Demand-Side Electricity Load Forecasting Based on Time-Series Decomposition Combined with Kernel Extreme Learning Machine Improved by Sparrow Algorithm

Liyuan Sun, Yuang Lin (), Nan Pan, Qiang Fu, Liuyong Chen and Junwei Yang
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Liyuan Sun: Metrology Center, Yunnan Power Grid Co., Ltd., Kunming 650500, China
Yuang Lin: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Nan Pan: Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China
Qiang Fu: Marketing Department, Lijiang Power Supply Bureau, Ltd., Yunnan Power Grid Co., Kunming 650500, China
Liuyong Chen: Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China
Junwei Yang: Longshine Technology Group Co., Ltd., Wuxi 214000, China

Energies, 2023, vol. 16, issue 23, 1-16

Abstract: With the rapid development of new power systems, power usage stations are becoming more diverse and complex. Fine-grained management of demand-side power load has become increasingly crucial. To address the accurate load forecasting needs for various demand-side power consumption types and provide data support for load management in diverse stations, this study proposes a load sequence noise reduction method. Initially, wavelet noise reduction is performed on the multiple types of load sequences collected by the power system. Subsequently, the northern goshawk optimization is employed to optimize the parameters of variational mode decomposition, ensuring the selection of the most suitable modal decomposition parameters for different load sequences. Next, the SSA–KELM model is employed to independently predict each sub-modal component. The predicted values for each sub-modal component are then aggregated to yield short-term load prediction results. The proposed load forecasting method has been verified using actual data collected from various types of power terminals. A comparison with popular load forecasting methods demonstrates the proposed method’s higher prediction accuracy and versatility. The average prediction results of load data in industrial stations can reach RMSE = 0.0098, MAE = 0.0078, MAPE = 1.3897%, and R 2 = 0.9949. This method can be effectively applied to short-term load forecasting in multiple types of power stations, providing a reliable basis for accurate demand-side power load management and decision-making.

Keywords: load forecasting; variational mode decomposition; northern goshawk optimization algorithm; improved kernel extreme learning machine; power system load management (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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