Grey-box modeling and application for building energy simulations - A critical review

Li, Yanfei; O'Neill, Zheng; Zhang, Liang; Chen, Jianli; Im, Piljae; DeGraw, Jason

doi:10.1016/j.rser.2021.111174

Grey-box modeling and application for building energy simulations - A critical review

Journal Article · Thu May 06 00:00:00 UTC 2021 · Renewable and Sustainable Energy Reviews

DOI:https://doi.org/10.1016/j.rser.2021.111174· OSTI ID:1808373

^[1]; O'Neill, Zheng ^[2]; Zhang, Liang ^[3]; Chen, Jianli ^[4]; ^[1]; DeGraw, Jason ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Texas A & M Univ., College Station, TX (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Univ. of Utah, Salt Lake City, UT (United States)

Grey-box modeling, as one of the three fundamental modeling techniques for building energy models, has many advantages compared with black-box modeling and white-box modeling. Additionally, it has been widely applied to solve problems of building technologies, such as building load estimation, control and optimization, and building-grid integration. However, a thorough review of grey-box modeling is not available. This review study systematically investigated various aspects of grey-box modeling for buildings. First, the fundamental aspects of grey-box modeling are presented, including the theoretical background, modeling of building elements, modeling order, modeling diagram, and order reduction. Second, the detailed modeling approaches are discussed. Third, multiple applications of grey-box modeling are investigated for building energy domain, which are categorized into the following groups: heat dynamics analysis, thermal load estimation, building control and optimization, district/urban scale energy modeling, and building-grid integration. Finally, the available software packages for grey-box modeling are compared. Overall, the challenges of using grey-box modeling can be summarized as follows: (1) the theoretical limitations and assumptions of grey-box modeling are unclear; (2) grey-box model naming convention and structure are confusing; (3) grey-box model creation is vague; (4) suitable applications of grey-box models are unknown; and (5) grey-box models lack unified software solutions for wider adoption.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1808373

Alternate ID(s):: OSTI ID: 1815265
OSTI ID: 1785340

Journal Information:: Renewable and Sustainable Energy Reviews, Journal Name: Renewable and Sustainable Energy Reviews Vol. 146; ISSN 1364-0321

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (85)

