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A simulation and optimisation methodology for choosing energy efficiency measures in non-residential buildings

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  • Ceballos-Fuentealba, Irlanda
  • Álvarez-Miranda, Eduardo
  • Torres-Fuchslocher, Carlos
  • del Campo-Hitschfeld, María Luisa
  • Díaz-Guerrero, John

Abstract

The global stock of buildings account for more than 40% of global energy consumption. Improving their energy behaviour thus offers tremendous potential for promoting sustainable development. While new buildings can be benefited from new construction methods and techniques for ensuring a sustainable operation, a sustainable operation of existing buildings is only possible by retrofitting. However, the later represent the larger portion of the total stock, so effective retrofitting is fundamental for global improvement of energy efficiency. This article develops a methodological framework for predicting (i) the energy consumed in heating and cooling an existing commercial or institutional building, and (ii) the potential impact of different energy conservation measures that could be implemented on a given building. The proposed tool incorporates a simulation model and an algorithm strategy for parameter optimization. The framework is implemented in the JAVA programming language and evaluated in a case study of a 500 [m2] institutional building located in Puerto Montt, Chile. The results of this implementation show that the tool is competitive with the state-of-the art commercial simulation tool DesignBuilder. More importantly, it successfully estimated the savings obtained from different combinations of energy conservation measures for the building and proved to be computationally efficient, the algorithm requiring only 2.5 h to complete the simulation.

Suggested Citation

  • Ceballos-Fuentealba, Irlanda & Álvarez-Miranda, Eduardo & Torres-Fuchslocher, Carlos & del Campo-Hitschfeld, María Luisa & Díaz-Guerrero, John, 2019. "A simulation and optimisation methodology for choosing energy efficiency measures in non-residential buildings," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s030626191931640x
    DOI: 10.1016/j.apenergy.2019.113953
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    4. Wei, Shuangyu & Tien, Paige Wenbin & Calautit, John Kaiser & Wu, Yupeng & Boukhanouf, Rabah, 2020. "Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method," Applied Energy, Elsevier, vol. 277(C).
    5. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Hoare, Cathal & Purcell, Karl & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings," Applied Energy, Elsevier, vol. 267(C).
    6. Prokop, Viktor & Gerstlberger, Wolfgang & Zapletal, David & Gyamfi, Solomon, 2023. "Do we need human capital heterogeneity for energy efficiency and innovativeness? Insights from European catching-up territories," Energy Policy, Elsevier, vol. 177(C).
    7. Esmaeilzadeh, Ahmad & Deal, Brian & Yousefi-Koma, Aghil & Zakerzadeh, Mohammad Reza, 2023. "How combination of control methods and renewable energies leads a large commercial building to a zero-emission zone – A case study in U.S," Energy, Elsevier, vol. 263(PD).
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