Support Vector Regression based on Grid Search method of Hyperparameters for Load Forecasting

Support Vector Regression is becoming one of the most attractive models for load forecasting, in recent years.The performance of Support Vector Regression deeply depends on its hyperparameters, such as, Kernel function, Kernel function parameters and a penalty factor.This paper proposes a methodology for the Grid Search hyperparameters of the Support Vector Regression model.In the training process, the optimal hyperparameters will specify conditions that satisfy requirements for minimizing evaluation indexes of Root Mean Square Error, Mean Absolute Percentage Error, Symmetric Mean Absolute Percentage Error and Mean Absolute Error.In the testing process, the optimal models will be used to evaluate the obtained results along with all other ones.It is indicated that the evaluation indexes of these optimal models are close to the minimum values of all models.Load demand data of Tasmania State, Australia, and Ho Chi Minh City, Vietnam were utilized to verify the accuracy and reliability of the Grid Search methodology.