View article

Recently, many countries have spent great efforts on wind power generation. Although there have been many methods in the field of wind power forecasting, the persistence statistics model based on historical data is still being challenged due to the randomness and uncontrollability in wind power. Hence, a more accurate and effective wind power forecasting method is still required. In this paper, a new forecasting method is proposed by combining stacked auto-encoders (SAE) and the back propagation (BP) algorithm. First, an SAE with three hidden layers is designed to extract the characteristics from the reference data sequence, and the subsequent loss function is used in the pre-training process to obtain the optimal initial connection weights of the deep network. Second, after adding one output layer to the stacked auto encoders, the BP algorithm is used to fine tune the weights of the whole network. To achieve …