Building energy regulations have been developed to reduce impacts on climate change. As they require quantifying energy use, simulation tools can help designers and decision makers improve buildings' energetical and environmental aspects. Decision making is a complex process in the early design stage of a high-performance building, as there are several variables to be considered that may be contradictory to each other. In this context, Building Energy Simulation and Optimization (BESO) technique can generate many alternatives for solving a problem, achieving optimal solutions from energy efficiency and thermal comfort criteria. Some studies present important achievements in optimization methods for sustainable building design. Genetic algorithm, for example, is a popular method based on the natural selection of living organisms that optimizes an objective function to select the most suitable solutions for a given problem. However, some challenges regarding BESO include validating the method for conditioned and mixed-mode buildings and optimizing the use of passive bioclimatic strategies for complex commercial buildings. This study aims at developing an optimization procedure with genetic algorithms in a BESO process to automatically simulate conditioned and mixed-mode office buildings archetypes using passive bioclimatic strategies to satisfy energy consumption and thermal comfort criteria. The procedure couples MATLAB's Optimization Toolbox and EnergyPlus simulation software. The methodology is divided into three stages: a pre-processing stage to run simulations for different office buildings archetypes, an optimization stage for defining the genetic algorithm settings and optimizing passive bioclimatic strategies use on the models and finally, the post-processing stage to analyze the results based on energy consumption and thermal comfort criteria. Results from this research will contribute to the development of a BESO technique for high-performance office buildings in different Brazilian climate regions using genetic algorithms to optimize passive design measures.
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