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

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.