caa a22 4500 606159479 CHVBK 20210128100625.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00521-014-1727-5 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1727-5 Applying the ensemble artificial neural network-based hybrid data-driven model to daily total load forecasting [Elektronische Daten] [Jia-rui Dong, Chui-yong Zheng, Guang-yuan Kan, Min Zhao, Jie Wen, Jing Yu] 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. The Natural Computing Applications Forum, 2014 Load forecasting nationallicence Non-updating nationallicence Ensemble artificial neural network nationallicence K -nearest neighbor algorithm nationallicence PEK nationallicence Dong Jia-rui Business School, Hohai University, 210098, Nanjing, China aut Zheng Chui-yong Business School, Hohai University, 210098, Nanjing, China aut Kan Guang-yuan College of Hydrology and Water Resources, Hohai University, 210098, Nanjing, China aut Zhao Min Business School, Hohai University, 210098, Nanjing, China aut Wen Jie BaoTou Power Supply, 014000, Baotou, China aut Yu Jing Business School, Hohai University, 210098, Nanjing, China aut Neural Computing and Applications Springer London 26/3(2015-04-01), 603-611 0941-0643 26:3<603 2015 26 521 https://doi.org/10.1007/s00521-014-1727-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1727-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Dong Jia-rui Business School, Hohai University, 210098, Nanjing, China aut NATIONALLICENCE 700 1- Zheng Chui-yong Business School, Hohai University, 210098, Nanjing, China aut NATIONALLICENCE 700 1- Kan Guang-yuan College of Hydrology and Water Resources, Hohai University, 210098, Nanjing, China aut NATIONALLICENCE 700 1- Zhao Min Business School, Hohai University, 210098, Nanjing, China aut NATIONALLICENCE 700 1- Wen Jie BaoTou Power Supply, 014000, Baotou, China aut NATIONALLICENCE 700 1- Yu Jing Business School, Hohai University, 210098, Nanjing, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/3(2015-04-01), 603-611 0941-0643 26:3<603 2015 26 521