Machine learning for advanced characterisation of silicon photovoltaics: A comprehensive review of techniques and applications

Yoann Buratti, Gaia M.N. Javier, Zubair Abdullah-Vetter, Priya Dwivedi and Ziv Hameiri

Renewable and Sustainable Energy Reviews, 2024, vol. 202, issue C

Abstract: Accurate and efficient characterisation techniques are essential to ensure the optimal performance and reliability of photovoltaic devices, especially given the large number of silicon solar cells produced each day. To unlock valuable insights from the amount of data generated during the characterisation process, researchers have increasingly turned to different machine learning (ML) techniques. In this review, advances in ML applications for silicon photovoltaic (PV) characterisation from 2018 to 2023, including device investigation, process optimisation, and manufacturing line assessment are examined. Additionally, studies on deep learning techniques for luminescence-based measurements, such as defect classification, detection, and segmentation, which can help manufacturers identify potential reliability issues are explored. Despite the abundance of ML applications, it is emphasised that the lack of both publicly available datasets and the uniform use of ML metrics poses a significant challenge for researchers to benchmark their frameworks and achieve consistent and accurate results. In advancing ML applications in PV, future research should focus on improving model interpretability, balancing speed and accuracy, understanding computational demands, and integrating niche applications into a unified framework. Lastly, industry involvement and interdisciplinary collaboration among experts in solar energy, data science, and engineering are vital in tailoring ML solutions and enhancing innovation in addressing various challenges in the PV field.

Keywords: Photovoltaics; Silicon solar cells; Characterisation; Machine learning; Deep learning; Optimisation; Luminescence; Defects; Artificial intelligence (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2024.114617

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