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.

翻译：本研究基于一年的高分辨率运行数据，提出了一种统计与机器学习框架，用于表征基于氢的多能源系统（H-MES）。统计分析揭示了一种由可再生能源盈余驱动的二元运行模式，其中太阳辐照度解释了氢产量中45.7%的基于秩的方差，按常规标准属于大效应量。仅在高辐照度时段电解槽才会显著启动，而电力需求则呈现较弱的反向抑制效应（$ε^2 = 0.126$）。多元回归证实电解槽功率是主要线性预测因子，并存在协同的风-光交互作用。值得注意的是，随机森林分析将风电输出列为预测重要性首位，尽管其双变量相关性较弱（r = 0.167），从而揭示了参数化方法无法捕捉的非线性动态特性。序列模型利用显著的24小时自相关性（r = 0.845）进行运行预测，而强化学习智能体则优化了氢能收益调度。核心贡献在于论证了统计方法与机器学习方法在H-MES建模与控制中的互补性。