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Photovoltaic (PV) cells, which convert sunlight directly into electricity, are a crucial technology for renewable energy generation. This paper investigates various parameters that influence power generation in PV cells. Additionally, it explores the potential of machine learning algorithms in monitoring the structural health of PV cells using sensor data. The search was conducted from 2017 to 2024. By analyzing 49 research papers identified through a bibliometric analysis, the study reveals a global upsurge in this research area. Notably, the India, China, United States, and Saudi Arabia are leading contributors, with most publications occurring in the last seven years. However, a significant research gap exists, particularly regarding the application of machine learning for PV health monitoring in the Indian context. This study aims to address this gap by identifying key parameters affecting power generation and exploring various machine-learning algorithms for robust structural health monitoring of PV cells in India.

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A Bibliometric-Based Analysis of Solar Energy Management Using Digital Technology and Machine Learning Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Sanjeev Kumar Sharma,
  • Siddhartha Srinivas Rentala,
  • Vishwajeet Singh Tomar,
  • Sumit Kumar Jha,
  • Jatin Katyal,
  • Priyank Srivastava

摘要

Photovoltaic (PV) cells, which convert sunlight directly into electricity, are a crucial technology for renewable energy generation. This paper investigates various parameters that influence power generation in PV cells. Additionally, it explores the potential of machine learning algorithms in monitoring the structural health of PV cells using sensor data. The search was conducted from 2017 to 2024. By analyzing 49 research papers identified through a bibliometric analysis, the study reveals a global upsurge in this research area. Notably, the India, China, United States, and Saudi Arabia are leading contributors, with most publications occurring in the last seven years. However, a significant research gap exists, particularly regarding the application of machine learning for PV health monitoring in the Indian context. This study aims to address this gap by identifying key parameters affecting power generation and exploring various machine-learning algorithms for robust structural health monitoring of PV cells in India.