Skip to main content
padlock icon - secure page this page is secure

Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM

Buy Article:

$60.00 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

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

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more