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- W3150872569 abstract "Accurate electricity forecasting has become a very important research field for high-efficiency electricity production. But the hybrid data-driven models for load fore- casting are rarely studied. This paper presents a novel hybrid data-driven ''PEK'' model for predicting the daily total load. The proposed hybrid model is mainly constructed by various function approximators, which containing the partial mutual information (PMI)-based input variable selection (IVS), ensemble artificial neural network-based output estimation and K-nearest neighbor regression-based output error esti- mation. The PMI-based IVS algorithm is used to select the input variables, resulting in a good compromise between the parsimony and adequacy of the input information. After that, the topology and parameter calibration of the PEK model are implemented by the NSGA-II multi-objective optimization algorithm. The electricity load demands from years 2010 to 2012 of the Shuyang hydrothermal station are chosen as a case study to verify the performance of the PEK model. Simulation results show that this model obtains significantly better accuracy in the prediction of daily total load." @default.
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- W3150872569 date "2015-01-01" @default.
- W3150872569 modified "2023-09-24" @default.
- W3150872569 title "Applying the ensemble artificial neural network-based hybrid data-driven model to daily total load forecasting" @default.
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