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The world is going to be in a deep crisis of energy. The non-renewable sources are fast depleting due to the increased usage in the various industries and housing. However, the existing renewable sources of energy especially the solar energy has some drawbacks in operation which need to be dealt with. In this research paper, we will try to find a way to smartly detect the future prediction of the energy which will be generated by the solar panels in a solar power station. Along with that we also aim to detect the condition of the solar plates so that we get an idea if there is any anomaly that can reduce the power output. The entire process is automated with the help of ML and deep learning techniques and the output generated is much more accurate compared to the previously used methods. This helps to leverage the full potential of artificial intelligence and devise a smart and easy procedure to help the plant managers easily understand the energy which can be probably generated by the solar panels. Thus, we solve an existing issue with a renewable source of energy making it a sustainable solution for the problem.

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Solar Energy Generation System: Prediction and Inspection Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Sneh Gupta,
  • Devansh Maheshwari,
  • P. Rajasekaran,
  • N. Prabakaran

摘要

The world is going to be in a deep crisis of energy. The non-renewable sources are fast depleting due to the increased usage in the various industries and housing. However, the existing renewable sources of energy especially the solar energy has some drawbacks in operation which need to be dealt with. In this research paper, we will try to find a way to smartly detect the future prediction of the energy which will be generated by the solar panels in a solar power station. Along with that we also aim to detect the condition of the solar plates so that we get an idea if there is any anomaly that can reduce the power output. The entire process is automated with the help of ML and deep learning techniques and the output generated is much more accurate compared to the previously used methods. This helps to leverage the full potential of artificial intelligence and devise a smart and easy procedure to help the plant managers easily understand the energy which can be probably generated by the solar panels. Thus, we solve an existing issue with a renewable source of energy making it a sustainable solution for the problem.