Table of Contents
International Scholarly Research Notices
Volume 2014 (2014), Article ID 972580, 16 pages
Research Article

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

1Waterloo CFD Engineering Consulting Inc., Waterloo, ON, Canada N2T 2N7
2Department of Mechanical & Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada N2L 3G1
3Defence Research and Development Canada, Suffield Research Centre, P.O. Box 4000, Stn Main, Medicine Hat, AB, Canada T1A 8K6
4School of Renewable Energy, North China Electric Power University, Beijing 102206, China

Received 27 March 2014; Revised 28 May 2014; Accepted 28 May 2014; Published 8 September 2014

Academic Editor: Ka-Veng Yuen

Copyright © 2014. Her Majesty the Queen in Right of Canada. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.