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Research Article

Energy positive curtain wall configurations for a cold climate using the Analysis of Variance (ANOVA) approach

T C LamHua Ge( )Paul Fazio
Department of Building, Civil and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. W. Montreal, Quebec, H3G 1M8, Canada
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Abstract

Curtain walls are believed to be "energy sinks" because of their relatively low thermal performance; however, the integration of energy generating technologies such as photovoltaic (PV) panels may enable converting these systems to "energy positive" curtain walls. A methodology using the Analysis of Variance (ANOVA) approach is developed and implemented to identify configurations of energy positive curtain walls by accounting for the complex interacting effect of facade design parameters. The "energy positive" curtain wall in this paper is defined as the energy generated by the curtain wall facade on an annual basis exceeds the energy consumption of a perimeter zone office enclosed by this curtain wall facade. Ten design parameters are studied, including glazing U-value, solar heat gain coefficient (SHGC), and visible transmittance (Tv); U-value of the spandrel panel; U-value of the mullion; window wall ratio (WWR); infiltration rate; depth and inclination of overhang; and efficiency of PV modules. The significance of individual design parameters on the energy performance is ranked for four cardinal orientations based on the total sensitivity index. The WWR, U-glazing, and infiltration rate are the three most significant parameters influencing the total annual energy consumption of the office unit simulated, while the WWR, PV efficiency, and U-glazing are the most significant design parameters for achieving energy positive curtain walls. The methodology presented in this paper helps facilitate the design process to resolve the issues with conflicting effects of design parameters.

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Building Simulation
Pages 297-310
Cite this article:
Lam TC, Ge H, Fazio P. Energy positive curtain wall configurations for a cold climate using the Analysis of Variance (ANOVA) approach. Building Simulation, 2016, 9(3): 297-310. https://doi.org/10.1007/s12273-016-0275-6

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Received: 02 October 2015
Revised: 14 December 2015
Accepted: 30 December 2015
Published: 29 January 2016
© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2016
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