Validation of an inverse model of zone air heat balance

Lee, Sang Hoon; Hong, Tianzhen

doi:10.1016/j.buildenv.2019.106232

Title: Validation of an inverse model of zone air heat balance

Journal Article · 27 June 2019 · Building and Environment

DOI: https://doi.org/10.1016/j.buildenv.2019.106232 · OSTI ID:1572834

Lee, Sang Hoon ^[1];

^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Our work introduces the validation method and results of an inverse model of zone air heat balance. The inverse model, implemented in EnergyPlus and published in a previous article [1], calculates highly uncertain model parameters such as internal thermal mass and infiltration airflow by inversely solving the zone air heat balance equation using the easy-to-measure zone air temperature data. The paper provides technical details of validation from the experiments using LBNL's Facility for Low Energy eXperiment in Buildings (FLEXLAB) that measures zone air temperature under the controlled experiment of two levels of internal mass and four levels of infiltration airflow. The simulation results of the zone infiltration airflow and internal thermal mass from the inverse model agree well with the measured data from the FLEXLAB experiments. The validated inverse model in EnergyPlus can be utilized to enhance the energy modeling of existing buildings that enables energy performance assessments for energy efficiency improvements.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1572834

Journal Information:: Building and Environment, Journal Name: Building and Environment Journal Issue: C Vol. 161; ISSN 0360-1323

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (25)

