Machine Learning Risk Intelligence for Green Hydrogen Investment: Insights for Duqm R3 Auction

2025-07-21

AI Summary

In the Duqm region of Oman, the absence of historical operational data for green hydrogen facilities impedes quantitative assessment of maintenance and production risks under harsh desert conditions. Method: This study proposes a machine learning–based decision support framework leveraging publicly available meteorological data. It introduces a novel Maintenance Pressure Index (MPI), constructed from environmental proxy variables—including sandstorm frequency, extreme temperature excursions, and humidity volatility—to model equipment degradation trends without requiring prior maintenance records. Contribution/Results: The resulting model enables time-series forecasting of future maintenance requirements, thereby enhancing regulatory foresight and improving the scientific rigor and verifiability of Resource-Response-Reserve (R3) auction decisions. By bridging the data gap in early-stage green hydrogen infrastructure deployment, the approach supports risk-informed operations planning in arid environments.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

As green hydrogen emerges as a major component of global decarbonisation, Oman has positioned itself strategically through national auctions and international partnerships. Following two successful green hydrogen project rounds, the country launched its third auction (R3) in the Duqm region. While this area exhibits relative geospatial homogeneity, it is still vulnerable to environmental fluctuations that pose inherent risks to productivity. Despite growing global investment in green hydrogen, operational data remains scarce, with major projects like Saudi Arabia's NEOM facility not expected to commence production until 2026, and Oman's ACME Duqm project scheduled for 2028. This absence of historical maintenance and performance data from large-scale hydrogen facilities in desert environments creates a major knowledge gap for accurate risk assessment for infrastructure planning and auction decisions. Given this data void, environmental conditions emerge as accessible and reliable proxy for predicting infrastructure maintenance pressures, because harsh desert conditions such as dust storms, extreme temperatures, and humidity fluctuations are well-documented drivers of equipment degradation in renewable energy systems. To address this challenge, this paper proposes an Artificial Intelligence decision support system that leverages publicly available meteorological data to develop a predictive Maintenance Pressure Index (MPI), which predicts risk levels and future maintenance demands on hydrogen infrastructure. This tool strengthens regulatory foresight and operational decision-making by enabling temporal benchmarking to assess and validate performance claims over time. It can be used to incorporate temporal risk intelligence into auction evaluation criteria despite the absence of historical operational benchmarks.

Problem

• Assessing green hydrogen investment risks without historical data
• Predicting maintenance pressures using environmental conditions
• Developing AI tool for infrastructure risk intelligence

Innovation

• AI system predicts maintenance using weather data
• Maintenance Pressure Index assesses infrastructure risks
• Temporal benchmarking for auction evaluation criteria