A grey-box model of next-day building thermal load prediction for energy-efficient control Zhou, Qiang; Wang, Shengwei; Xu, Xinhua International Journal of Energy Research, Vol. 32, Issue 15 https://doi.org/10.1002/er.1458	journal	December 2008
Building simulation: Ten challenges Hong, Tianzhen; Langevin, Jared; Sun, Kaiyu Building Simulation, Vol. 11, Issue 5 https://doi.org/10.1007/s12273-018-0444-x	journal	April 2018
A critical review of fault modeling of HVAC systems in buildings Li, Yanfei; O’Neill, Zheng Building Simulation, Vol. 11, Issue 5 https://doi.org/10.1007/s12273-018-0458-4	journal	July 2018
Application of model reduction techniques to building energy simulation Rabenstein, R. Solar Energy, Vol. 53, Issue 3 https://doi.org/10.1016/0038-092X(94)90635-1	journal	September 1994
Model for the computation of transient temperatures in buildings Weedy, B. M.; Isgar, P. Energy Conversion and Management, Vol. 25, Issue 1 https://doi.org/10.1016/0196-8904(85)90070-6	journal	January 1985
ELAN—A computer model for building energy design De Wit, M. H.; Driessen, H. H. Building and Environment, Vol. 23, Issue 4 https://doi.org/10.1016/0360-1323(88)90034-0	journal	January 1988
Least squares parameter estimation of a reduced order thermal model of an experimental building Dewson, T.; Day, B.; Irving, A. D. Building and Environment, Vol. 28, Issue 2 https://doi.org/10.1016/0360-1323(93)90046-6	journal	April 1993
Estimation of continuous-time models for the heat dynamics of a building Madsen, H.; Holst, J. Energy and Buildings, Vol. 22, Issue 1 https://doi.org/10.1016/0378-7788(94)00904-X	journal	March 1995
A first-order thermal model for building design Mathews, E. H.; Richards, P. G.; Lombard, C. Energy and Buildings, Vol. 21, Issue 2 https://doi.org/10.1016/0378-7788(94)90006-X	journal	January 1994
Modelling heat transfers in building by coupling reduced-order models Ménézo, C.; Roux, J. J.; Virgone, J. Building and Environment, Vol. 37, Issue 2 https://doi.org/10.1016/S0360-1323(01)00023-3	journal	February 2002
Building thermal model reduction using nonlinear constrained optimization Gouda, M. M.; Danaher, S.; Underwood, C. P. Building and Environment, Vol. 37, Issue 12 https://doi.org/10.1016/S0360-1323(01)00121-4	journal	December 2002
A new multizone model for the simulation of building thermal performance Hong, Tianzhen; Jiang, Yi Building and Environment, Vol. 32, Issue 2 https://doi.org/10.1016/S0360-1323(96)00045-5	journal	March 1997
A review of identification in continuous-time systems Unbehauen, H.; Rao, G. P. Annual Reviews in Control, Vol. 22 https://doi.org/10.1016/S1367-5788(98)00015-7	journal	January 1998
Reduced order modeling and parameter identification of a building energy system model through an optimization routine Harish, V. S. K. V.; Kumar, Arun Applied Energy, Vol. 162 https://doi.org/10.1016/j.apenergy.2015.10.137	journal	January 2016
Thermal performance evaluation of an active building integrated photovoltaic thermoelectric wall system Luo, Yongqiang; Zhang, Ling; Liu, Zhongbing Applied Energy, Vol. 177 https://doi.org/10.1016/j.apenergy.2016.05.087	journal	September 2016
Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model Hu, Maomao; Xiao, Fu; Wang, Lingshi Applied Energy, Vol. 207 https://doi.org/10.1016/j.apenergy.2017.05.099	journal	December 2017
Building-to-grid predictive power flow control for demand response and demand flexibility programs Razmara, M.; Bharati, G. R.; Hanover, Drew Applied Energy, Vol. 203 https://doi.org/10.1016/j.apenergy.2017.06.040	journal	October 2017
Model predictive control for building loads connected with a residential distribution grid Mirakhorli, Amin; Dong, Bing Applied Energy, Vol. 230 https://doi.org/10.1016/j.apenergy.2018.08.051	journal	November 2018
A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses Cui, Borui; Fan, Cheng; Munk, Jeffrey Applied Energy, Vol. 236 https://doi.org/10.1016/j.apenergy.2018.11.077	journal	February 2019
