Machine Learning for Optimization of Mass-Produced Industrial Silicon Solar Cells

Wagner-Mohnsen, Hannes; Altermatt, Pietro P.

doi:10.1109/nusod52207.2021.9541457

Cited by 1 publication

(1 citation statement)

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“…However, their review lacks a comprehensive comparison of ML techniques for other low‐cost solar cells, such as organic, inorganic, hybrid, and DSSCs. Additionally, Hannes et al [ 43 ] discussed the challenges of ambient hybrid solar cells for IoT devices, while the article presented by Hannes et al [ 44 ] reveals the study on solar cell cracks using statistical parameters of electroluminescent images using ML. However, both studies presented limited ML algorithms to explore solar cell electrical characteristics.…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques

Bhatti,

Manzoor,

Michel

et al. 2023

Adv Energy and Sustain Res

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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.

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Section: Introductionmentioning

confidence: 99%