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

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.

Automated summary

This paper comprehensively summarizes the load forecasting based on artificial intelligence models, including data processing, forecasting methods, and the selection of artificial intelligence models. The future research trends are also discussed.