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实验室侯慧老师文章被Electric Power Systems Research(EPSR)期刊收录

已有 698 次阅读 2023-4-14 09:03 |系统分类:博客资讯

Review of load forecasting based on artificial intelligence methodologies, models, and challenges

Hui Houa,b, Chao Liua,b, Qing Wanga,b, Xixiu Wua,b, Jinrui Tanga,b, Ying Shia,b, Changjun Xiea,b

a School of Automation, Wuhan University of Technology, Wuhan, China
b Shenzhen Research Institute, Wuhan University of Technology, Shenzhen, China




• Methodologies and models of load forecasting are reviewed comprehensively.
• Provide insights of data processing method on different time series.
• Reveal differences on point prediction, interval and probability prediction.
• Comparative study of load forecasting based on AI models is provided.
• Discussion on development trend of load forecasting in the future is prospected.


Accurate load forecasting can efficiently reduce the day-ahead dispatch stress of power system or microgrid. The overview of load forecasting based on artificial intelligence models are comprehensively summarized in this paper. As the steps of load forecasting based on artificial intelligence model mainly include data processing, setting up forecasting strategy and model forecasting, the paper firstly reviewed the data processing studies. According to the kinds of data obtained, the data can be classified into two categories: multivariate time series and single variate time series. Secondly the forecasting methodologies including one-step forecasting and rolling forecasting are summarized and compared. In addition, according to the form of the prediction results, point prediction, interval prediction and probability prediction are summarized. Thirdly, the paper reviews the artificial intelligence models used in load forecasting. In light of the application scenarios, it can be classified into single model and combination model. Finally, we also discussed the future trend for the research, such as fuzzy reasoning, intelligent optimization in forecasting, novel machine learning and transfer learning technologies, etc.


Load forecasting; Artificial intelligence; Phase space reconstruction; Combination model



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