期刊名称:International Journal of Grid and Distributed Computing
印刷版ISSN:2005-4262
出版年度:2016
卷号:9
期号:4
页码:287-302
DOI:10.14257/ijgdc.2016.9.4.26
出版社:SERSC
摘要:Load forecasting plays a major role in planning and operation of a power system. Many techniques are available in the literature among these neural networks, linear multiple regression, regression trees, curve fitting and averaging models are the most popular because these models gives accurate solutions with very less tolerable Least Mean Absolute Percent Error(MAPE). In this paper a comparative study was made between these forecasting models and it was found that when compared to the four independent models, the averaging model i.e. combination of Curve Fitting, Regression Trees & Neural Network gives less MAPE. MATLAB programming results validates that averaging model gives better performance than individual models.
关键词:Artificial Neural Network (ANN); Mean Absolute Percent Error(MAPE); ; Linear Multiple Regression; Regression Trees; averaging; Load Forecasting