Short-term power load forecasting based on support vector machine and particle swarm optimization. (October 2018)

• Record Type: Journal Article
• Authors: Qiang, Song; Pu, Yang
• Is Part Of: Journal of algorithms & computational technology. Volume 13(2019)
• Publication Date: 2018-10
• Subjects: Short-term load forecasting -- support vector machines -- least squares support vector machine -- particle swarm optimization -- parameter selection; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301818797061
• Languages: English
• ISSNs: 1748-3018

Abstract

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the "abnormal data" in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.