Ye Kang, Nan Ma, Victor Bunster, Victor W-C Chang, Jin Zhou

Published in Energy and Buildings

The Passive House Planning Package (PHPP) simulation tool is the only authorized software in Australia for the Passivhaus (PH) certification. Despite its key role in this certification scheme, previous studies have reported significant gaps between the simulated and actual performance. To address this limitation, this study introduces a bottom-up approach to building performance simulation based on a dynamic method combined with advanced appliance profiles. This study uses two-year monitored data in a PH-certified student accommodation building in Australia and validates the proposed bottom-up approach in both annual and monthly granularity. The results show that the level of reliability and robustness of the PHPP tool is substantially improved for three PH criteria (i.e., overheating frequency, space heating energy demand, and primary energy renewable) and three operational variables (i.e., indoor temperature, plug load, and lighting power density), all within the ranges of error allowed by ASHRAE’s Guideline 14–2014. Based on the proposed approach, a set of stepwise retrofit actions was put in place to further improve the energy efficiency and occupant thermal comfort in the case study building.

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https://doi.org/10.1016/j.enbuild.2022.112512

Abstract

Ye Kang, Nan Ma, Victor Bunster, Victor W-C Chang, Jin Zhou

The Passive House Planning Package (PHPP) simulation tool is the only authorized software in Australia for the Passivhaus (PH) certification. Despite its key role in this certification scheme, previous studies have reported significant gaps between the simulated and actual performance. To address this limitation, this study introduces a bottom-up approach to building performance simulation based on a dynamic method combined with advanced appliance profiles. This study uses two-year monitored data in a PH-certified student accommodation building in Australia and validates the proposed bottom-up approach in both annual and monthly granularity.

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