Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm

Suqi Zhang (), Ningjing Zhang, Ziqi Zhang and Ying Chen
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Suqi Zhang: School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
Ningjing Zhang: School of Science, Tianjin University of Commerce, Tianjin 300134, China
Ziqi Zhang: China Construction Second Engineering Bureau Ltd., South China Company, Shenzhen 518000, China
Ying Chen: State Grid Tianjin Marketing Service Center (Metrology Center), Tianjin 300200, China

Energies, 2022, vol. 15, issue 23, 1-17

Abstract: Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the Improved Seagull Optimization Algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the Improved Seagull Optimization Algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the Seagull Optimization Algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system.

Keywords: power load forecasting; electric management system; Support Vector Machine; Improved Seagull Optimization Algorithm; Seagull Optimization Algorithm (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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