Wind Speed Time Series Predicted by Neural Network

Abstract

An important step for generation adequacy evacuation in power system planning involving wind farms is to develop an accurate wind speed model for a site. Auto-regressive Moving Average (ARMA) model is a most common approach for predicting future wind speeds. This method, however, has some drawback, for example, the probability distribution of ARMA model might follow a Normal distribution with negative wind speeds. In this paper, a neural network based approach is proposed for wind speed time series prediction, and three training algorithms, Bayesian Regularization, Levenberg Marquardt, and Scaled Conjugate Gradient, are considered. The wind speed data in St. John's, Newfoundland and Labrador, Canada, are used in the case study to validate the proposed approach. The results obtained from the neural network approach are compared with that from the ARMA model. It is found that the neural network approach provides more accurate wind speed time series prediction.