A Combined Model for Short-term Load Forecasting Based on Bird Swarm Algorithm

Abstract

Short-term load forecasting (STLF) plays a very important role in the power system scheduling of smart grid. In this paper, a variable weight combined load forecasting model is proposed, effectively improves the accuracy of short-term load forecasting. A prediction model is presented by combining there single prediction models, i.e. random forest, extreme learning machine and Elman neural network. Then a bird swarm-based intelligent algorithm is utilized to solve the weighting problem among them. Experimental results demonstrate that the new constructed prediction model has higher prediction accuracy than any single load forecasting model.