EMGRL, an actor-critic ensemble of ARIMA, LightGBM, LSTM, and SGNN with a GNN-based spatial state embedding, is claimed to beat baselines on NREL and GEFC wind datasets.
The hyperparameters of the SVM are optimized using the JAYA optimization algorithm using the most representative features in the input data
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Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting
EMGRL, an actor-critic ensemble of ARIMA, LightGBM, LSTM, and SGNN with a GNN-based spatial state embedding, is claimed to beat baselines on NREL and GEFC wind datasets.