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This paper presents a techno-economic model of grid-scale PEM electrolysis across a range of electricity price and utilization scenarios. We find that utilization rate and power price together explain the overwhelming majority of levelized hydrogen cost variance across the scenarios modeled, more than electrolyzer capital cost itself, reinforcing that power procurement strategy, not equipment selection, is the primary lever available to developers seeking cost-competitive green hydrogen in the near term.

We compare three machine learning approaches, gradient-boosted trees, a recurrent neural network, and an unsupervised anomaly detection model, for predicting wind turbine gearbox failure using SCADA and vibration data. The unsupervised anomaly detection approach achieved the best lead-time-to-failure performance despite requiring no labeled failure examples, suggesting it is the more practical starting point for operators with limited historical failure data, a common constraint across most operating wind fleets.