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This study introduces a machine learning-based climate classification framework specifically designed for optimizing photovoltaic (PV) module performance across diverse geographical regions. By incorporating both energy yield and module lifetime with climate-dependent degradation, the authors generate a dataset that identifies key predictors such as annual global horizontal irradiation and ambient temperature. The resulting model achieves impressive accuracy, revealing that low temperature continental climates yield the highest discounted lifetime energy output, which can significantly inform PV module design and deployment strategies.
Low temperature continental climates deliver the highest discounted lifetime energy yield for photovoltaic modules, reshaping strategies for solar energy deployment.
To resiliently and sustainably meet our future energy demand, photovoltaic (PV) modules must be deployed across a broad and diverse range of geographical regions with varying operating conditions. As these conditions strongly affect both performance and optimal system design, a dedicated PV-specific climate classification can be of great use. In this work, we develop a climate classification framework tailored to PV applications using a variety of machine learning (ML) techniques. Building on previous studies, our approach incorporates both energy yield, and for the first time, also the module lifetime with climate dependent degradation. We generate an interpolated dataset containing twelve input features and two target variables (i.e. energy yield and module lifetime). Feature importance analysis shows that annual global horizontal irradiation and ambient temperature are the most influential predictors. The most accurate regression model achieves root mean square errors (RMSE) of 0.007 MWh for energy yield and 1.5 years for lifetime prediction. The calculated feature importance scores are then integrated into a hierarchical clustering framework, resulting in 6 primary climate clusters (Tropical, Desert, Continental, Temperate, Boreal, and Polar) and 15 corresponding subclusters. Our analysis shows that the low temperature continental climate offers the highest discounted lifetime energy yield. These results can support a wide range of applications, including PV module optimization, system siting decisions, and comparative performance studies.
