Benchmarking Advanced AI for Renewable Energy Forecasting and Optimization

Optimizing and forecasting for renewable energy farms presents complex challenges, requiring sophisticated AI methods. A new benchmarking study comprehensively evaluates various machine learning, deep neural network, and hybrid ensemble deep learning approaches across diverse renewable energy scenarios. The research utilized three distinct datasets, including large-scale wind energy converter (WEC) data and operational SCADA measurements from a wind farm. For structured WEC layout data, tree ensembles, particularly Extra Trees, showed a significant advantage over conventional machine learning and neural predictors. Their ability to efficiently capture nonlinear layout power interactions without explicit feature representation learning led to a 63.7% reduction in Mean Absolute Error (MAE) compared to an MLP baseline. Graph-based models like STGCN also performed well when spatial and temporal turbine interactions were dominant, reducing MAE to 167.0 kW. The study concluded that no single AI architecture is universally optimal. The best overall forecasting accuracy was achieved by the RF BiLSTM hybrid, which reduced MAE by approximately 75% compared to a standalone LSTM. This hybrid model demonstrated the strongest balance when both nonlinear tabular relationships and temporal dynamics were present, highlighting the importance of selecting the right AI approach based on data characteristics and specific forecasting needs.