Ultra‐short‐term multi‐step wind power forecasting based on CNN‐LSTM

Abstract

The fluctuation and intermission of large-scale wind power integration is a serious threat to the stability and security of the power system. Accurate prediction of wind power is of great significance to the safety of wind power grid connection. This study proposes a novel spatio-temporal correlation model (STCM) for ultra-short-term wind power prediction based on convolutional neural networks-long short-term memory (CNN-LSTM). The original meteorological factors at multi-historical time points of different sites throughout the target wind farm can be reconstructed into the input window of the model, and thus a new data reconstruction method is represented. CNN is used to extract the spatial correlation feature vectors of meteorological factors of different sites and the temporal correlation vectors of the meteorological features in ultra-short term, which are reconstructed in time series and used as the input data of LSTM. Then, LSTM extracts the temporal feature relationship between the historical time points for multi-step wind power forecasting. The STCM based on CNN-LSTM proposed in this study is suitable for wind farms that can collect meteorological factors at different locations. Taking the measured meteorological factors and wind power dataset of a wind farm in China as an example, four evaluation metrics of the CNN-LSTM model, CNN and LSTM individually used for multi-step wind power prediction, are obtained. The results show that the proposed STCM based on CNN-LSTM has better spatial and temporal characteristics extraction ability than the traditional structure model and can forecast the power of wind farm more accurately.