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This paper focuses on the development of two prediction models for a solar photovoltaic system that is part of a multimachine industrial manufacturing plant. These models are part of the set of models that form the digital twin of their physical counterparts, which will be used to perform control and optimization strategies to maximize the use of renewable energy sources within a Digital Twin (DT) architecture. The first model is based on a fuzzy neural network and the second one is a Gaussian regression model. The obtained models present a good performance in the prediction of the nonlinear dynamic over the entire operating range in the system.

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Development of Neurofuzzy and Gaussian Regression Models for a Solar Photovoltaic System

  • 会议论文

  • 发表日期：

    2025年

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  • William D. Chicaiza,
  • Yeyson A. Becerra-Mora,
  • Juan M. Escaño,
  • Adolfo J. Sánchez,
  • José Ángel Acosta

摘要

This paper focuses on the development of two prediction models for a solar photovoltaic system that is part of a multimachine industrial manufacturing plant. These models are part of the set of models that form the digital twin of their physical counterparts, which will be used to perform control and optimization strategies to maximize the use of renewable energy sources within a Digital Twin (DT) architecture. The first model is based on a fuzzy neural network and the second one is a Gaussian regression model. The obtained models present a good performance in the prediction of the nonlinear dynamic over the entire operating range in the system.