Mid-long term load forecasting model based on support vector machine optimized by improved sparrow search algorithm
Jinghua Li, Yongsheng Lei, Shuhui Yang
Abstract
The regression model based on the support vector machine (SVM) is a standard mid-term and long-term load forecasting method. However, the hyperparameters of traditional SVM are challenging to determine, leading to its poor forecasting effect. This paper proposes an improved sparrow search algorithm (ISSA) to solve the problem of hyperparameter selection of the SVM model and construct the mid-long term load prediction model by ISSA-SVM. This ISSA is enhanced by a new dynamic adaptive t-distribution mutation. Compared to the SSA, the ISSA has better convergence precision, stability and speed, which was verified by the comparative test based on six benchmark functions. The simulation results also show that the ISSA-SVM can effectively improve the prediction accuracy compared with the original SVM, BP neural network, multiple linear regression, etc.