Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (12): 3117-3128.

### Short-Term Load Forecasting based on Variational Mode Decomposition and Least Squares Support Vector Machine by Improved Artificial Fish Swarm-Shuffled Frog Jump Algorithms

Haizhu Yanga, Zhaoyang Jianga,*, Menglong Lia, and Peng Zhangb

1. aSchool of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China;
bSchool of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
• Submitted on  ;  Revised on  ; Accepted on
• Contact: * E-mail address: zhaoyangjiang1994@163.com
• About author:Haizhu Yang received the Ph.D. from Beijing Jiaotong University in 2005. He is an associate professor working in the School of Electrical Engineering and Automation, Henan Polytechnic University. His research interests include power electronics and electrical drive and power systems and automation.Zhaoyang Jiang is currently pursuing the M.S degree in the School of Electrical Engineering and Automation, Henan Polytechnic University. His research interests include power system and automation and power load forecasting.Menglong Li is currently pursuing the M.S degree in the School of Electrical Engineering and Automation, Henan Polytechnic University. His research interest includes power system and automation.Peng Zhang is a lecturer working in the School of Electrical and Information Engineering, Tianjin University. His research interest includes Integrated energy system planning and operation.

Abstract: Short-term load forecasting plays a key role in the safe dispatching and economic operation of the power system. The lease square support vector machine (LSSVM) has the power system. The least square support vector machine (LSSVM) has great potential in forecasting problems, particularly by employing an appropriate algorithm to determine the values of its two parameters. In order to improve LSSVM load prediction accuracy, this paper proposes a LSSVM based on the Variational mode decomposition(VMD) electric load forecasting model that uses an artificial fish swarm-shuffled frog leaping algorithm to determine the appropriate values of the two parameters. The historical data such as load and weather in the first 15 days of the forecast day are the input into LSSVM. The AFSA-SFLA-LSSVM forecasting model, the LAVAFSA-SFLA-LSSVM forecasting model, the AFSA-LSSVM forecasting model, and the VMD-LAVAFSA-SFLA-LSSVM forecasting model were established for electrical load forecasting in a certain area within 24 hours of a specific day. The results of the example show that the accuracy of the VMD-LAVAFSA-SFLA-LSSVM forecasting model was higher than the other three forecasting models and the prediction error was smaller as well.