Optimizing Energy and Cost Performance in Residential Buildings: A Multi-Objective Approach Applied to the City of Patras, Greece

Dionyssis Makris, Anastasia Antzoulatou, Alexandros Romaios, Sonia Malefaki, John A. Paravantis, Athanassios Giannadakis and Giouli Mihalakakou ()
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Dionyssis Makris: Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece
Anastasia Antzoulatou: Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece
Alexandros Romaios: Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece
Sonia Malefaki: Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece
John A. Paravantis: Department of International and European Studies, University of Piraeus, 80 Karaoli and Dimitriou Street, 18534 Piraeus, Greece
Athanassios Giannadakis: Department of Mechanical Engineering, University of Peloponnese, M. Alexandrou 1, 26334 Patras, Greece
Giouli Mihalakakou: Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece

Energies, 2025, vol. 18, issue 13, 1-20

Abstract: Improving the energy efficiency of buildings is a critical pathway in mitigating greenhouse gas emissions and fostering sustainable urban development. This study introduces a simulation-based multi-objective optimization framework designed to enhance both the thermal and economic performance of residential buildings. A representative single-family dwelling located in Patras, Greece, served as a case study to demonstrate the application and scalability of the proposed methodology. The optimization simultaneously minimized two conflicting objectives: the building’s annual thermal energy demand and the cost of construction materials. The computational process was implemented using MATLAB’s Multi-Objective Genetic Algorithm, supported by a modular Excel interface that enables the dynamic customization of design parameters and climatic inputs. A parametric analysis across four optimization scenarios was conducted by systematically varying the key algorithmic hyperparameters—population size, mutation rate, and number of generations—to assess their impact on convergence behavior, Pareto front resolution, and solution diversity. The results confirmed the algorithm’s robustness in producing technically feasible and non-dominated solutions, while also highlighting the sensitivity of optimization outcomes to hyperparameter tuning. The proposed framework is a flexible, reproducible, and computationally tractable approach to supporting early-stage, performance-driven building design under realistic constraints.

Keywords: energy efficient buildings; multi-objective optimization; simulation-based optimization; Pareto front; energy consumption in buildings (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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