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Surrogate Models for Efficient Multi-Objective Optimization of Building Performance

Author

Listed:
  • 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
    Civil Engineering Research and Innovation for Sustainability, Civil 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)

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.

Suggested Citation

  • Gonçalo Roque Araújo & Ricardo Gomes & Maria Glória Gomes & Manuel Correia Guedes & Paulo Ferrão, 2023. "Surrogate Models for Efficient Multi-Objective Optimization of Building Performance," Energies, MDPI, vol. 16(10), pages 1-13, May.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4030-:d:1144484
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    References listed on IDEAS

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    1. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    2. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    3. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
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