Affiliations: [a]
School of Information, Shenyang Institute of Engineering, Shenyang, China
| [b]
Shenyang Key Laboratory of Energy Internet Intelligent Perception and Security Technology, Shenyang, China
Correspondence:
[*]
Corresponding author. Yan Xia, School of Information, Shenyang Institute of Engineering, Shenyang, China. E-mail: xiayan@sie.edu.cn.
Abstract: Power system load forecasting is a method that uses historical load data to predict electricity load data for a future time period. Aiming at the problems of general prediction accuracy and slow prediction speed in using typical machine learning methods, an improved fuzzy support vector regression machine method is proposed for power load forecasting. In this method, the boundary vector extraction technique is employed in the design of the membership function for fuzzy support vectors to differentiate the importance of different samples in the regression process. This method utilizes a membership function based on boundary vectors to assign differential weights to different sample points that used to differentiate the importance of different types of samples in the regression analysis process in order to improve the accuracy of electricity load prediction. The key parameters of the fuzzy support vector regression model are optimized, further enhancing the precision of the forecasting results. Simulation experiments are conducted using real power load data sets, and the experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and speed in predicting power load data compared to other prediction models. This method can be widely applied in real power production and scheduling processes.
Keywords: Machine learning, fuzzy support vector regressive machine, power load prediction, membership function, boundary vector
