Short-Term Residential Load Forecasting Based on the Fusion of Customer Load Uncertainty Feature Extraction and Meteorological Factors

Wenzhi Cao, Houdun Liu, Xiangzhi Zhang, Yangyan Zeng () and Xiao Ling
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Wenzhi Cao: School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China
Houdun Liu: School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China
Xiangzhi Zhang: School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China
Yangyan Zeng: School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China
Xiao Ling: State Grid Hunan Electric Power Co., Ltd. Information and Communication Branch, Changsha 410205, China

Sustainability, 2025, vol. 17, issue 3, 1-21

Abstract: With the proliferation of distributed energy resources, advanced metering infrastructure, and advanced communication technologies, the grid is transforming into a flexible, intelligent, and collaborative system. Short-term electric load forecasting for individual residential customers is playing an increasingly important role in the operation and planning of the future grid. Predicting the electrical load of individual households is more challenging with higher uncertainty and volatility at the household level compared to the total electrical load at the feeder and regional levels. The previous research results show that the accuracy of forecasting using machine learning and a single deep learning model is far from adequate and there is still room for improvement.

Keywords: residential load forecasting; subsequence partitioning; feature extraction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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