
Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM
Short-term wind speed prediction is of importance for power grids. It can mitigate the disadvantageous impacts of wind farms on power systems and enhance the competitiveness of wind power in electricity markets. A short-term wind speed prediction model is proposed. Many useless neurons
of incremental extreme learning machine have little influences on the final output, at the same time, reduce the efficiency of the algorithm. In order to solve this problem, based on error minimized extreme learning machine, an improved particle swarm optimization algorithm is proposed to
decrease the number of useless neurons, achieve the goal of reducing the network complexity and improving the efficiency of the algorithm. The stability and convergence of the algorithm are proved. The actual short-term wind speed time series is used as the research object. Multistep prediction
simulation of short-term wind speed is performed out. Compared with the other prediction models, the simulation results show that the prediction model proposed in this paper reduces the training time of the model and decreases the number of hidden layer nodes. The prediction model has higher
prediction accuracy and reliability, meanwhile improve the prediction performance indicators.
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Keywords: Error minimized extreme learning machine; improved particle swarm optimization algorithm; incremental extreme learning machine; prediction; short-term wind speed
Document Type: Research Article
Affiliations: College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China
Publication date: January 2, 2019