Feature Selection used for Wind Speed Forecasting with Data Driven Approaches

Abstract

Wind speed forecasting is important for wind power generation and integration. In this paper, Nonlinear Autoregressive model process with eXogenous input (NARX) is proposed for wind speed forecast. The main aim of this experiment is to forecast wind speed with meteorological time series data as input variable using NARX model. Prior to forecasting, ReliefF feature selection is used for identification of important features for wind speed forecast and reduces the complexity of the model. Performance is evaluated in terms of mean square error when using the feature selection method with the NARX model.

Keywords

• Wind speed
• Feature selection
• NARX
• ReliefF ___________________