​

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

​

​

期刊简介

​

​

EPSR，ELSEIVR旗下期刊，电力系统研究是发表有关电能产生、传输、分配和利用的原始论文的国际媒介。该杂志旨在介绍这一领域的重要工作成果，不论是以应用研究的形式、新程序或器件的开发，还是对现有知识或新设计方法的初步应用。该期刊收录范围广泛，涵盖电力系统的各个方面。JCR分区：Q2，中科院分区：3区，影响因子：3.414

​

​

1.Highlights

​

​

• 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.

​

​

2.Abstract

​

​

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.

​

​

3.Keywords

​

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