Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model. (November 2022)

• Record Type: Journal Article
• Authors: Deng, Xuzhi; Ye, Aoshuang; Zhong, Jiashi; Xu, Dong; Yang, Wangwang; Song, Zhaofang; Zhang, Zitong; Guo, Jing; Wang, Tao; Tian, Yifan; Pan, Hongguang; Zhang, Zhijing; Wang, Hui; Wu, Chen; Shao, Jiajia; Chen, Xiaoyi
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 8661
• Page End: 8674
• Publication Date: 2022-11
• Subjects: Ensemble learning -- Extreme weather identification -- Mutual information -- Short-term load forecasting -- XGBoost; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.06.072
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Accurate short-term load forecasting of distribution transformer in extreme weather will effectively assist power dispatching and enable safe and stable operation of power grid. Therefore, this paper proposes the Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model, which can warn the time period and detailed value of peak load in advance. Firstly, based on Extreme Gradient Boosting (XGBoost) algorithm, the idea of Bagging is introduced to reduce the output variance and enhance the generalization ability of the algorithm. Then, the mutual information (MI) between weather influencing factors and load is analyzed to adjust the input weight of the model and improve its ability to track weather changes. Next, considering the load, weather and time factors, the extreme weather identification model is established to determine the occurrence range of peak load. Finally, the specialized training set is selected based on the weighted similarity, and high-accuracy short-term load forecasting model is established. Compared with the traditional model, the model proposed in this paper reduces the average Mean Absolute Percentage Error (MAPE) of peak load by 3% to 10%.