Abstract
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.
Recommended Citation
G. K. Venayagamoorthy et al., "Comparison of Feedforward and Feedback Neural Network Architectures for Short Term Wind Speed Prediction," Proceedings of the International Joint Conference on Neural Networks, 2009. IJCNN 2009, Institute of Electrical and Electronics Engineers (IEEE), Jun 2009.
The definitive version is available at https://doi.org/10.1109/IJCNN.2009.5179034
Meeting Name
International Joint Conference on Neural Networks, 2009. IJCNN 2009
Department(s)
Electrical and Computer Engineering
Sponsor(s)
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
text
Language(s)
English
Rights
© 2009 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jun 2009
