Revolutionizing low-cost solar cells with machine learning: a systematic review of optimization techniques

Bhatti, S., Manzoor, H. U., Michel, B., Bonilla, R. S., Abrams, R., Zoha, A. , Hussain, S. and Ghannam, R. (2023) Revolutionizing low-cost solar cells with machine learning: a systematic review of optimization techniques. Advanced Energy and Sustainability Research, 10(4), 2300004. (doi: 10.1002/aesr.202300004)

Abstract

Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low-cost photovoltaic (PV) cells in miniaturized electronic devices. Indeed, ML is set to significantly contribute to the development of more efficient and cost-effective solar cells. This systematic review offers an extensive analysis of recent ML techniques in designing novel solar cell materials and structures, highlighting their potential to transform the low-cost solar cell research and development landscape. The review encompasses a variety of ML approaches, such as Gaussian process regression (GPR), Bayesian optimization (BO), and deep neural networks (DNNs), which have proven effective in boosting the efficiency, stability, and affordability of solar cells. The findings of this review indicate that GPR combined with BO is the most promising method for developing low-cost solar cells. These techniques can significantly speed up the discovery of new PV materials and structures while enhancing the efficiency and stability of low-cost solar cells. The review concludes with insights on the challenges, prospects, and future directions of ML in low-cost solar cell research and development.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zoha, Dr Ahmed and Manzoor, Habib Ullah and Ghannam, Professor Rami and Bhatti, Mr Satyam and Hussain, Dr Sajjad
• Authors: Bhatti, S., Manzoor, H. U., Michel, B., Bonilla, R. S., Abrams, R., Zoha, A., Hussain, S., and Ghannam, R.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Advanced Energy and Sustainability Research
• Publisher: Wiley
• ISSN: 2699-9412
• ISSN (Online): 2699-9412
• Published Online: 23 August 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Advanced Energy and Sustainability Research 10(4):2300004
• Publisher Policy: Reproduced under a Creative Commons License