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

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.