Unsupervised Prediction of Damp Heat Degradation in Heterojunction Photovoltaic Modules Using Deep Learning

ABSTRACT Photovoltaic energy is currently the most affordable form of electricity due to ongoing improvements in the efficiency and reliability of solar modules. Recently, it was suggested that 75 terawatts of installed photovoltaic capacity will be necessary by 2050 to reach the global decarbonisation targets. This demand increases the need for photovoltaic modules to become even cheaper and more reliable. The capability to predict the long‐term field performance of photovoltaic modules is vital to improving their reliability. An accurate performance prediction will also improve the bankability of utility‐scale photovoltaic plants, as trustworthy long‐term predictions reduce uncertainties in financing such projects. This study presents a proof‐of‐concept approach for predicting performance trends using a generative, deep learning model. This model was trained on time series data gathered from multiple 1512‐h accelerated damp heat tests of heterojunction PV modules. Despite using only the first 30% of the time series data, this generative learning framework accurately predicts multiple current–voltage and electroluminescence trends throughout the test duration. These predictions also highlight a key advantage over traditional approaches, which require the manual fitting of single‐parameter trends. The model is also shown to automatically correlate the impact of temperature on the degradation kinetics of photovoltaic modules, demonstrating its ability to learn and model the impact of environmental conditions on the observed degradation. While the model is trained on laboratory‐based measurements, the approach can be modified to use data collected from fielded photovoltaic modules. With further development, this approach could support improved module design and maintenance strategies, enhancing the reliability of fielded PV systems. This study is a critical step towards developing machine learning‐based tools for data‐driven reliability improvements of photovoltaic modules.