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Effect of window-to-wall ratio on measured energy consumption in US office buildings

Troup, Luke ; Phillips, Robert ; Eckelman, Matthew J. ; Fannon, David

Energy and buildings, 2019-11, Vol.203, p.109434, Article 109434 [Periódico revisado por pares]

Lausanne: Elsevier B.V

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  • Título:
    Effect of window-to-wall ratio on measured energy consumption in US office buildings
  • Autor: Troup, Luke ; Phillips, Robert ; Eckelman, Matthew J. ; Fannon, David
  • Assuntos: building energy use ; Buildings ; CBECS (commercial building energy consumption survey) ; Commercial buildings ; Computer simulation ; Construction ; Cooling ; Cooling loads ; Cooling systems ; Energy consumption ; Energy measurement ; Goodness of fit ; Iterative methods ; Lighting ; Office buildings ; Optimization ; Polls & surveys ; Regression analysis ; Statistical analysis ; Ventilation ; Window-to-wall ratio (WWR)
  • É parte de: Energy and buildings, 2019-11, Vol.203, p.109434, Article 109434
  • Descrição: •Area of fenestration is a poor predictor of measured energy use in US office buildings•Descriptive statistics suggest average total EUI increases with greater WWR•Total EUI has low goodness of fit, militating for end-use analysis of CBECS data•The most pronounced effect of varying WWR in US office buildings is cooling energy•Window to wall ratio is only a statistically-significant predictor for cooling energy Windows can significantly affect building performance, and the window-to-wall ratio (WWR) describes the portion of an exterior wall that consists of windows. Previous research on the effects of WWR and efforts to determine optimal WWR generally rely on iterative simulation or algorithmic optimization. This study seeks to understand the effects of WWR in actual office buildings using survey data reported in the 2012 CBECS (Commercial Building Energy Consumption Survey). Both total annual energy use and four discrete end-uses (heating, cooling, lighting and ventilation) were characterized, and 32 categorical and numerical building characteristic variables were selected for linear regression analysis. Descriptive statistics for energy use intensity (EUI) in ∼1000 office buildings across 6 WWR intervals show increased median total EUI with increasing fenestration, driven by increasing cooling loads. Total EUI generally decreases with year of construction, regardless of WWR. Single-variable regression finds consistent statistical significance for WWR on cooling, lighting, and ventilation energy use, but with a maximum goodness of fit R2=0.04. In contrast, the single variable with the largest explanatory power is cooling degree days (R2=0.22 for cooling energy use). Multi-regression modeling finds a maximum R2 value of 0.34, with WWR appearing as a significant variable in the regression equations for cooling and lighting. In sum, the 2012 CBECS microdata for office buildings suggests that WWR is a significant predictor of energy use for cooling, and to a lesser extent lighting and ventilation, but to a much lower degree than has been found by purely simulation studies.
  • Editor: Lausanne: Elsevier B.V
  • Idioma: Inglês
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