Air Change Measurements Using Tracer Gases: Methods and Results. Significance of air change for indoor air quality Laußmann, Detlef; Helm, Dieter Robert Koch-Institut, Epidemiologie und Gesundheitsberichterstattung https://doi.org/10.25646/898	other	January 2011
Trends in building simulation Augenbroe, Godfried Building and Environment, Vol. 37, Issue 8-9 https://doi.org/10.1016/S0360-1323(02)00041-0	journal	August 2002
Building simulation: an overview of developments and information sources Hong, Tianzhen; Chou, S. K.; Bong, T. Y. Building and Environment, Vol. 35, Issue 4 https://doi.org/10.1016/S0360-1323(99)00023-2	journal	May 2000
Commercial Building Energy Saver: An energy retrofit analysis toolkit Hong, Tianzhen; Piette, Mary Ann; Chen, Yixing Applied Energy, Vol. 159 https://doi.org/10.1016/j.apenergy.2015.09.002	journal	December 2015
A pattern-based automated approach to building energy model calibration Sun, Kaiyu; Hong, Tianzhen; Taylor-Lange, Sarah C. Applied Energy, Vol. 165 https://doi.org/10.1016/j.apenergy.2015.12.026	journal	March 2016
Evaluation of “Autotune” calibration against manual calibration of building energy models Chaudhary, Gaurav; New, Joshua; Sanyal, Jibonananda Applied Energy, Vol. 182 https://doi.org/10.1016/j.apenergy.2016.08.073	journal	November 2016
Contrasting the capabilities of building energy performance simulation programs Crawley, Drury B.; Hand, Jon W.; Kummert, Michaël Building and Environment, Vol. 43, Issue 4, p. 661-673 https://doi.org/10.1016/j.buildenv.2006.10.027	journal	April 2008
Performance analysis of pulsed flow control method for radiant slab system Tang, Haida; Raftery, Paul; Liu, Xiaohua Building and Environment, Vol. 127 https://doi.org/10.1016/j.buildenv.2017.11.004	journal	January 2018
Optimization-aided calibration of an urban microclimate model under uncertainty Mao, Jiachen; Fu, Yangyang; Afshari, Afshin Building and Environment, Vol. 143 https://doi.org/10.1016/j.buildenv.2018.07.034	journal	October 2018
Integrating physics-based models with sensor data: An inverse modeling approach Hong, Tianzhen; Lee, Sang Hoon Building and Environment, Vol. 154 https://doi.org/10.1016/j.buildenv.2019.03.006	journal	May 2019
New concepts and approach for developing energy efficient buildings: Ideal specific heat for building internal thermal mass Zeng, Ruolang; Wang, Xin; Di, Hongfa Energy and Buildings, Vol. 43, Issue 5 https://doi.org/10.1016/j.enbuild.2010.08.035	journal	May 2011
Calibration of building energy models for retrofit analysis under uncertainty Heo, Y.; Choudhary, R.; Augenbroe, G. A. Energy and Buildings, Vol. 47 https://doi.org/10.1016/j.enbuild.2011.12.029	journal	April 2012
Reduced-order residential home modeling for model predictive control Cole, Wesley J.; Powell, Kody M.; Hale, Elaine T. Energy and Buildings, Vol. 74 https://doi.org/10.1016/j.enbuild.2014.01.033	journal	May 2014
Different modeling strategies of infiltration rates for an office building to improve accuracy of building energy simulations Han, Guiyuan; Srebric, Jelena; Enache-Pommer, Elena Energy and Buildings, Vol. 86 https://doi.org/10.1016/j.enbuild.2014.10.028	journal	January 2015
Full scale laboratory experiment on the cooling capacity of a radiant floor system Pantelic, Jovan; Schiavon, Stefano; Ning, Baisong Energy and Buildings, Vol. 170 https://doi.org/10.1016/j.enbuild.2018.03.002	journal	July 2018
Side-by-side laboratory comparison of space heat extraction rates and thermal energy use for radiant and all-air systems Woolley, Jonathan; Schiavon, Stefano; Bauman, Fred Energy and Buildings, Vol. 176 https://doi.org/10.1016/j.enbuild.2018.06.018	journal	October 2018
Continuous-time Bayesian calibration of energy models using BIM and energy data Chong, Adrian; Xu, Weili; Chao, Song Energy and Buildings, Vol. 194 https://doi.org/10.1016/j.enbuild.2019.04.017	journal	July 2019
Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm Wang, Shengwei; Xu, Xinhua Energy Conversion and Management, Vol. 47, Issue 13-14, p. 1927-1941 https://doi.org/10.1016/j.enconman.2005.09.011	journal	August 2006
Energy retrofit analysis toolkits for commercial buildings: A review Lee, Sang Hoon; Hong, Tianzhen; Piette, Mary Ann Energy, Vol. 89 https://doi.org/10.1016/j.energy.2015.06.112	journal	September 2015
Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance Lee, Sang Hoon; Hong, Tianzhen; Piette, Mary Ann Energy, Vol. 90 https://doi.org/10.1016/j.energy.2015.07.107	journal	October 2015
A review on the prediction of building energy consumption Zhao, Hai-xiang; Magoulès, Frédéric Renewable and Sustainable Energy Reviews, Vol. 16, Issue 6 https://doi.org/10.1016/j.rser.2012.02.049	journal	August 2012
Influence of internal thermal mass on the indoor thermal dynamics and integration of phase change materials in furniture for building energy storage: A review Johra, Hicham; Heiselberg, Per Renewable and Sustainable Energy Reviews, Vol. 69 https://doi.org/10.1016/j.rser.2016.11.145	journal	March 2017
Thermal inertia in buildings: A review of impacts across climate and building use Verbeke, Stijn; Audenaert, Amaryllis Renewable and Sustainable Energy Reviews, Vol. 82 https://doi.org/10.1016/j.rser.2017.08.083	journal	February 2018
Modeling the direct sun component in buildings using matrix algebraic approaches: Methods and validation Lee, Eleanor S.; Geisler-Moroder, David; Ward, Gregory Solar Energy, Vol. 160 https://doi.org/10.1016/j.solener.2017.12.029	journal	January 2018
Comparison of the simplified methods of the ISO 13790 standard and detailed modelling programs in a regulatory context Kokogiannakis, Georgios; Strachan, Paul; Clarke, Joe Journal of Building Performance Simulation, Vol. 1, Issue 4 https://doi.org/10.1080/19401490802509388	journal	December 2008

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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
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EnergyPlus
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