Photovoltaic Lifetime Forecast: Models for long-term photovoltaic degradation prediction and forecast

Abstract

When exposed in outdoor operating conditions, photovoltaic (PV) modules are affected by different climatic stresses causing different degradation mechanisms within the module, which leads to performance loss. Mathematical models are usually utilized to determine degradation rates of PV modules in shorter periods and extrapolate to long-term degradation predictions. Two modelling methods: physical and data driven are commonly used. However, when long-term photovoltaic degradation predictions are required after a shorter time, the existing physical and data-driven methods often provide unrealistic degradation scenarios. Therefore, in this research two modelling approaches are developed. Firstly, a physical model to determine the degradation rates of PV modules based on outdoor climatic variables is developed. The model depends on the different degradation mechanisms leading to PV modules degradation. Secondly, a novel data-driven method has been proposed to improve the accuracy of long-term prediction with small degradation history. The model depends on the degradation patterns and multiple time dependent degradation factors. Finally, the two approaches are combined to form a hybrid model. The proposed physical model has been validated using outdoor experimental data of three identical mono-crystalline silicon modules installed in three benchmarking climates: maritime, arid and alpine. The proposed model can be applied to determine the degradation rates worldwide, given climatic variables are available. The proposed data-driven model has been calibrated and validated using different photovoltaic modules and systems data. The model provides reliable and more accurate long-term lifetime prediction compared to the available data-driven methods.