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An exergy-based multi-objective optimisation model for energy retrofit strategies in non-domestic buildings

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  • García Kerdan, Iván
  • Raslan, Rokia
  • Ruyssevelt, Paul

Abstract

While the building sector has a significant thermodynamic improvement potential, exergy analysis has been shown to provide new insight for the optimisation of building energy systems. This paper presents an exergy-based multi-objective optimisation tool that aims to assess the impact of a diverse range of retrofit measures with a focus on non-domestic buildings. EnergyPlus was used as a dynamic calculation engine for first law analysis, while a Python add-on was developed to link dynamic exergy analysis and a Genetic Algorithm optimisation process with the aforementioned software. Two UK archetype case studies (an office and a primary school) were used to test the feasibility of the proposed framework. Different measures combinations based on retrofitting the envelope insulation levels and the application of different HVAC configurations were assessed. The objective functions in this study are annual energy use, occupants' thermal comfort, and total building exergy destructions. A large range of optimal solutions was achieved highlighting the framework capabilities. The model achieved improvements of 53% in annual energy use, 51% of exergy destructions and 66% of thermal comfort for the school building, and 50%, 33%, and 80% for the office building. This approach can be extended by using exergoeconomic optimisation.

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  • García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul, 2016. "An exergy-based multi-objective optimisation model for energy retrofit strategies in non-domestic buildings," Energy, Elsevier, vol. 117(P2), pages 506-522.
    • Handle: RePEc:eee:energy:v:117:y:2016:i:p2:p:506-522
    DOI: 10.1016/j.energy.2016.06.041
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    5. Tian Zhao & Di Liu & Ke-Lun He & Xi Chen & Qun Chen, 2020. "An Integrated Three-Level Synergetic and Reliable Optimization Method Considering Heat Transfer Process, Component, and System," Energies, MDPI, vol. 13(16), pages 1-19, August.
    6. Harkouss, Fatima & Fardoun, Farouk & Biwole, Pascal Henry, 2018. "Passive design optimization of low energy buildings in different climates," Energy, Elsevier, vol. 165(PA), pages 591-613.
    7. Maccarini, Alessandro & Sotnikov, Artem & Sommer, Tobias & Wetter, Michael & Sulzer, Matthias & Afshari, Alireza, 2023. "Influence of building heat distribution temperatures on the energy performance and sizing of 5th generation district heating and cooling networks," Energy, Elsevier, vol. 275(C).
    8. García Kerdan, Iván & Morillón Gálvez, David, 2020. "Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building," Applied Energy, Elsevier, vol. 280(C).
    9. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    10. Seyedzadeh, Saleh & Pour Rahimian, Farzad & Oliver, Stephen & Rodriguez, Sergio & Glesk, Ivan, 2020. "Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making," Applied Energy, Elsevier, vol. 279(C).
    11. Richarz, Jan & Henn, Sarah & Osterhage, Tanja & Müller, Dirk, 2022. "Optimal scheduling of modernization measures for typical non-residential buildings," Energy, Elsevier, vol. 238(PA).
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