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 WPF problem remains unresolved due to numerous influencing variables, such as wind speed, temperature, latitude, and longitude
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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.