Load forecasting with support vector regression: influence of data normalization on grid search algorithm

Abstract

In recent years, support vector regression (SVR) models have been widely applied in short-term electricity load forecasting. A critical challenge when applying the SVR model is to determine the model for optimal hyperparameters, which can be solved using several optimization methods as the grid search algorithm. Another challenge that affects the response time and the precision of the SVR model is the normalization process of input data. In this paper, the grid search algorithm will be suggested based on data normalization methods including Z-score, min-max, max, decimal, sigmoidal, softmax, and then utilized to evaluate both the response time and precision. To verify the proposed methods, the actual electricity load demand data of two cities, including Queensland of Australia and Ho Chi Minh City of Vietnam, were utilized in this study.