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Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level

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  • Glasgo, Brock
  • Hendrickson, Chris
  • Azevedo, Inês Lima

Abstract

Building energy simulation tools are now being used in a number of new roles such as building operation optimization, performance verification for efficiency programs, and – recently – building energy code analysis, design, and compliance verification in the residential sector. But increasing numbers of studies show major differences between the results of these simulations and the actual measured performance of the buildings they are intended to model. The accuracy and calibration of building simulations have been studied extensively in the commercial sector, but these new applications have created a need to better understand the performance of home energy simulations.

Suggested Citation

  • Glasgo, Brock & Hendrickson, Chris & Azevedo, Inês Lima, 2017. "Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level," Applied Energy, Elsevier, vol. 203(C), pages 348-363.
    • Handle: RePEc:eee:appene:v:203:y:2017:i:c:p:348-363
    DOI: 10.1016/j.apenergy.2017.05.164
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    References listed on IDEAS

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    1. Li, Nan & Yang, Zheng & Becerik-Gerber, Burcin & Tang, Chao & Chen, Nanlin, 2015. "Why is the reliability of building simulation limited as a tool for evaluating energy conservation measures?," Applied Energy, Elsevier, vol. 159(C), pages 196-205.
    2. 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.
    3. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
    4. Lin, Hung-Wen & Hong, Tianzhen, 2013. "On variations of space-heating energy use in office buildings," Applied Energy, Elsevier, vol. 111(C), pages 515-528.
    5. Loutzenhiser, Peter G. & Maxwell, Gregory M. & Manz, Heinrich, 2007. "An empirical validation of the daylighting algorithms and associated interactions in building energy simulation programs using various shading devices and windows," Energy, Elsevier, vol. 32(10), pages 1855-1870.
    6. 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.
    7. Mustafaraj, Giorgio & Marini, Dashamir & Costa, Andrea & Keane, Marcus, 2014. "Model calibration for building energy efficiency simulation," Applied Energy, Elsevier, vol. 130(C), pages 72-85.
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    Citations

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    Cited by:

    1. Glasgo, Brock & Khan, Nyla & Azevedo, Inês Lima, 2020. "Simulating a residential building stock to support regional efficiency policy," Applied Energy, Elsevier, vol. 261(C).
    2. Michel Noussan & Benedetto Nastasi, 2018. "Data Analysis of Heating Systems for Buildings—A Tool for Energy Planning, Policies and Systems Simulation," Energies, MDPI, vol. 11(1), pages 1-15, January.
    3. Vicente Gutiérrez González & Lissette Álvarez Colmenares & Jesús Fernando López Fidalgo & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Uncertainy’s Indices Assessment for Calibrated Energy Models," Energies, MDPI, vol. 12(11), pages 1-18, May.
    4. Im, Piljae & Joe, Jaewan & Bae, Yeonjin & New, Joshua R., 2020. "Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season," Applied Energy, Elsevier, vol. 261(C).
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    6. Eskander, Monica M. & Silva, Carlos A., 2023. "Techno-economic and environmental comparative analysis for DC microgrids in households: Portuguese and French household case study," Applied Energy, Elsevier, vol. 349(C).

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