AI-Enhanced lifecycle assessment of renewable energy systems

Abstract

As the global push towards renewable energy intensifies, it becomes imperative to comprehensively assess the environmental impacts and sustainability of renewable energy systems throughout their operational lifecycle. Traditional lifecycle assessment (LCA) methods, while useful, often fall short in handling the complex, dynamic data associated with renewable energy systems. This study explores the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance lifecycle assessments of wind, solar, and green hydrogen energy systems, aiming to provide more accurate, efficient, and comprehensive evaluations. AI-driven LCA models leverage extensive datasets from various stages of the lifecycle of renewable energy systems, including raw material extraction, manufacturing, installation, operation, maintenance, and decommissioning. By employing ML algorithms, these models can identify patterns and relationships within the data, predict potential environmental impacts, and provide insights into sustainability performance over time. The research focuses on developing and validating ML models that incorporate diverse data inputs such as material usage, energy consumption, emissions, and waste generation. These models are trained using historical data from multiple renewable energy projects and are capable of adapting to new data inputs, ensuring continuous improvement in assessment accuracy. Key findings demonstrate that AI-enhanced LCA models significantly improve the precision and depth of environmental impact assessments. For wind energy systems, ML models help in predicting turbine lifespan and maintenance needs, thereby optimizing resource use and minimizing environmental footprints. In solar energy systems, AI techniques assist in forecasting degradation rates and energy yield, contributing to more sustainable design and operation. For green hydrogen production, ML models optimize the electrolysis process and assess the overall sustainability of hydrogen supply chains. The integration of AI in LCA facilitates real-time monitoring and dynamic adjustments, ensuring that renewable energy systems operate at peak sustainability. This approach not only enhances the environmental performance of individual systems but also supports strategic decision-making in renewable energy deployment and policy development. In conclusion, the application of AI and ML techniques in lifecycle assessment offers a transformative approach to evaluating the environmental impact and sustainability of renewable energy systems. This research underscores the critical role of advanced analytics in advancing the global transition to sustainable energy and calls for further exploration and adoption of AI-driven LCA methodologies. Keywords: Machine Learning, Renewable Energy Systems, Environmental Impact, Sustainability, AI-Enhanced Lifecycle.