Surrogate Models for Efficient Multi-Objective Optimization of Building Performance

Gonçalo Roque Araújo, Ricardo Gomes, Maria Glória Gomes (), Manuel Correia Guedes and Paulo Ferrão
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Gonçalo Roque Araújo: Center for Innovation, Technology and Policy Research, Mechanical Engineering Department, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
Ricardo Gomes: Center for Innovation, Technology and Policy Research, Mechanical Engineering Department, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
Maria Glória Gomes: Civil Engineering Research and Innovation for Sustainability, Civil Engineering Department, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
Manuel Correia Guedes: Center for Innovation in Territory, Urbanism, and Architecture, Civil Engineering Department, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
Paulo Ferrão: Center for Innovation, Technology and Policy Research, Mechanical Engineering Department, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal

Energies, 2023, vol. 16, issue 10, 1-13

Abstract: 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.

Keywords: building optimization; building simulation; surrogate models; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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