Ultra-short-term wind speed prediction based on deep spatial-temporal residual network

To maintain power system stability, accurate wind speed prediction is essential. Taking into account the temporal and spatial characteristics of wind speed in an integrated manner can improve the accuracy of wind speed prediction. Considering complex nonlinear spatial factors such as wake effects in wind farms, a deep residual network is valuable in predicting wind speed with a high degree of accuracy. Wind speed data are typically a time series that requires feature extraction and attribute modeling, while maintaining signal integrity. In order to measure the importance of different temporal attributes and effectively aggregate temporal and spatial features, we used a parameter fusion matrix. We introduce a deep spatial-temporal residual network (DST-ResNet) for wind speed prediction that extracts the spatial-temporal characteristics, which can forecast the future wind speed of a multi-site wind farm in a particular region. In this model, wind speed data's nearby property and periodic property are separately modeled using a residual network. The outputs of the two temporal components are dynamically aggregated using a parameter fusion matrix and then fused with additional meteorological features to achieve wind speed prediction. Based on wind data from the National Renewable Energy Laboratory, our experiments show that the proposed DST-ResNet improves prediction accuracy by 8.90%.