Abstract

The demand for air conditioning and heating in the construction sector has increased in about two decades, mainly because of demographic and economic growth. The result of this increase in energy consumption has been more greenhouse gas emissions (GHGs). This paper proposes a multi-objective optimization design that helps reduce heating, ventilation, and air-conditioning (HVAC) energy consumption and increases the thermal comfort levels in low-income housing. Achieving these objectives required choosing a dwelling unit that is representative of the Chilean housing stock, which was identified with the help of the National Housing Monitoring Network (ReNaM). The selected optimization variables correspond to the type of windows, type and thickness of ceiling insulation, and type of PCM-impregnated Pinus radiata wood panel, with PCM melting points between 8 and 27 degrees C. Subsequently, we minimized the hours of thermal discomfort and the dwelling typology's electrical consumption of heating and cooling by using the algorithm the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The main results include a reduction of up to 21.5 % in the thermal discomfort hours and a decrease of up to 80.5 % in energy consumption associated with the optimal housing typology compared to the base case. Additionally, the economic analysis indicated the payback period is 12.9 years for the Pareto solution, which presents a lower distance to the utopic point (UP).