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  • 标题:Performance Comparison of Short Term Load Forecasting Techniques
  • 本地全文:下载
  • 作者:Kumar Reddy Cheepati ; T. Nageswara Prasad
  • 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
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