This paper compares three types of neural networks trained using particle swarm optimization (PSO) for use in the short term prediction of wind speed. The three types of neural networks compared are the multi-layer perceptron (MLP) neural network, Elman recurrent neural network, and simultaneous recurrent neural network (SRN). Each network is trained and tested using meteorological data of one week measured at the National Renewable Energy Laboratory National Wind Technology Center near Boulder, CO. Results show that while the recurrent neural networks outperform the MLP in the best and average case with a lower overall mean squared error, the MLP performance is comparable. The better performance of the feedback architectures is also shown using the mean absolute relative error. While the SRN performance is superior, the increase in required training time for the SRN over the other networks may be a constraint, depending on the application.

Meeting Name

International Joint Conference on Neural Networks, 2009. IJCNN 2009


Electrical and Computer Engineering


National Science Foundation (U.S.)
United States. Department of Education

Keywords and Phrases

Feedback Architecture; Neural Networks; Short Term Prediction; Wind Speed Prediction

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2009 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jun 2009