Fast and accurate district heating and cooling energy demand and load calculations using reduced-order modelling Kim, Eui-Jong; He, Xi; Roux, Jean-Jacques Applied Energy, Vol. 238 https://doi.org/10.1016/j.apenergy.2019.01.183	journal	March 2019
Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors Zhao, Yang; Wen, Jin; Wang, Shengwei Applied Thermal Engineering, Vol. 90 https://doi.org/10.1016/j.applthermaleng.2015.07.001	journal	November 2015
Diagnostic Bayesian networks for diagnosing air handling units faults – part I: Faults in dampers, fans, filters and sensors Zhao, Yang; Wen, Jin; Xiao, Fu Applied Thermal Engineering, Vol. 111 https://doi.org/10.1016/j.applthermaleng.2015.09.121	journal	January 2017
A dynamic simplified model of radiant ceiling cooling integrated with underfloor ventilation system Zhang, Dongliang; Xia, Xueying; Cai, Ning Applied Thermal Engineering, Vol. 106 https://doi.org/10.1016/j.applthermaleng.2016.06.017	journal	August 2016
Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems Zong, Yi; Böning, Georg Martin; Santos, Rui Mirra Applied Thermal Engineering, Vol. 114 https://doi.org/10.1016/j.applthermaleng.2016.11.141	journal	March 2017
Development and experiment validation of variable-resistance-variable-capacitance dynamic simplified thermal models for shape-stabilized phase change material slab Gao, Jiajia; Yan, Tian; Xu, Tao Applied Thermal Engineering, Vol. 146 https://doi.org/10.1016/j.applthermaleng.2018.09.124	journal	January 2019
Model Predictive Control of thermal comfort as a benchmark for controller performance Hazyuk, Ion; Ghiaus, Christian; Penhouet, David Automation in Construction, Vol. 43 https://doi.org/10.1016/j.autcon.2014.03.016	journal	July 2014
Structure-preserving model reduction of nonlinear building thermal models Deng, Kun; Goyal, Siddharth; Barooah, Prabir Automatica, Vol. 50, Issue 4 https://doi.org/10.1016/j.automatica.2014.02.009	journal	April 2014
An optimized RC-network for thermally activated building components Weber, T.; Jóhannesson, G. Building and Environment, Vol. 40, Issue 1 https://doi.org/10.1016/j.buildenv.2004.04.012	journal	January 2005
Model-based demand-limiting control of building thermal mass Lee, Kyoung-ho; Braun, James E. Building and Environment, Vol. 43, Issue 10, p. 1633-1646 https://doi.org/10.1016/j.buildenv.2007.10.009	journal	October 2008
A resistance-capacitance network model for the analysis of the interactions between the energy performance of buildings and the urban climate Bueno, Bruno; Norford, Leslie; Pigeon, Grégoire Building and Environment, Vol. 54 https://doi.org/10.1016/j.buildenv.2012.01.023	journal	August 2012
System parameter identification theory and uncertainty analysis methods for multi-zone building heat transfer and infiltration Okuyama, Hiroyasu; Onishi, Yoshinori Building and Environment, Vol. 54 https://doi.org/10.1016/j.buildenv.2012.02.002	journal	August 2012
Inverse modeling of simplified hygrothermal building models to predict and characterize indoor climates Kramer, Rick; van Schijndel, Jos; Schellen, Henk Building and Environment, Vol. 68 https://doi.org/10.1016/j.buildenv.2013.06.001	journal	October 2013
Model predictive control strategies for buildings with mixed-mode cooling Hu, Jianjun; Karava, Panagiota Building and Environment, Vol. 71 https://doi.org/10.1016/j.buildenv.2013.09.005	journal	January 2014
Theory and applications of HVAC control systems – A review of model predictive control (MPC) Afram, Abdul; Janabi-Sharifi, Farrokh Building and Environment, Vol. 72 https://doi.org/10.1016/j.buildenv.2013.11.016	journal	February 2014
Low order thermal network models for dynamic simulations of buildings on city district scale Lauster, M.; Teichmann, J.; Fuchs, M. Building and Environment, Vol. 73 https://doi.org/10.1016/j.buildenv.2013.12.016	journal	March 2014
