In this paper,a particle swarm optimization extreme learning machine(PSO-ELM)algorithm is proposed to predict the load of the heat substation,and the input weight and hidden layer threshold of the extreme learning machine(ELM)are optimized by the particle swarm optimization(PSO)algorithm.The combined algorithm is applied to the load prediction of a residential heat substation in Tianjin,and compared with ELM,support vector regression(SVR)and particle swarm optimization support vector regression(PSO-SVR)under the same conditions.The results show that the PSO-ELM has better prediction accuracy than other algorithms.When the heating load fluctuation is large,its performance is better than the PSO-SVR.In a certain range,the sample size has little effect on the prediction results,and the PSO-ELM can forget more data.
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