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Wind power forecasting helps with the planning for the power systems bycontributing to having a higher level of certainty in decision-making. Due tothe randomness inherent to meteorological events (e.g., wind speeds), makinghighly accurate long-term predictions for wind power can be extremelydifficult. One approach to remedy this challenge is to utilize weatherinformation from multiple points across a geographical grid to obtain aholistic view of the wind patterns, along with temporal information from theprevious power outputs of the wind farms. Our proposed CNN-RNN architecturecombines convolutional neural networks (CNNs) and recurrent neural networks(RNNs) to extract spatial and temporal information from multi-dimensional inputdata to make day-ahead predictions. In this regard, our method incorporates anultra-wide learning view, combining data from multiple numerical weatherprediction models, wind farms, and geographical locations. Additionally, weexperiment with global forecasting approaches to understand the impact oftraining the same model over the datasets obtained from multiple different windfarms, and we employ a method where spatial information extracted fromconvolutional layers is passed to a tree ensemble (e.g., Light GradientBoosting Machine (LGBM)) instead of fully connected layers. The results showthat our proposed CNN-RNN architecture outperforms other models such as LGBM,Extra Tree regressor and linear regression when trained globally, but fails toreplicate such performance when trained individually on each farm. We alsoobserve that passing the spatial information from CNN to LGBM improves itsperformance, providing further evidence of CNN's spatial feature extractioncapabilities.

A Concurrent CNN-RNN Approach for Multi-Step Wind Power Forecasting