A state-space modeling approach and multi-level optimization algorithm for predictive control of multi-zone buildings with mixed-mode cooling Hu, Jianjun; Karava, Panagiota Building and Environment, Vol. 80 https://doi.org/10.1016/j.buildenv.2014.05.003	journal	October 2014
Comparisons of inverse modeling approaches for predicting building energy performance Zhang, Yuna; O'Neill, Zheng; Dong, Bing Building and Environment, Vol. 86 https://doi.org/10.1016/j.buildenv.2014.12.023	journal	April 2015
A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study Huang, Hao; Chen, Lei; Hu, Eric Building and Environment, Vol. 89 https://doi.org/10.1016/j.buildenv.2015.01.037	journal	July 2015
System identification for building thermal systems under the presence of unmeasured disturbances in closed loop operation: Lumped disturbance modeling approach Kim, Donghun; Cai, Jie; Ariyur, Kartik B. Building and Environment, Vol. 107 https://doi.org/10.1016/j.buildenv.2016.07.007	journal	October 2016
Simultaneous identification of dynamic model and occupant-induced disturbance for commercial buildings Coffman, Austin R.; Barooah, Prabir Building and Environment, Vol. 128 https://doi.org/10.1016/j.buildenv.2017.10.020	journal	January 2018
A new simplified modeling method for model predictive control in a medium-sized commercial building: A case study Zhuang, Junhua; Chen, Yimin; Chen, Xiangguang Building and Environment, Vol. 127 https://doi.org/10.1016/j.buildenv.2017.10.022	journal	January 2018
Calibration of simplified building energy models for parameter estimation and forecasting: Stochastic versus deterministic modelling Rouchier, Simon; Rabouille, Mickaël; Oberlé, Pierre Building and Environment, Vol. 134 https://doi.org/10.1016/j.buildenv.2018.02.043	journal	April 2018
A Simplified Thermal Model to Control the Energy Fluxes and to Improve the Performance of Buildings Danza, Ludovico; Belussi, Lorenzo; Meroni, Italo Energy Procedia, Vol. 101 https://doi.org/10.1016/j.egypro.2016.11.013	journal	November 2016
The Prediction of Thermal Loads in Building by Means of the EN ISO 13790 Dynamic Model: A Comparison with TRNSYS Bruno, Roberto; Pizzuti, Gianluca; Arcuri, Natale Energy Procedia, Vol. 101 https://doi.org/10.1016/j.egypro.2016.11.025	journal	November 2016
Investigation of the Demand Response Potentials of Residential Air Conditioners Using Grey-box Room Thermal Model Hu, Maomao; Xiao, Fu Energy Procedia, Vol. 105 https://doi.org/10.1016/j.egypro.2017.03.594	journal	May 2017
The adjacent walls effects in simplified thermal model of buildings Bagheri, Ali; Feldheim, Véronique; Thomas, Dimitrios Energy Procedia, Vol. 122 https://doi.org/10.1016/j.egypro.2017.07.359	journal	September 2017
Model-based optimal load control of inverter-driven air conditioners responding to dynamic electricity pricing Hu, Maomao; Xiao, Fu Energy Procedia, Vol. 142 https://doi.org/10.1016/j.egypro.2017.12.395	journal	December 2017
Applying support vector machines to predict building energy consumption in tropical region Dong, Bing; Cao, Cheng; Lee, Siew Eang Energy and Buildings, Vol. 37, Issue 5 https://doi.org/10.1016/j.enbuild.2004.09.009	journal	May 2005
Identifying suitable models for the heat dynamics of buildings Bacher, Peder; Madsen, Henrik Energy and Buildings, Vol. 43, Issue 7 https://doi.org/10.1016/j.enbuild.2011.02.005	journal	July 2011
A method for model-reduction of non-linear thermal dynamics of multi-zone buildings Goyal, Siddharth; Barooah, Prabir Energy and Buildings, Vol. 47 https://doi.org/10.1016/j.enbuild.2011.12.005	journal	April 2012
EKF based self-adaptive thermal model for a passive house Fux, Samuel F.; Ashouri, Araz; Benz, Michael J. Energy and Buildings, Vol. 68 https://doi.org/10.1016/j.enbuild.2012.06.016	journal	January 2014
A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform Li, Shun; Wen, Jin Energy and Buildings, Vol. 68 https://doi.org/10.1016/j.enbuild.2013.08.044	journal	January 2014
