Advanced search
Start date
Betweenand

Method for evaluating the thermal energy performance in office buildings with genetic algorithms

Grant number: 16/21667-1
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): January 01, 2017
Effective date (End): February 29, 2020
Field of knowledge:Applied Social Sciences - Architecture and Town Planning - Architecture and Urbanism Technology
Principal researcher:Lucila Chebel Labaki
Grantee:Felipe da Silva Duarte Lopes
Home Institution: Faculdade de Engenharia Civil, Arquitetura e Urbanismo (FEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated scholarship(s):18/14291-0 - Coupling EnergyPlus and Matlab to optimize energy efficiency and thermal comfort in office buildings design using genetic algorithm, BE.EP.DR

Abstract

The energy demand in buildings has increased considerably in recent years, and Brazilian non-residential buildings already account for a quarter of the country's total electricity consumption. Many of these buildings are considered efficient by certification and energy labeling programs, despite their high-energy consumption and little environmental conditioning strategies. In the architectural design process, there are several variables to be considered that may be contradictory to each other, and computational optimization methods represent a significant potential to generate solutions for a good design. In this context, genetic algorithms are mechanisms to solve highly complex problems, obtaining the best solutions satisfactorily. Thus, this research aims to develop a predictive method with genetic algorithms for the thermal-energetic performance in office buildings. Initially usual office buildings typologies in Brazil will be defined, and there will be conducted computer simulations using EnergyPlus. Passive and active bioclimatic strategies, and renewable energy sources will be applied in each simulated model and automated with Matlab, a data processing routines software, with a tool for optimizing with genetic algorithms. Then parametric variations will be performed using the Rhinoceros-Grasshopper platform on the selected models from the Matlab simulations, such as the building's form factor, it's site location and the relation to surroundings, to establish optimal solutions for energy consumption and thermal comfort for each model. The results will be evaluated by comfort performance indices (predicted mean vote and adaptive model), and annual energy consumption, as well as a sensitivity analysis to evaluate the uncertainties in the simulations. This research intends to develop a method to assist architects and other designers in energy-efficiency projects, with lower energy consumption and greater environmental comfort for users, as well as defining the most suitable bioclimatic strategies in office buildings.