Multi-objective optimization of the solar orientation of two residential multifamily buildings in south Brazil

[Display omitted] •A low-cost energy demand optimization strategy is analyzed.•Optimizes heating demand, without neglecting cooling, for colder climates.•Simplified optimization is used to reduce computational costs.•The optimization method was developed in open-access software.•Impact of the urban context inserted into the optimization process.•Demonstrates energy discrimination in the same building. The shape and orientation of a building influence the energy demand, therefore optimal decisions should only be made rigorously supported by energy evaluation programs, which allow for measuring the energy demand of a building more precisely. The main purpose of this research is to evaluate the shape and orientation of massive residential social housing multifamily buildings to find the best solar positioning to minimize cooling and heating demands simultaneously in the bioclimatic zone 2 (Cfa) in the southern region of Brazil. To do this, this study utilizes multi-objective optimization with a genetic algorithm (NSGA-II) simulating the thermal behavior in EnergyPlus and performing the optimization with a Python language programming code, totalizing 80,000 simulations. The main results showed that solar orientation optimization could reduce the total demand by 4% for the “H” shape and 22% for linear buildings in the isolated scenario. For the condominium condition, the reduction is 2% for the “H” typology and 8% for the linear shape. The results presented can help engineers and architects to design more energy-efficient buildings and address the energetic vulnerability in the same building. Moreover, future work can be carried out to improve the constructive pattern replicated all over the country, improving the surroundings.