Deep learning approach for wind speed forecasts at turbine locations in a wind farm

In a wind farm, individual turbines disturb the wind field by generating wakes, so wind speeds at various turbine locations are different. From the perspective of wind farm control, there is an interest in dynamic optimization of the power reference for each individual wind turbine, and the wind speed forecast at each turbine location is hence required. This paper develops a joint model of convolutional neural network (CNN) and the gated recurrent units (GRU) to forecast the wind speed at turbine locations. This model employs a two‐layer architecture. At the lower‐layer, the spatial features are automatically extracted by CNN. The extracted spatial features describe the spatial correlations among multiple wind turbines. At the upper‐layer, GRU learns the temporal correlations across the extracted spatial features. This joint model is trained in an integrated manner. A salient characteristic of this model is that it extracts high‐level spatial‐temporal features from wind data. These automatically learnt features capture the spatial‐temporal wind dynamics and interactions in a wind farm, thus being informative and appropriate for the forecasting at specific turbine locations. The simulation on actual data demonstrates the effectiveness of the presented model.