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A framework for using calibrated campus-wide building energy models for continuous planning and greenhouse gas emissions reduction tracking

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  • Nagpal, Shreshth
  • Hanson, Jared
  • Reinhart, Christoph

Abstract

Physics-based building energy models, once calibrated to historic energy data, are increasingly used to explore energy efficiency retrofits. When utilized for large building portfolios, such as university campuses, these calibrated models require substantial initial effort to develop, but are then typically only used once for analyzing potential building upgrades and estimating carbon reduction opportunities. This paper presents a continuous energy performance planning and tracking system for a university campus that automatically updates and compares measured against simulated building energy use for a hundred campus buildings. The system archives historic data, enables exploration of potential upgrade scenarios, and allows for the documentation of energy retrofits to individual buildings. The objectives of this tracking system are to support facility managers to ensure that their buildings are performing as intended, financial administrators to quantify potential energy savings and payback times of building upgrades and the overall university community to track campus-wide carbon emissions from buildings vis-à-vis previously defined reduction targets. A proof-of-concept implementation is presented for the authors’ campus in Cambridge, Massachusetts.

Suggested Citation

  • Nagpal, Shreshth & Hanson, Jared & Reinhart, Christoph, 2019. "A framework for using calibrated campus-wide building energy models for continuous planning and greenhouse gas emissions reduction tracking," Applied Energy, Elsevier, vol. 241(C), pages 82-97.
  • Handle: RePEc:eee:appene:v:241:y:2019:i:c:p:82-97
    DOI: 10.1016/j.apenergy.2019.03.010
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    1. Cerezo Davila, Carlos & Reinhart, Christoph F. & Bemis, Jamie L., 2016. "Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets," Energy, Elsevier, vol. 117(P1), pages 237-250.
    2. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    3. Yang, Zheng & Becerik-Gerber, Burcin, 2015. "A model calibration framework for simultaneous multi-level building energy simulation," Applied Energy, Elsevier, vol. 149(C), pages 415-431.
    4. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    5. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
    6. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.
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    Cited by:

    1. Ehsan Kamel, 2022. "A Systematic Literature Review of Physics-Based Urban Building Energy Modeling (UBEM) Tools, Data Sources, and Challenges for Energy Conservation," Energies, MDPI, vol. 15(22), pages 1-24, November.
    2. Pasichnyi, Oleksii & Wallin, Jörgen & Kordas, Olga, 2019. "Data-driven building archetypes for urban building energy modelling," Energy, Elsevier, vol. 181(C), pages 360-377.
    3. Zhu, Chuanqi & Tian, Wei & Yin, Baoquan & Li, Zhanyong & Shi, Jiaxin, 2020. "Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms," Applied Energy, Elsevier, vol. 268(C).
    4. Sumiani Yusoff & Azizi Abu Bakar & Mohd Fadhli Rahmat Fakri & Aireen Zuriani Ahmad, 2021. "Sustainability initiative for a Malaysian university campus: living laboratories and the reduction of greenhouse gas emissions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 14046-14067, September.
    5. Song, Jeonghun & Song, Seung Jin, 2020. "A framework for analyzing city-wide impact of building-integrated renewable energy," Applied Energy, Elsevier, vol. 276(C).
    6. Chen, Yixing & Deng, Zhang & Hong, Tianzhen, 2020. "Automatic and rapid calibration of urban building energy models by learning from energy performance database," Applied Energy, Elsevier, vol. 277(C).
    7. Younghoon Kwak & Jeonga Kang & Sun-Hye Mun & Young-Sun Jeong & Jung-Ho Huh, 2020. "Development and Application of a Flexible Modeling Approach to Reference Buildings for Energy Analysis," Energies, MDPI, vol. 13(21), pages 1-22, November.
    8. Zhang, Wei & Valencia, Andrea & Gu, Lixing & Zheng, Qipeng P. & Chang, Ni-Bin, 2020. "Integrating emerging and existing renewable energy technologies into a community-scale microgrid in an energy-water nexus for resilience improvement," Applied Energy, Elsevier, vol. 279(C).
    9. Li, Ruishi & Zhao, Rongqin & Xie, Zhixiang & Xiao, Liangang & Chuai, Xiaowei & Feng, Mengyu & Zhang, Huifang & Luo, Huili, 2022. "Water–energy–carbon nexus at campus scale: Case of North China University of Water Resources and Electric Power," Energy Policy, Elsevier, vol. 166(C).
    10. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
    11. Cho, Hyun Mi & Yun, Beom Yeol & Yang, Sungwoong & Wi, Seunghwan & Chang, Seong Jin & Kim, Sumin, 2020. "Optimal energy retrofit plan for conservation and sustainable use of historic campus building: Case of cultural property building," Applied Energy, Elsevier, vol. 275(C).
    12. Zhang, Qiang & Tian, Zhe & Ma, Zhijun & Li, Genyan & Lu, Yakai & Niu, Jide, 2020. "Development of the heating load prediction model for the residential building of district heating based on model calibration," Energy, Elsevier, vol. 205(C).
    13. Georgios Tsoumanis & João Formiga & Nuno Bilo & Panagiotis Tsarchopoulos & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "The Smart Evolution of Historical Cities: Integrated Innovative Solutions Supporting the Energy Transition while Respecting Cultural Heritage," Sustainability, MDPI, vol. 13(16), pages 1-29, August.

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