Artificial bee colony algorithm optimized error minimized extreme learning machine and its application in short-term wind speed prediction

Zhongda Tian, Gang Wang, Yi Ren, Shujiang Li, Yanhong Wang

Abstract


Accurate short-term wind speed prediction has important theoretical significance and practical application for wind power industry. In order to improve the prediction accuracy of short-term wind speed, a short-term wind speed prediction model based on artificial bee colony algorithm optimized error minimized extreme learning machine model is proposed. The extreme learning machine have the advantages of fast learning speed and strong generalization ability. As a kind of improved extreme learning machine, incremental extreme learning machine has been applied in many applications because it does not have the optimal hidden layer nodes and the over fitting problem. But many useless neurons of incremental extreme learning machine has little influences on the final output, at the same time, reduce the efficiency of the algorithm. The optimal parameters of the hidden layer nodes will make network output error of incremental extreme learning machine decrease with fast speed. If the optimal parameters are obtained, this means that it can greatly improve the learning speed of algorithm. It is very necessary to optimize the parameters of the hidden layer nodes. As an intelligent optimization algorithm, artificial bee colony algorithm has highly competitive, which is very suitable for the optimization of the parameters of the hidden layer nodes. Based on the error minimized extreme learning machine, artificial bee colony algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons, reduce training and prediction error, achieve the goal of reducing the network complexity and improve the efficiency of the algorithm. The error minimized extreme learning machine prediction model is constructed with the obtained optimal parameters. The stability and convergence property of artificial bee colony algorithm optimized error minimized extreme learning machine model are proved. The practical short-term wind speed time series is used as the research object and verify the validity of the prediction model. Multi step prediction simulation of short-term wind speed is carried out in simulations. Compared with other prediction models, simulation results show that the prediction model proposed in this paper reduce the training time of the prediction model, decrease the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.


Keywords


extreme learning machine; artificial bee colony algorithm; short-term wind speed; prediction



DOI: http://dx.doi.org/10.14311/NNW.2018.%25x

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