (US)
- OSTI ID:
- 20013157
- Journal Information:
- IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers), Vol. 14, Issue 4; Other Information: PBD: Nov 1999; ISSN 0885-8950
- Country of Publication:
- United States
- Language:
- English
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