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Large-scale building energy efficiency retrofit: Concept, model and control

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  • Wu, Zhou
  • Wang, Bo
  • Xia, Xiaohua

Abstract

BEER (Building energy efficiency retrofit) projects are initiated in many nations and regions over the world. Existing studies of BEER focus on modeling and planning based on one building and one year period of retrofitting, which cannot be applied to certain large BEER projects with multiple buildings and multi-year retrofit. In this paper, the large-scale BEER problem is defined in a general TBT (time-building-technology) framework, which fits essential requirements of real-world projects. The large-scale BEER is newly studied in the control approach rather than the optimization approach commonly used before. Optimal control is proposed to design optimal retrofitting strategy in terms of maximal energy savings and maximal NPV (net present value). The designed strategy is dynamically changing on dimensions of time, building and technology. The TBT framework and the optimal control approach are verified in a large BEER project, and results indicate that promising performance of energy and cost savings can be achieved in the general TBT framework.

Suggested Citation

  • Wu, Zhou & Wang, Bo & Xia, Xiaohua, 2016. "Large-scale building energy efficiency retrofit: Concept, model and control," Energy, Elsevier, vol. 109(C), pages 456-465.
  • Handle: RePEc:eee:energy:v:109:y:2016:i:c:p:456-465
    DOI: 10.1016/j.energy.2016.04.124
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    Cited by:

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    10. Dodoo, Ambrose & Gustavsson, Leif & Tettey, Uniben Y.A., 2017. "Final energy savings and cost-effectiveness of deep energy renovation of a multi-storey residential building," Energy, Elsevier, vol. 135(C), pages 563-576.
    11. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    12. 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.
    13. Qiong He & Md. Uzzal Hossain & S. Thomas Ng & Godfried L. Augenbroe, 2020. "Retrofitting High-Rise Residential Building in Cold and Severe Cold Zones of China—A Deterministic Decision-Making Mechanism," Sustainability, MDPI, vol. 12(14), pages 1-28, July.
    14. Iakovos T. Michailidis & Roozbeh Sangi & Panagiotis Michailidis & Thomas Schild & Johannes Fuetterer & Dirk Mueller & Elias B. Kosmatopoulos, 2020. "Balancing Energy Efficiency with Indoor Comfort Using Smart Control Agents: A Simulative Case Study," Energies, MDPI, vol. 13(23), pages 1-28, November.
    15. 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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    17. Fan, Yuling & Xia, Xiaohua, 2017. "A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance," Applied Energy, Elsevier, vol. 189(C), pages 327-335.
    18. Ki Uhn Ahn & Han Sol Shin & Cheol Soo Park, 2019. "Energy Analysis of 4625 Office Buildings in South Korea," Energies, MDPI, vol. 12(6), pages 1-16, March.
    19. Thrampoulidis, Emmanouil & Hug, Gabriela & Orehounig, Kristina, 2023. "Approximating optimal building retrofit solutions for large-scale retrofit analysis," Applied Energy, Elsevier, vol. 333(C).
    20. Mei, Jun & Xia, Xiaohua & Song, Mengjie, 2018. "An autonomous hierarchical control for improving indoor comfort and energy efficiency of a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 221(C), pages 450-463.

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