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/lp/sage/artificial-bee-colony-algorithm-optimized-error-minimized-extreme-d1Am0SCdwW
- Publisher
- SAGE Publications
- Copyright
- © The Author(s) 2018
- ISSN
- 0309-524X
- eISSN
- 2048-402X
- D.O.I.
- 10.1177/0309524X18780401
- Publisher site
- See Article on Publisher Site
Abstract
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 has the advantages of fast learning speed and strong generalization ability. But many useless neurons of incremental extreme learning machine have little influences on the final output and, 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. 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 to verify the validity of the prediction model. Multi-step prediction simulation of short-term wind speed is carried out. Compared with other prediction models, simulation results show that the prediction model proposed in this article reduces the training time of the prediction model and decreases the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability performance, meanwhile improves the performance indicators.
Journal
Wind Engineering – SAGE
Published: Jun 1, 2018
@Phil_Robichaud
@deepthiw
@JoseServera