Development and validation of a gray box model to predict thermal behavior of occupied office buildings Berthou, Thomas; Stabat, Pascal; Salvazet, Raphael Energy and Buildings, Vol. 74 https://doi.org/10.1016/j.enbuild.2014.01.038	journal	May 2014
Quality of grey-box models and identified parameters as function of the accuracy of input and observation signals Reynders, G.; Diriken, J.; Saelens, D. Energy and Buildings, Vol. 82 https://doi.org/10.1016/j.enbuild.2014.07.025	journal	October 2014
Urban energy simulation: Simplification and reduction of building envelope models Kim, Eui-Jong; Plessis, Gilles; Hubert, Jean-Luc Energy and Buildings, Vol. 84 https://doi.org/10.1016/j.enbuild.2014.07.066	journal	December 2014
Application of a simplified thermal network model for real-time thermal load estimation Ogunsola, Oluwaseyi T.; Song, Li Energy and Buildings, Vol. 96 https://doi.org/10.1016/j.enbuild.2015.03.044	journal	June 2015
On the lumped capacitance approximation accuracy in RC network building models Kircher, Kevin J.; Max Zhang, K. Energy and Buildings, Vol. 108 https://doi.org/10.1016/j.enbuild.2015.09.053	journal	December 2015
Thermal building modelling using Gaussian processes Massa Gray, Francesco; Schmidt, Michael Energy and Buildings, Vol. 119 https://doi.org/10.1016/j.enbuild.2016.02.004	journal	May 2016
Development and validation of grey-box models for forecasting the thermal response of occupied buildings Harb, Hassan; Boyanov, Neven; Hernandez, Luis Energy and Buildings, Vol. 117 https://doi.org/10.1016/j.enbuild.2016.02.021	journal	April 2016
Validation of a lumped RC model for thermal simulation of a double skin natural and mechanical ventilated test cell Oliveira Panão, Marta J. N.; Santos, Carolina A. P.; Mateus, Nuno M. Energy and Buildings, Vol. 121 https://doi.org/10.1016/j.enbuild.2016.03.054	journal	June 2016
A new analytical approach for simplified thermal modelling of buildings: Self-Adjusting RC-network model Rodríguez Jara, Enrique Á.; Sánchez de la Flor, Francisco J.; Álvarez Domínguez, Servando Energy and Buildings, Vol. 130 https://doi.org/10.1016/j.enbuild.2016.08.039	journal	October 2016
Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control Salakij, Saran; Yu, Na; Paolucci, Samuel Energy and Buildings, Vol. 133 https://doi.org/10.1016/j.enbuild.2016.09.044	journal	December 2016
Inverse modeling of the urban energy system using hourly electricity demand and weather measurements, Part 2: Gray-box model Afshari, Afshin; Liu, Nengbao Energy and Buildings, Vol. 157 https://doi.org/10.1016/j.enbuild.2017.01.052	journal	December 2017
The development and validation of the linear time varying Simulink-based model for the dynamic simulation of the thermal performance of buildings Michalak, Piotr Energy and Buildings, Vol. 141 https://doi.org/10.1016/j.enbuild.2017.02.047	journal	April 2017
Development of a simplified resistance and capacitance (RC)-network model for pipe-embedded concrete radiant floors Li, Anbang; Sun, Yongjun; Xu, Xinhua Energy and Buildings, Vol. 150 https://doi.org/10.1016/j.enbuild.2017.06.011	journal	September 2017
An evaluation of the suitability of lumped-capacitance models in calculating energy needs and thermal behaviour of buildings Vivian, Jacopo; Zarrella, Angelo; Emmi, Giuseppe Energy and Buildings, Vol. 150 https://doi.org/10.1016/j.enbuild.2017.06.021	journal	September 2017
Demonstration of reduced-order urban scale building energy models Heidarinejad, Mohammad; Mattise, Nicholas; Dahlhausen, Matthew Energy and Buildings, Vol. 156 https://doi.org/10.1016/j.enbuild.2017.08.086	journal	December 2017
A hybrid approach to thermal building modelling using a combination of Gaussian processes and grey-box models Massa Gray, Francesco; Schmidt, Michael Energy and Buildings, Vol. 165 https://doi.org/10.1016/j.enbuild.2018.01.039	journal	April 2018
