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<p>Air conditioning systems are essential for ensuring indoor thermal comfort in commercial buildings; however, they are also significant consumers of electrical energy, contributing to increased environmental impact. Optimizing the design of mechanical ventilation (MV) systems through multi-objective approaches can greatly improve both energy efficiency and cost-effectiveness. This study presents an advanced optimization strategy for MV in both a classical reference case and a real-world commercial installation. The methodology integrates principles of fluid mechanics with computational modeling to perform mass and pressure balances, combined with a simulated annealing algorithm for system optimization. The results demonstrate notable reductions in energy consumption, installation costs, and root mean square deviation of airflow rates from design targets. Furthermore, the proposed approach enables effective airflow distribution without the use of dampers. These findings highlight the potential of optimization techniques, particularly simulated annealing, in enhancing the performance, economic feasibility, and environmental sustainability of HVAC systems in commercial applications.</p>

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Optimization of air conditioning mechanical ventilation using simulated annealing for enhanced energy efficiency and cost reduction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

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  • Enio Pedone Bandarra Filho,
  • Gleyzer Martins,
  • Muhammad Bilal Riaz,
  • Sardar Muhammad Bilal,
  • Oscar Saul Hernandez Mendonza

摘要

Air conditioning systems are essential for ensuring indoor thermal comfort in commercial buildings; however, they are also significant consumers of electrical energy, contributing to increased environmental impact. Optimizing the design of mechanical ventilation (MV) systems through multi-objective approaches can greatly improve both energy efficiency and cost-effectiveness. This study presents an advanced optimization strategy for MV in both a classical reference case and a real-world commercial installation. The methodology integrates principles of fluid mechanics with computational modeling to perform mass and pressure balances, combined with a simulated annealing algorithm for system optimization. The results demonstrate notable reductions in energy consumption, installation costs, and root mean square deviation of airflow rates from design targets. Furthermore, the proposed approach enables effective airflow distribution without the use of dampers. These findings highlight the potential of optimization techniques, particularly simulated annealing, in enhancing the performance, economic feasibility, and environmental sustainability of HVAC systems in commercial applications.