Point-Interval Forecasting for Electricity Load Based on Regular Fluctuation Component Extraction

Bilin Shao, Zixuan Yao () and Yifan Qiang
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Bilin Shao: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Zixuan Yao: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Yifan Qiang: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China

Energies, 2023, vol. 16, issue 4, 1-20

Abstract: The fluctuation and uncertainty of the electricity load bring challenges to load forecasting. Traditional point forecasting struggles to avoid errors, and pure interval forecasting may cause the problem of too wide an interval. In this paper, we combine point forecasting and interval forecasting and propose a point-interval forecasting model for electricity load based on regular fluctuation component extraction. Firstly, the variational modal decomposition is combined with the sample entropy to decompose the original load series into a strong regular fluctuation component and a weak regular fluctuation component. Then, the gate recurrent unit neural network is used for point forecasting of the strong regular fluctuation component, and the support vector quantile regression model is used for interval forecasting of the weak regular fluctuation component, and the results are accumulated to obtain the final forecasting intervals. Finally, experiments were conducted using electricity load data from two regional electricity grids in Shaanxi Province, China. The results show that combining the idea of point interval, point forecasting, and interval forecasting for components with different fluctuation regularity can effectively reduce the forecasting interval width while having high accuracy. The proposed model has higher forecasting accuracy and smaller mean interval width at various confidence levels compared to the commonly used models.

Keywords: electricity load forecasting; point-interval forecasting; regular fluctuation component; variational modal decomposition; sample entropy; gate recurrent unit; support vector quantile regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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