Estimation of thermophysical properties from in-situ measurements in all seasons: Quantifying and reducing errors using dynamic grey-box methods Gori, Virginia; Elwell, Clifford A. Energy and Buildings, Vol. 167 https://doi.org/10.1016/j.enbuild.2018.02.048	journal	May 2018
Reduced-order modeling for energy performance contracting Giretti, A.; Vaccarini, M.; Casals, M. Energy and Buildings, Vol. 167 https://doi.org/10.1016/j.enbuild.2018.02.049	journal	May 2018
A state-space thermal model incorporating humidity and thermal comfort for model predictive control in buildings Yang, Shiyu; Wan, Man Pun; Ng, Bing Feng Energy and Buildings, Vol. 170 https://doi.org/10.1016/j.enbuild.2018.03.082	journal	July 2018
Model predictive control of a thermally activated building system to improve energy management of an experimental building: Part I—Modeling and measurements Viot, H.; Sempey, A.; Mora, L. Energy and Buildings, Vol. 172 https://doi.org/10.1016/j.enbuild.2018.04.055	journal	August 2018
Automated grey box model implementation using BIM and Modelica Andriamamonjy, Ando; Klein, Ralf; Saelens, Dirk Energy and Buildings, Vol. 188-189 https://doi.org/10.1016/j.enbuild.2019.01.046	journal	April 2019
An innovative fault impact analysis framework for enhancing building operations Li, Yanfei; O'Neill, Zheng Energy and Buildings, Vol. 199 https://doi.org/10.1016/j.enbuild.2019.07.011	journal	September 2019
Improvement to EN ISO 52016-1:2017 hourly heat transfer through a wall assessment: the Italian National Annex Mazzarella, Livio; Scoccia, Rossano; Colombo, Paola Energy and Buildings, Vol. 210 https://doi.org/10.1016/j.enbuild.2020.109758	journal	March 2020
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
Dynamic modeling for evaluation of triple-pipe configuration potential in geothermal district heating networks Fabre, Antoine; Thomas, Romain; Duplessis, Bruno Energy Conversion and Management, Vol. 173 https://doi.org/10.1016/j.enconman.2018.07.087	journal	October 2018
The simple hourly method of EN ISO 13790 standard in Matlab/Simulink: A comparative study for the climatic conditions of Poland Michalak, Piotr Energy, Vol. 75 https://doi.org/10.1016/j.energy.2014.08.019	journal	October 2014
Simplified building model for transient thermal performance estimation using GA-based parameter identification Wang, Shengwei; Xu, Xinhua International Journal of Thermal Sciences, Vol. 45, Issue 4 https://doi.org/10.1016/j.ijthermalsci.2005.06.009	journal	April 2006
System identification and model-predictive control of office buildings with integrated photovoltaic-thermal collectors, radiant floor heating and active thermal storage Li, Siwei; Joe, Jaewan; Hu, Jianjun Solar Energy, Vol. 113 https://doi.org/10.1016/j.solener.2014.11.024	journal	March 2015
An Inverse Gray-Box Model for Transient Building Load Prediction Braun, James; Chaturvedi, Nitin HVAC&R Research, Vol. 8, Issue 1 https://doi.org/10.1080/10789669.2002.10391290	journal	January 2002
A general approach for generating reduced-order models for large multi-zone buildings Kim, Donghun; Braun, James E. Journal of Building Performance Simulation, Vol. 8, Issue 6 https://doi.org/10.1080/19401493.2014.977952	journal	November 2014
Toolbox for development and validation of grey-box building models for forecasting and control De Coninck, Roel; Magnusson, Fredrik; Åkesson, Johan Journal of Building Performance Simulation, Vol. 9, Issue 3 https://doi.org/10.1080/19401493.2015.1046933	journal	June 2015
Linear system reduction by impulse energy approximation Lucas, T. IEEE Transactions on Automatic Control, Vol. 30, Issue 8 https://doi.org/10.1109/TAC.1985.1104063	journal	August 1985
System Identification: A Machine Learning Perspective Chiuso, A.; Pillonetto, G. Annual Review of Control, Robotics, and Autonomous Systems, Vol. 2, Issue 1 https://doi.org/10.1146/annurev-control-053018-023744	journal	May 2019

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