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This chapter delves into innovative data analysis techniques within the context of renewable energy engineering. Focused on machine learning (ML) techniques, and multi-objective optimization, the chapter aims to provide a comprehensive exploration of cutting-edge methodologies. The theoretical foundation encompasses the application of ML in renewable energy systems, highlighting their potential for optimization and decision-making. Subsequently, notable case studies showcase practical implementations, illustrating the efficacy of these techniques in real-world engineering scenarios. This chapter concludes with a detailed application of ML and metaheuristic algorithms in a hybrid Generator-Absorber Exchange (GAX) solar cooling system used for thermal conditioning applications, presenting the methodology and key results associated to the experimental measurements and computational multi-objective optimization. This chapter serves as a valuable resource for researchers and practitioners seeking novel data analysis approaches in renewable energy applications.

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Advancing Renewable Energy: Integrating Machine Learning and Multi-Objective Optimization for Efficient Data Analysis and Applications

  • 章节

  • 发表日期：

    2026年

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  • V. Cardoso-Fernández,
  • Luis J. Ricalde,
  • A. Bassam,
  • M. A. Escalante Soberanis,
  • Oscar May Tzuc

摘要

This chapter delves into innovative data analysis techniques within the context of renewable energy engineering. Focused on machine learning (ML) techniques, and multi-objective optimization, the chapter aims to provide a comprehensive exploration of cutting-edge methodologies. The theoretical foundation encompasses the application of ML in renewable energy systems, highlighting their potential for optimization and decision-making. Subsequently, notable case studies showcase practical implementations, illustrating the efficacy of these techniques in real-world engineering scenarios. This chapter concludes with a detailed application of ML and metaheuristic algorithms in a hybrid Generator-Absorber Exchange (GAX) solar cooling system used for thermal conditioning applications, presenting the methodology and key results associated to the experimental measurements and computational multi-objective optimization. This chapter serves as a valuable resource for researchers and practitioners seeking novel data analysis approaches in renewable energy applications.