Short-term wind power forecasting based on Attention Mechanism and Deep Learning

Wind power forecasting is an important means to alleviate the pressure of peak and frequency regulation in power systems and improve the acceptance capacity of wind power. However, physical attribute data related to wind power have different effects on its forecasting, and the long-term sequence of original features has redundant information, which makes wind power prediction a daunting challenge. To address these problems, this paper proposes a multi-dimensional extended features fusion model called AMC-LSTM to predict wind power. The Attention Mechanism is utilized to dynamically assign the weight of physical attribute data, which effectively deals with the model's failure to distinguish the difference in importance of input data. Convolutional neural network (CNN) is used for short-term abstract feature extraction to obtain local high-dimensional features, and then Long short-term memory (LSTM) is used to extract the long-term trend of local high-dimensional features, which can effectively reduce the problem of inaccurate prediction caused by the mixing of original data. The extracted temporal features and physical features are fused to predict wind power. Using actual operation data of wind turbine, we verified that the proposed AMC-LSTM hybrid model is capable of integrating multi-scale extended features and providing better performance for short-term wind forecasting.