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Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction

Author

Listed:
  • Ganapathy Ramesh

    (Department of Information Technology, KLN College of Engineering, Madurai 630612, India)

  • Jaganathan Logeshwaran

    (Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore 641202, India)

  • Thangavel Kiruthiga

    (Department of Electronics and Communication Engineering, Vetri Vinayaha College of Engineering and Technology, Thottiam 621214, India)

  • Jaime Lloret

    (Integrated Management Coastal Research Institute, Universitat Politecnica de Valencia, Camino Vera s/n, 46022 Valencia, Spain)

Abstract

In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovoltaics, enhance organizational dependability, and maximize the utilization factor of the PV systems for something such as an energy management system (EMS) of microgrids. This paper proposes an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, the solar energy output may be projected using prior sun illumination and meteorological data. The feature selection and prediction modules use an AUTO encoder-based Neural Network to improve the process of energy prediction (AUTO-NN). Restricted Boltzmann Machines (RBM) can be used during a set of regulations for development-based feature extraction. The proposed model’s result is evaluated using various constraints. As a result, the proposed AUTO-NN achieved 58.72% of RMSE (Root Mean Square Error), 62.72% of nRMSE (Normalized Root Mean Square Error), 48.04% of MaxAE (Maximum Absolute Error), 48.66% of (Mean Absolute Error), and 46.76% of (Mean Absolute Percentage Error).

Suggested Citation

  • Ganapathy Ramesh & Jaganathan Logeshwaran & Thangavel Kiruthiga & Jaime Lloret, 2023. "Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:46-:d:1046835
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    References listed on IDEAS

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