Nowadays, the large set of available simulation tools brings numerous benefits to urban and architectural practices. However, simulations often take a considerable amount of time to yield significant results, particularly when performing many simulations and with large models, as is typical in complex urban and architectural endeavors. Additionally, multiple objective optimizations with metaheuristic algorithms have been widely used to solve building optimization problems. However, most of these optimization processes exponentially increase the computational time to correctly produce outputs and require extensive knowledge to interpret results. Thus, building optimization with time-consuming simulation tools is often rendered unfeasible and requires a specific methodology to overcome these barriers. This work integrates a baseline multi-objective optimization process with a widely used, validated building energy simulation tool. The goal is to minimize the energy use and cost of the construction of a residential building complex. Afterward, machine learning and optimization techniques are used to create a surrogate model capable of accurately predicting the simulation results. Finally, different metaheuristics with their tuned hyperparameters are compared. Results show significant improvements in optimization results with a decrease of up to 22% in the total cost while having similar performance results and execution times up to 100 times faster.

Passive design strategies aim at creating comfortable buildings while reducing their energy consumption. Simple strategies such as the correct orientation of the building, the correct design of the building's openings, or the correct sizing of the building's constituent parts (such as walls, ceilings, etc.) according to their material specificities (regarding the region's climate) would greatly improve the performance of a building. This paper intends to show how the application of simple passive techniques in housing design can reduce the energy loads spent annually and improve the house's interior comfort. It translates the results of interior comfort computer simulations into suggestions for the application of passive strategies in housing design, to improve the living conditions in Southern Angola.