Optimizing building envelope design across various French climates: A Multi- objective approach using NSGA II and RMP method

Architects face a major challenge in designing buildings that enhance human comfort while minimizing energy consumption. To address this, the present work presents a novel multi-objective optimization approach, aiming to determine the optimal building envelope design. The developed approach focuses on minimizing energy consumption for both heating and cooling demand. Therefore, the methodology follows three basic steps, namely, (i) energy simulation using EnergyPlus software, (ii) optimization (non-dominant sorting genetic algorithm NSGA II), and (iii) multi-criteria decision analysis (MCDA). In this study, simulations were firstly carried out on a single-story family house located in three distinct French climates: Nancy (semi-continental climate), Embrun (oceanic climate), and Nice (Mediterranean climate) in order to analyze the effect of climate on optimization results. For each site, seven decision variables for optimization problem are considered, including, wall insulation material properties, building rotation, glazing type, window-to-wall ratio (WWR), heating and cooling set-point temperatures, and infiltration rate. Pareto fronts are used to display the optimization results, which are obtained through NSGA II iterations depending on the specified optimization objectives of heating and cooling demand. The optimization (after 900 iterations) yielded 10 Pareto solutions for Nancy, 26 for Embrun, and 23 for Nice. The top-ranked Pareto-optimal solution, considering the requirements of the French building code (RT2012/RE2020), Passivhaus standard, and client preferences, which minimizes cooling, heating, and total energy consumption, was selected using a new MCDA method called ranking with multiple reference profiles (RMP). The results showed that when equal weight is given to the heating and cooling objective functions, total energy consumption decreases by 8.71% for Nancy, by 9.81% to 18.34% for Embrun, and by 6.52% to 21.7% for Nice, compared to the initial design, depending on the adopted lexicographic order.