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标题：Short Term Load Prediction of a Distribution Network based on an Artificial Intelligent Method
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作者：K.Aruna ; Dr.N.Yadaiah
期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2014
卷号：3
期号：4
页码：5514-5517
出版社：IJECS
摘要：Accurate load forecasting plays a key role in economical use of energy and real time security analysis of system.Artificial Neural Network (ANN) model have been extensively implemented to produce accurate results for short-termload forecasting with time lead ranging from an hour to a week. In this paper a practical case of the small load area of atown getting supplied by 19 distribution feeders is considered with dominant residential-type of load. Historical load andtemperature data is collected from January-2010 to December- 2010.Four weather seasons are defined by theMeteorological Department, India. Each season includes the group of month .Representative months are selected fromeach season by observing the variation in load behavior patterns. An input vector composed of load and temperaturevalues at previous instants, is employed to train ANN designed for each selected month by using Back-Propagationalgorithm with Momentum learning rule. ANN testing is carried out and their performance is evaluated using meanabsolute percentage error (MAPE) criterion. Finally, error values are compared for each month and hence the deviationin forecasting ability of ANN is observed for each month and season.
关键词：Artificial Neural Network;Back-Propagation algorithm;mean absolute percentage error