Ensemble learning for wind profile prediction with missing values

In this paper, we aim to develop computational intelligence approaches for wind profile prediction. Specifically, we focus on two aspects in this work. First, we investigate the missing value recovery for wind data. Due to the complexity of data collection in such processes, wind data normally include missing values. Therefore, how to effectively recover such missing values for learning and prediction is an important aspect for wind profile prediction. Second, we develop an ensemble learning approach based on multiple neural network models. Our proposed method uses a new strategy based on the temporal information to assign the weights for each model dedicated for wind profile prediction to achieve better prediction performance. Various simulation studies and statistical testing demonstrate the effectiveness of our approach.