Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (12): 67-74.doi: 10.16180/j.cnki.issn1007-7820.2020.12.013

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Parameters Selection for LSSVM Based on Artificial Fish Swarm-Shuffled Frog Jump Algorithms Optimization in Short-Term Load Forecasting

YANG Haizhu,JIANG Zhaoyang,LI Menglong,KANG Le   

  1. School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454003,China
  • Received:2019-09-06 Online:2020-12-15 Published:2020-12-22
  • Supported by:
    National Natural Science Foundation of China(61703144);The Department of Education Project of Guizhou Province(KY[2015]468)


Short-term load forecasting plays a key role in safe dispatching and economic operation of power system.The parameters of the LSSVM directly affect the prediction effect during the load forecasting accuracy. In order to improve LSSVM load prediction accuracy, a method based on levy adaptive vision artificial fish swarm-shuffled frog leaping algorithm for parameter optimization of LSSVM is proposed. LSSVM is trained by historical data such as load and weather in a certain area. The LAFSA-SFLA-LSSVM forecasting model, the LAVAFSA-SFLA-LSSVM forecasting model and the AFSA-LSSVM forecasting model are established for power load forecasting in a certain area within 24 hours of a certain day. The results show that the accuracy of the LAVAFSA-SFLA-LSSVM forecasting model is higher than the AFSA-LSSVM forecasting model and the LAFSA-SFLA-LSSVM forecasting model, and the prediction error is smaller.

Key words: short-term load forecasting, power system scheduling, prediction accuracy, least squares support vector machine, improve artificial fish swarm-shuffled frog leaping algorithm, optimization parameter

CLC Number: 

  • TP13