A real-time diagnostic tool for evaluating the thermal performance of nearly zero energy buildings

Mohamed Hany Abokersh, Marleen Spiekman, Olav Vijlbrief, T.A.J. van Goch, Manel Vallès and Dieter Boer

Applied Energy, 2021, vol. 281, issue C, No S0306261920315166

Abstract: The nearly zero-energy buildings (nZEB) presents a promising contribution to fulfill the EU sustainable future targets. However, the construction industry that leads the development of nZEB is facing challenges to guarantee its performance. In this context, this paper proposes a methodology framework based on Multizone Resistance–Capacitance Model to trace the nZEB performance challenges with quantifications for the time-dependent variables comprising occupant behaviors as well as the dynamic behavior of weather conditions and building operations. This approach incorporates Bayesian optimization for calibration purposes to minimize the required monitoring data. Moreover, the proposed framework integrates the uncertainty analysis (UA) with two-step global sensitivity analysis (GSA) in order to quantify and assess the uncertainty associate with the performance of the developed digital dwelling. The methodology application is demonstrated through a case study for a newly renovated two-story dwelling located in a district of Emmen at the Netherlands. The results confirm a high accuracy for the digital dwelling performance where the model offers a prediction accuracy of 2.2% and 7.03% for the thermal energy consumption and indoor zone temperature, respectively. On the other hand, the UA confirms a high uncertainty associate with the nZEB performance where the total thermal energy consumption can increase up to 100 kWh/m2/yr. This variation is driven by the infiltration rates followed by the building envelope characteristics. The proposed framework can serve a diagnostic tool to assist the construction and installation companies to maintain the performance of their products proactively.

Keywords: Nearly zero energy building; Building performance simulation; Occupant behaviors; Bayesian calibration; Monte Carlo uncertainty analysis; Global sensitivity analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.apenergy.2020.116091

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