Abstract
Accurate electricity forecasting has become a very important research field for high-efficiency electricity production. But the hybrid data-driven models for load forecasting 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 estimation. 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.
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Dong, Jr., Zheng, Cy., Kan, Gy. et al. Applying the ensemble artificial neural network-based hybrid data-driven model to daily total load forecasting. Neural Comput & Applic 26, 603–611 (2015). https://doi.org/10.1007/s00521-014-1727-5
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DOI: https://doi.org/10.1007/s00521-014-1727-5