对比多种AI模型在风电场优化与预测中的表现，发现不同场景下各有优劣。
Technical Comparative Benchmarking Study: Advanced AI Hybrid Methods for Renewable Energy Farm Optimization and Forecasting
- 用随机森林等集成方法处理结构化风电数据，效果优于传统神经网络。
- 混合模型RF BiLSTM将预测误差降低75%，是整体最优方案。
- 根据数据特点选模型：结构数据用集成树，空间关系强用图网络。
本研究对传统机器学习、集成学习、深度神经网络、循环架构、Transformer、图模型及混合集成深度学习方法，在互补可再生能源场景下进行了全面基准测试。使用三个数据集：大规模波浪能转换器（WEC）数据集、16个WEC数据集，以及彭曼希风场的10分钟实时SCADA运行数据。对于结构化WEC布局数据，随机分割与提升的树集成方法显著优于传统机器学习和神经网络预测器，因其无需显式特征学习即可高效捕捉非线性布局功率交互。其中Extra Trees表现最佳，相比MLP基线，平均绝对误差（MAE）降低约63.7%。STGCN通过显式学习时空涡轮互动，将MAE降至约167.0 kW，R值达0.93。最优综合预测精度由RF BiLSTM混合模型实现，MAE为150.5 kW。相较独立LSTM，误差减少约75%，较STGCN提升约10.0%。实验表明：无单一模型通用于所有场景；随机与增强集成适用于结构化WEC代理建模；当显式空间交互主导时，图网络更优；当非线性表格关系与时间动态共存时，集成循环混合模型最具平衡优势。
原文摘要 · Abstract (English)
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.
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