Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. this paper, a hybrid method, composed kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed short-term load forecasting. Specifically, randomly generated weights biases ELM have significant impact stability prediction results. Therefore, in order to solve problem, LTSA utilized obtain optimal parameters before process executed by ELM, which called LTSA-ELM. Meanwhile, input data extracted KPCA considering sparseness electric used as LTSA-ELM model. The method tested from European network intelligent technologies (EUNITE) experimental results demonstrate superiority approaches compared other methods involved paper.