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This study benchmarks various AI methodologies, including conventional machine learning, deep learning, and hybrid approaches, for optimizing and forecasting renewable energy outputs from wind energy converters (WECs). The findings reveal that tree ensembles, particularly Extra Trees, significantly outperform traditional models, achieving a 63.7% reduction in mean absolute error (MAE) compared to multi-layer perceptrons, while the RF BiLSTM hybrid model achieved the best overall forecasting accuracy with an MAE of 150.5 kW. The results underscore that no single architecture is optimal across all scenarios, highlighting the need for tailored approaches based on data characteristics and relationships.
Randomized tree ensembles can reduce forecasting errors in wind energy optimization by over 75% compared to traditional models, revealing a critical advantage in structured data scenarios.
This study provides a comprehensive benchmarking of conventional machine learning (ML), ensemble learning, deep neural networks, recurrent architectures, Transformers, graph based models, and hybrid ensemble deep learning approaches under complementary renewable energy scenarios. Three datasets are considered: a large scale WEC dataset, a 16 WEC dataset, and operational 10 min SCADA measurements at the Penmanshiel wind farm. For structured WEC layout data, tree ensembles exhibited a clear advantage over conventional ML and neural predictors because randomized partitioning and boosting efficiently captured nonlinear layout power interactions without requiring explicit feature representation learning. The Extra Trees was the strongest model, achieving considerable results. Relative to the MLP baseline, this corresponds to an approximately 63.7% reduction in MAE, demonstrating the suitability of randomized tree ensembles for high dimensional structured WEC data. Also, STGCN reduced the MAE to approximately 167.0 kW and achieved R = 0.93 by explicitly learning spatial and temporal turbine interactions. The best overall forecasting accuracy was obtained by the RF BiLSTM hybrid, with an MAE=150.5 kW. Compared with standalone LSTM, this represents an approximately 75% reduction in MAE, while improving on STGCN by approximately 10.0%. Finally, the experiments reveal that no single AI architecture is universally optimal: randomized and boosted ensembles are particularly effective for structured WEC surrogate modeling, graph networks become advantageous when explicit spatial interactions dominate, and ensemble recurrent hybrids provide the strongest balance when nonlinear tabular relationships and temporal dynamics coexist.
