期刊名称：International Journal of Electronics and Computer Science Engineering
电子版ISSN：2277-1956
出版年度：2012
卷号：1
期号：3
页码：992-999
出版社：Buldanshahr : IJECSE
摘要：Artificial Neural Network (ANN) Method is applied to fore cast the short-term load for a large power system. A nonlinear load model is proposed and several structures of ANN for short term forecasting are tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers is tested with various combinations of neurons, and the results are compared in terms of forecasting error. To demonstrate the effectiveness of the proposed approach, publicly available data from the State load Dispatch Centre, Airoli, Navi Mumbai's web site has been taken to forecast the hourly load. We predicted the hourly load demand with a high degree of accuracy. Historical load data was divided into two parts where half of them are used for training and the other half is used for testing the ANN. Learning methods such as artificial neural networks, and more recently, support vector regression machines have been introduced to this field. In practices we often expect a fast forecasting, while standard algorithms based on the whole data set are time consuming. An absolute mean error of 2.26% was achieved when the trained network was tested on data
