A Hybrid Forecasting Model for Electricity Demand in Sustainable Power Systems Based on Support Vector Machine

Xuejun Li, Minghua Jiang, Deyu Cai (), Wenqin Song and Yalu Sun
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Xuejun Li: State Grid Gansu Electric Power Co., Ltd., Jinan 730030, China
Minghua Jiang: State Grid Gansu Electric Power Co., Ltd., Jinan 730030, China
Deyu Cai: School of Electrical Engineering, Shandong University, Jinan 250061, China
Wenqin Song: State Grid Gansu Electric Power Company Economic and Technological Research Institute Co., Ltd., Jinan 730050, China
Yalu Sun: State Grid Gansu Electric Power Company Economic and Technological Research Institute Co., Ltd., Jinan 730050, China

Energies, 2024, vol. 17, issue 17, 1-16

Abstract: Renewable energy sources, such as wind and solar power, are increasingly contributing to electricity systems. Participants in the energy market need to understand the future electricity demand in order to plan their purchasing and selling strategies. To forecast the electricity demand, this study proposes a hybrid forecasting model. The method uses Kalman filtering to eliminate noise from the electricity demand series. After decomposing the electricity demand using an empirical model, a support vector machine optimized by a genetic algorithm is employed for prediction. The performance of the proposed forecasting model was evaluated using actual electricity demand data from the Australian energy market. The simulation results indicate that the proposed model has the best forecasting capability, with a mean absolute percentage error of 0.25%. Accuracy improved by 74% compared to the Support Vector Machine (SVM) electricity demand forecasting model, by 73% when compared to the SVM with empirical mode decomposition, and by 51% when compared to the SVM with Kalman filtering for noise reduction. Additionally, compared to existing forecasting methods, this study’s accuracy surpasses LSTM by 63%, Transformer by 47%, and LSTM-Adaboost by 36%. The simulation of and comparison with existing forecasting methods validate the effectiveness of the proposed hybrid forecasting model, demonstrating its superior predictive capabilities.

Keywords: sustainable power system; genetic algorithm; electricity demand forecasting; Kalman filtering; support vector machine (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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