In the heart of Greece, a city known for its rich history and vibrant culture, a new chapter is being written in the realm of sustainable construction. Patras, a coastal city in the western Peloponnese, is serving as the testing ground for a groundbreaking approach to residential building design that could reshape the energy sector. At the helm of this innovative research is Dionyssis Makris, an assistant professor in the Department of Mechanical Engineering and Aeronautics at the University of Patras.
Makris and his team have developed a simulation-based multi-objective optimization framework that aims to strike the perfect balance between thermal efficiency and construction costs in residential buildings. “Our goal is to create a tool that supports early-stage, performance-driven building design under realistic constraints,” Makris explains. This tool, he believes, could be a game-changer for the energy sector, offering a flexible and reproducible approach to optimizing energy performance in buildings.
The research, published in the journal Energies (which translates to “Energies” in English), introduces a methodology that simultaneously minimizes two conflicting objectives: the building’s annual thermal energy demand and the cost of construction materials. This is no small feat, as these two factors often pull in opposite directions, making the optimization process complex and challenging.
To tackle this complexity, the team employed MATLAB’s Multi-Objective Genetic Algorithm, supported by a modular Excel interface. This interface allows for dynamic customization of design parameters and climatic inputs, making the tool adaptable to various scenarios and locations. “We wanted to create a user-friendly interface that enables architects and engineers to easily input their design parameters and see the results,” Makris says.
The team conducted a parametric analysis across four optimization scenarios, systematically varying key algorithmic hyperparameters such as population size, mutation rate, and number of generations. This analysis assessed the impact of these parameters on convergence behavior, Pareto front resolution, and solution diversity. The results confirmed the algorithm’s robustness in producing technically feasible and non-dominated solutions, highlighting the sensitivity of optimization outcomes to hyperparameter tuning.
So, what does this mean for the future of the energy sector? The proposed framework could significantly enhance the energy efficiency of residential buildings, leading to reduced energy consumption and lower greenhouse gas emissions. This is a critical pathway in mitigating climate change and fostering sustainable urban development.
Moreover, the tool could have significant commercial impacts. By optimizing construction costs alongside thermal performance, it could help developers and builders create more affordable, energy-efficient homes. This could drive demand for energy-efficient materials and technologies, creating new opportunities for businesses in the energy sector.
The research also paves the way for future developments in the field. The team’s approach could be applied to other types of buildings and locations, further expanding its potential impact. Additionally, the tool could be integrated with other software and technologies, such as building information modeling (BIM) systems, to create a more comprehensive and powerful design tool.
In the words of Makris, “This is just the beginning. We see great potential in this approach and are excited to explore its applications further.” As the world grapples with the challenges of climate change and sustainable development, this research offers a beacon of hope and a promising path forward.