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

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

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.

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  • Abokersh, Mohamed Hany & Spiekman, Marleen & Vijlbrief, Olav & van Goch, T.A.J. & Vallès, Manel & Boer, Dieter, 2021. "A real-time diagnostic tool for evaluating the thermal performance of nearly zero energy buildings," Applied Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920315166
    DOI: 10.1016/j.apenergy.2020.116091
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    as
    1. Spandagos, Constantinos & Ng, Tze Ling, 2017. "Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities," Applied Energy, Elsevier, vol. 189(C), pages 352-368.
    2. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2018. "Development of a lightweight building simulation tool using simplified zone thermal coupling for fast parametric study," Applied Energy, Elsevier, vol. 223(C), pages 188-214.
    3. Hong, Tianzhen & Chang, Wen-Kuei & Lin, Hung-Wen, 2013. "A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data," Applied Energy, Elsevier, vol. 111(C), pages 333-350.
    4. Shen, Pengyuan & Lior, Noam, 2016. "Vulnerability to climate change impacts of present renewable energy systems designed for achieving net-zero energy buildings," Energy, Elsevier, vol. 114(C), pages 1288-1305.
    5. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    6. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    7. Petropoulos, G. & Wooster, M.J. & Carlson, T.N. & Kennedy, M.C. & Scholze, M., 2009. "A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation," Ecological Modelling, Elsevier, vol. 220(19), pages 2427-2440.
    8. Zhang, Sheng & Sun, Yongjun & Cheng, Yong & Huang, Pei & Oladokun, Majeed Olaide & Lin, Zhang, 2018. "Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty," Applied Energy, Elsevier, vol. 228(C), pages 1020-1031.
    9. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    10. Koo, Choongwan & Hong, Taehoon & Lee, Minhyun & Seon Park, Hyo, 2014. "Development of a new energy efficiency rating system for existing residential buildings," Energy Policy, Elsevier, vol. 68(C), pages 218-231.
    11. Bahaj, A.S. & James, P.A.B., 2007. "Urban energy generation: The added value of photovoltaics in social housing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(9), pages 2121-2136, December.
    12. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    13. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
    14. 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.
    15. 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.
    16. Hillary, Jason & Walsh, Ed & Shah, Amip & Zhou, Rongliang & Walsh, Pat, 2017. "Guidelines for developing efficient thermal conduction and storage models within building energy simulations," Energy, Elsevier, vol. 125(C), pages 211-222.
    17. Wang, Siyan & Sun, Xun & Lall, Upmanu, 2017. "A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A," Energy, Elsevier, vol. 140(P1), pages 601-611.
    18. Lu, Yuehong & Wang, Shengwei & Yan, Chengchu & Huang, Zhijia, 2017. "Robust optimal design of renewable energy system in nearly/net zero energy buildings under uncertainties," Applied Energy, Elsevier, vol. 187(C), pages 62-71.
    19. Østergård, Torben & Jensen, Rasmus L. & Maagaard, Steffen E., 2016. "Building simulations supporting decision making in early design – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 187-201.
    Full references (including those not matched with items on IDEAS)

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    3. Abokersh, Mohamed Hany & Gangwar, Sachin & Spiekman, Marleen & Vallès, Manel & Jiménez, Laureano & Boer, Dieter, 2021. "Sustainability insights on emerging solar district heating technologies to boost the nearly zero energy building concept," Renewable Energy, Elsevier, vol. 180(C), pages 893-913.
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