技术对比基准研究：用于风电场优化与预测的先进AI混合方法
Technical Comparative Benchmarking Study: Advanced AI Hybrid Methods for Renewable Energy Farm Optimization and Forecasting
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中文总结 AI 辅助
本研究对比多种AI方法，在三类风电场数据集上测试后发现，不同架构各有优势，RF BiLSTM混合模型获最佳预测精度。
中文摘要 AI 辅助
本研究在互补可再生能源场景下，对传统机器学习（ML）、集成学习、深度神经网络、循环架构、Transformer、基于图的模型及混合集成深度学习方法进行了全面基准测试。考虑了三个数据集：大规模WEC数据集、含16台WEC的数据集，以及Penmanshiel风电场的10分钟SCADA运行测量数据。对于结构化WEC布局数据，树集成相较于传统ML和神经预测器展现出明显优势，因为随机划分与提升方法能有效捕捉非线性布局功率交互，无需显式特征表示学习。Extra Trees是表现最强的模型，取得了显著结果；相较于MLP基线，这对应MAE降低约63.7%，证明随机树集成适用于高维结构化WEC数据。此外，STGCN通过显式学习涡轮机的时空交互，将MAE降至约167.0 kW，且达到R=0.93。整体最佳预测精度由RF BiLSTM混合模型获得，MAE=150.5 kW；与单独的LSTM相比，这对应MAE降低约75%，同时较STGCN提升约10.0%。最后，实验表明，没有单一AI架构是普遍最优的：随机与提升集成对结构化WEC代理建模特别有效，图网络在显式空间交互占主导时更具优势，而集成循环混合在非线性表格关系与时间动态共存时提供最强平衡。
英文摘要
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.
