Abstract
Wind energy is rapidly increasing and it is becoming a significant contributor to the electricity grid. Wind speed is an important factor in wind power production and integration. This paper presents a wind speed forecasting using feature selection method and bagging neural network. Feature selection plays an essential role in the machine learning environment and especially in the prediction task. The ReliefF feature selection method is used for identification of necessary features for wind speed forecast and reduces the complexity of the model. A detailed investigation is to forecast wind speed with meteorological time series data as input variable using a bagging neural network. Performance is evaluated in terms of mean square error when using the feature selection method with the bagging neural network.
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