基于鲸鱼优化参数的最小二乘支持向量机短期负荷预测方法

doi:10.12052/gdutxb.190099

Abstract

Abstract: With the emergence of a large number of distributed energy stations and the popularity of electric vehicles, the security and economic operation of the power system are affected. At the same time, the traditional load forecasting methods are also facing challenges. To solve this problem, a Whale Optimization Algorithm-Least Squares Support Vector Machine (WOA-LSSVM) is proposed for short-term power system load forecasting. Using the advantages of whale algorithm such as strong global optimization ability and fast convergence speed, the blindness of parameter selection of LS-SVM is overcome, and the load forecasting accuracy of LS-SVM is improved. WOA-LSSVM is used to forecast the load of a certain area in Zhejiang province in the next day based on the historical load data in 2013. The results are compared with those of the particle swarm optimization least squares support vector machine model and the standard LSSVM model. The results show that the short-term load forecasting based on the whale optimization LSSVM has a higher forecasting accuracy and a smaller relative error.

Key words: short-term load forecasting, least squares support vector machine, whale optimization algorithm

CLC Number:

TM734

Chen You-peng, Chen Jing-hua. A Short-term Load Forecasting Method Based on Support Vector Machine with Whale Optimization Parameters[J].Journal of Guangdong University of Technology, 2020, 37(03): 75-81.

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