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A Bio-Optimization Approach for Renewable Energy Management: The Case of a University Building in a Tropical Climate

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  • Orlando Aguilar Pinzón

    (Research Group Energy and Comfort in Bioclimatic Buildings (ECEB), Faculty of Mechanical Engineering, Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama)

  • Orlando Aguilar Gallardo

    (Research Group Energy and Comfort in Bioclimatic Buildings (ECEB), Faculty of Mechanical Engineering, Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama)

  • Miguel Chen Austin

    (Research Group Energy and Comfort in Bioclimatic Buildings (ECEB), Faculty of Mechanical Engineering, Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama
    Center of Research and Innovation for Electrical, Mechanical and Industry (CINEMI), Panama City 0819-07289, Panama
    Centro de Estudios Multidisciplinarios en Ciencia, Innovación y Tecnología AIP (CEMCIT-AIP), Panama City 0819-07289, Panama
    Sistema Nacional de Investigación (SNI), Clayton 0816, Panama City 0819-07289, Panama)

Abstract

As concerns about sustainable energy solutions grow, the exploration of bio-inspired techniques for optimizing renewable energy systems becomes increasingly important. This study presents a theoretical application of bio-inspired algorithms, specifically the Particle Swarm Optimization (PSO) algorithm and the Genetic Algorithm (GA), to enhance the energy availability of a renewable energy system in an existing university building in a tropical climate. The research followed a multi-step process. First, a renewable energy generation system was designed for the building, considering available resources and space limitations. Next, we optimized both electricity production and overall energy management. Using the PSO algorithm to find the ideal combination of power generators that would fit within the available space resulted in a 10% increase in the energy deficit. Additionally, PSO was used to optimize the discharge management of the battery bank, independently demonstrating a 2% efficiency improvement when incorporated into the original pre-optimization system. These findings highlight some of the challenges with integrating renewable energy systems into existing buildings while showcasing the potential of biomimetic algorithms, like the PSO and the GA, for targeted optimization tasks. Further research is warranted to refine such algorithms and explore their tailored applications for enhancing the performance of renewable energy systems within the often-restrictive parameters of existing infrastructure.

Suggested Citation

  • Orlando Aguilar Pinzón & Orlando Aguilar Gallardo & Miguel Chen Austin, 2025. "A Bio-Optimization Approach for Renewable Energy Management: The Case of a University Building in a Tropical Climate," Energies, MDPI, vol. 18(8), pages 1-28, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2100-:d:1637610
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    References listed on IDEAS

    as
    1. Zhang, H. & Aggidis, G.A., 2018. "Nature rules hidden in the biomimetic wave energy converters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 28-37.
    2. Hui Zhang & Wanan Sheng & Zhimin Zha & George Aggidis, 2022. "A Preliminary Study on Identifying Biomimetic Entities for Generating Novel Wave Energy Converters," Energies, MDPI, vol. 15(7), pages 1-20, March.
    3. Yang, Qiangda & Dong, Ning & Zhang, Jie, 2021. "An enhanced adaptive bat algorithm for microgrid energy scheduling," Energy, Elsevier, vol. 232(C).
    4. Stevovic, Ivan & Mirjanic, Dragoljub & Petrovic, Natasa, 2021. "Integration of solar energy by nature-inspired optimization in the context of circular economy," Energy, Elsevier, vol. 235(C).
    Full references (including those not matched with items on IDEAS)

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