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• W3200743803 abstract "There are many factors that affect short-term load forecasting performance, such as weather and holidays. However, most of the existing load forecasting models lack more detailed considerations for some special days. In this paper, the applicability of the bagged regression trees (BRT) model combined with eight variables is investigated to forecast short-term load in Qingdao. The comparative experiments show that the accuracy and speed of forecasting have some improvements using the BRT than the artificial neural network (ANN). Then, an indicator variable is newly proposed to capture the abnormal information during special days, which include national statutory holidays, bridging days, and proximity days. The BRT model combined with this indicator variable is tested on the load series measured in 2018. Experiments demonstrate that the improved model generates more accurate predictive results than BRT model combined with previously variables on special days." @default.
• W3200743803 created "2021-09-27" @default.
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• W3200743803 date "2021-09-15" @default.
• W3200743803 modified "2023-10-13" @default.
• W3200743803 title "The Short-Term Load Forecasting for Special Days Based on Bagged Regression Trees in Qingdao, China" @default.
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• W3200743803 doi "https://doi.org/10.1155/2021/3693294" @default.
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