Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm

ABSTRACT: Short-term power load forecasting is essential in ensuring the safe operation of power systems and a prerequisite in building automated power systems. Short-term power load demonstrates substantial volatility because of the effect of various factors, such as temperature and weather conditions. However, the traditional short-term power load forecasting method ignores the influence of various factors on the load and presents problems of limited nonlinear mapping ability and weak generalization ability to unknown data. Therefore, a short-term power load forecasting method based on GRA and ABC-SVM is proposed in this study. First, the Pearson correlation coefficient method is used to select critical influencing factors. Second, the gray relational analysis (GRA) method is utilized to screen si

SUBMITTER: Pang X 

PROVIDER: S-EPMC9575856 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature