Imperialist competitive algorithm combined with refined high-order weighted fuzzy time series (RHWFTS–ICA) for short term load forecasting
作者： Rasul Enayatifar , Hossein Javedani Sadaei , Abdul Hanan Abdullah , Abdullah Gani
DOI: 10.1016/J.ENCONMAN.2013.08.039
关键词:
摘要: Abstract In this study, a hybrid algorithm based on refined high-order weighted fuzzy and an imperialist competitive (RHWFTS–ICA) is developed. This method proposed to perform efficiently under short-term load forecasting (STLF). First, autocorrelation analysis was used recognize the order of logical relationships. Next, optimal coefficients intervals adaption were obtained by means in training dataset. Lastly, information employed forecast 48-step-ahead STLF problems. To validate method, eight case studies real data, collected from UK France during years 2003 2004, tested with certain enhanced models. The numerical results demonstrated efficiency terms accuracy.
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