Research and application of a combined model based on multi-objective optimization for electrical load forecasting

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• 作者：Liye Xiao^a ; Wei Shao^a ; ^{weishao@uestc.edu.cn} ; Mengxia Yu^a ; Jing Ma^b ; Congjun Jin^b
• 关键词：Multi-objective flower pollination algorithm ; Short-term load forecasting ; Combined model ; Forecasting accuracy and stability ; Weight coefficient optimization
• 刊名：Energy
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：119
• 期：Complete
• 页码：1057-1074
• 全文大小：5639 K
• 卷排序：119

文摘

Simultaneously improve the accuracy and stability of electrical load forecasting. Propose a combined model based on several artificial neural networks. Optimize weight coefficients of single models based on a multi-objective optimization algorithm. Use the multiple seasonal patterns and the fast ensemble empirical mode decomposition to pre-process data.