Achieving thermal comfort in social housing under variable and changing climates presents a critical challenge for sustainable building design and energy efficiency. This study develops a simulation-based multi-objective optimization framework to support early-stage design of climate-resilient social housing in Brazil, aiming to reduce thermal discomfort and associated mechanical conditioning energy demands. The goal is to identify building envelope configurations that minimize total construction cost while maximizing annual thermal comfort hours, thereby reducing the need for active heating and cooling systems. A reference single-room prototype is simulated in EnergyPlus for five cities representing distinct climatic zones. A wide range of construction alternatives for walls, roofs, slabs, and glazing are evaluated, with costs derived from the national SINAPI database and comfort assessed using the ASHRAE adaptive model based on operative temperature. The optimization, performed with the NSGA-II algorithm (via PyMOO), generates city-specific Pareto fronts that quantify the inherent trade-off between cost and comfort. Results show that optimal solutions range from approximately R4800 to R8900 in cost, achieving between 1350 and 3550 annual comfort hours, heavily influenced by local climate. Frequency analysis reveals that wall and roof assemblies are the most influential design variables. The proposed framework provides a transparent, data-driven decision-support tool for defining cost-effective, climate-adapted construction standards, contributing directly to sustainable housing policy, energy poverty reduction, and the development of resilient, low-carbon built environments aligned with the UN Sustainable Development Goals (SDG), particularly SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).
Alves et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: