用随机森林和强化学习优化氢能调度
该研究利用一年高分辨率运营数据，提出统计与机器学习框架表征氢基多能源系统。统计分析显示太阳辐照度解释了氢产量45.7%的秩基方差，且仅高辐照期触发电解槽有效运行。随机森林模型将风能输出排在预测重要性首位，尽管其二元相关性仅为r=0.167，揭示了非线性动力学。序列模型利用24小时自相关r=0.845实现运营预测，强化学习代理优化了氢收益调度。
A Statistical and Machine Learning Framework for Operational Threshold Detection and Deployable Dispatch Controller Development in Hydrogen Multi-Energy Systems
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.