用统计与机器学习方法，精准识别氢能系统运行阈值并优化调度策略。
A Statistical and Machine Learning Framework for Operational Threshold Detection and Deployable Dispatch Controller Development in Hydrogen Multi-Energy Systems
- 结合统计分析与机器学习，挖掘氢能系统的运行规律。
- 光照强度解释45.7%的制氢波动，风电虽相关性低但预测重要。
- 适合能源系统建模、智能调度研究者参考。
本研究基于一年高分辨率运行数据，构建了针对氢基多能系统（H-MES）的统计与机器学习框架。统计分析显示系统呈二元运行模式，受可再生能源过剩驱动；太阳辐照度解释了制氢量排名方差的45.7%，影响显著。仅高辐照期触发电解槽有效运行，电力需求则呈现较弱的反向抑制效应（ε² = 0.126）。多重回归确认电解槽功率为关键线性预测因子，并揭示太阳能与风能间的协同作用。值得注意的是，随机森林分析中风力输出预测重要性排名第一，尽管其双变量相关性较弱（r = 0.167），凸显非线性动态特征。序列模型利用强24小时自相关性（r = 0.845）实现运行预测，强化学习代理则优化氢气收益调度。核心贡献在于证明统计与机器学习方法在H-MES建模与控制中具有互补性。
原文摘要 · Abstract (English)
This study presents a statistical and machine learning framework for characterizing a hydrogen-based multi-energy system (H-MES) using one year of high-resolution operational data. Statistical analysis revealed a binary operation driven by renewable surplus, with solar irradiance explaining 45.7% of rank-based variance in hydrogen production, a large effect by conventional standards. Only high-irradiance periods triggered meaningful electrolyzer engagement, while electricity demand exerted a weaker inverse suppression effect ($ε^2 = 0.126$). Multiple regression confirmed electrolyzer power as the dominant linear predictor, with a synergistic solar-wind interaction. Notably, Random Forest analysis ranked wind output first in predictive importance despite its weak bivariate correlation (r = 0.167), revealing non-linear dynamics invisible to parametric methods. A sequence model exploited strong 24-hour autocorrelation (r = 0.845) for operational forecasting, while a reinforcement learning agent optimized hydrogen revenue dispatch. The core contribution is demonstrating that statistical and machine learning approaches are complementary for H-MES modeling and control.
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