关键词: 煤炭消费量, 最小二乘支持向量机, 粒子群优化算法, 鲶鱼效应

Abstract: The coal consumption has time-varying and nonlinear characteristics. In order to improve the prediction accuracy of coal consumption, a coal consumption prediction model based on Catfish Particle Swarm algorithm and Least Squares Support Vector Machine（CEPSO-LSSVM） is proposed. LSSVM parameter is encoded into the position of the particle, and minimum of the cross validation error of network training set is taken as optimal target, and then the parameters of LSSVM are obtained by the exchange information among particles, and “catfish effect” is introduced to keep the diversity of particle swarm to overcome the local optimum of the traditional particle swarm optimization algorithm, and coal consumption prediction model is built according to the optimum parameters, and the simulation test is carried out on actual coal consumption data. The results show that, compared with other prediction models, the proposed model can get better parameters, and coal consumption prediction accuracy can be improved. It is more suitable for complex coal consumption prediction.

Key words: coal consumption, Least Squares Support Vector Machine（LSSVM）, Particle Swarm Optimization（PSO） algorithm, catfish effect