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This research investigates the application of Artificial Neural Networks (ANNs) for predicting electrical and thermal efficiency in photovoltaic systems. The study employs input variables like solar irradiation, ambient temperature, and cell temperature to develop ANNs for accurate predictions. The ANN models exhibit strong relationships with the data, as indicated by high correlation coefficients (R) close to 1. Mean Square Error (MSE) and Root Mean Square Error (RMSE) were used to assess the models’ accuracy and validity, further confirming the robustness of the ANN models. The results demonstrate the potential of ANNs in optimizing photovoltaic system performance, making them a valuable tool in renewable energy research.

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Predicting Photovoltaic System Performance with Artificial Neural Networks: A Data-Driven Approach for Enhanced Efficiency

  • 会议论文

  • 发表日期：

    2025年

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  • Shweta Singh,
  • Rakesh Kumar Singh,
  • Anil Kumar

摘要

This research investigates the application of Artificial Neural Networks (ANNs) for predicting electrical and thermal efficiency in photovoltaic systems. The study employs input variables like solar irradiation, ambient temperature, and cell temperature to develop ANNs for accurate predictions. The ANN models exhibit strong relationships with the data, as indicated by high correlation coefficients (R) close to 1. Mean Square Error (MSE) and Root Mean Square Error (RMSE) were used to assess the models’ accuracy and validity, further confirming the robustness of the ANN models. The results demonstrate the potential of ANNs in optimizing photovoltaic system performance, making them a valuable tool in renewable energy research.