Dynamically Optimizing Parameters in Support Vector Regression: An Application of Electricity Load Forecasting

This study develops a novel model, GA-SVR, for parameters optimization in support vector regression and implements this new model in a problem forecasting maximum electrical daily load. The realvalued genetic algorithm (RGA) was adapted to search the optimal parameters of support vector regression (SVR) to increase the accuracy of SVR. The proposed model was tested on a complicated electricity load forecasting competition announced on the EUNITE network. The results illustrated that the new GA-SVR model outperformed previous models. Specifically, the new GA-SVR model can successfully identify the optimal values of parameters of SVR with the lowest prediction error values, MAPE and maximum error, in electricity load forecasting.

• Type: Conference
• Year: 2006
• Where: HICSS
• Authors: Chin-Chia Hsu, Chih-Hung Wu, Shih-Chien Chen, Kang-Lin Peng
• Keywords: Biometrics, Electricity Load Forecasting, HICSS 2006, Problem Forecasting Maximum, Support Vector Regression, System Sciences