title: Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.
abstract: 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.
authors.affiliation: Waterloo CFD Engineering Consulting Inc., Waterloo, ON, Canada N2T 2N7; Department of Mechanical & Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada N2L 3G1.
authors.forename: Zhongxian
authors.identifier: 0000-0003-4355-7100
authors.initials: Z
authors.lastname: Men
authors.affiliation: Department of Mechanical & Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada N2L 3G1; Defence Research and Development Canada, Suffield Research Centre, P.O. Box 4000, Stn Main, Medicine Hat, AB, Canada T1A 8K6.
authors.forename: Eugene
authors.identifier: 0000-0002-8934-9457
authors.initials: E
authors.lastname: Yee
authors.affiliation: Waterloo CFD Engineering Consulting Inc., Waterloo, ON, Canada N2T 2N7; Department of Mechanical & Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada N2L 3G1.
authors.forename: Fue-Sang
authors.initials: FS
authors.lastname: Lien
authors.affiliation: School of Renewable Energy, North China Electric Power University, Beijing 102206, China.
authors.forename: Zhiling
authors.initials: Z
authors.lastname: Yang
authors.affiliation: School of Renewable Energy, North China Electric Power University, Beijing 102206, China.
authors.forename: Yongqian
authors.initials: Y
authors.lastname: Liu