Perovskite Solar Cell Stability Analysis Using Entropy‐Based Support Vector Machines Learning

Rupam Bhaduri; S. Manasa

doi:10.1002/pip.3861

journal article Nov 16, 2024

Perovskite Solar Cell Stability Analysis Using Entropy‐Based Support Vector Machines Learning

Rupam Bhaduri

S. Manasa

Progress in Photovoltaics: Research and Applications Vol. 33 No. 9 pp. 962-979 · Wiley

View at Publisher Save 10.1002/pip.3861

Abstract

ABSTRACTLead halide perovskites have demonstrated significant potential for photovoltaic (PV) applications over the past 10 years. Perovskite solar cells (PSCs) stability, however, continues to limit their commercialization, and the inability to compare previous stability data to assess possible directions for increasing device stability is caused by a lack of effectively established unified stability testing and disseminating standards. In this article, we suggest applying machine learning (ML) to improve the thermal, chemical, and structural stability of PSCs. Data normalization and data augmentation are common preprocessing steps that are where the process starts. Then, using the Modified Grasshopper Optimisation Algorithm (MGO), feature selection techniques are used to remove unnecessary or irrelevant features. Finally, there is a novel machine learning technique that uses support vector machines (ESVM) that are based on entropy to forecast the stability classification of stable/unstable. The proposed reaches an accuracy of 0.99% far better than the proposed methods.

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Metrics

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Citations

26

References

Details

Published: Nov 16, 2024
Vol/Issue: 33(9)
Pages: 962-979
License: View

Authors

R

Rupam Bhaduri

Dayananda Sagar University Bangalore India

S

S. Manasa

Dayananda Sagar University Bangalore India

Cite This Article

Rupam Bhaduri, S. Manasa (2024). Perovskite Solar Cell Stability Analysis Using Entropy‐Based Support Vector Machines Learning. Progress in Photovoltaics: Research and Applications, 33(9), 962-979. https://doi.org/10.1002/pip.3861

Perovskite Solar Cell Stability Analysis Using Entropy‐Based Support Vector Machines Learning

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