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We present a physics-constrained control-oriented deep learning method for modeling building thermal dynamics.
Modelling the heat dynamics of a building using stochastic differential equations
Klaus Kaae Andersen, Henrik Madsen, and Lars H. Hansen · 2000
Earlier work this paper cites.
Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies
J. F. Kolen and S. C. Kremer · 2001
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Energy Consumption Characteristics of Commercial Building HVAC Systems - Volume III: Energy Savings Potential
K. W. Roth, D. Westphalen, J. Dieckmann, S. D. Hamilton, and W. Goetzler · 2002
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Mpc relevant identification––tuning the noise model
R.B. Gopaluni, R.S. Patwardhan, and S.L. Shah · 2004
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Prediction of building’s temperature using neural networks models
AE Ruano, EM Crispim, EZE Conceicao, and MMJR Lucio · 2006
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Dynamic mode decomposition of numerical and experimental data
P. Schmid · 2008
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Analysis of Energy Savings Potentials for Integrated Room Automation
D. Gyalistras, M. Gwerder, F. Schildbach, C.N. Jones, M. Morari, B. Lehmann, K. Wirth, and V. Stauch · 2010
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Ceiling radiant cooling: Comparison of armax and subspace identification modelling methods
L. Ferkl and J. Široký · 2010
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Pls-based model predictive control relevant identification: Pls-ph algorithm
D. Laurí, M. Martínez, J.V. Salcedo, and J. Sanchis · 2010
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Modeling and optimization of HVAC systems using a dynamic neural network
Andrew Kusiak and Guanglin Xu · 2010
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Experimental analysis of model predictive control for an energy efficient building heating system
Jan Širokỳ, Frauke Oldewurtel, Jiří Cigler, and Samuel Prívara · 2011
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Identifying suitable models for the heat dynamics of buildings
Peder Bacher and Henrik Madsen · 2011
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Model predictive control for the operation of building cooling systems
Y. Ma, F. Borrelli, B. Hencey, B. Coffey, S. Bengea, and P. Haves · 2012
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A model predictive control optimization environment for real-time commercial building application
Charles Corbin, Gregor Henze, and Peter May-Ostendorp · 2012
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Understanding the exploding gradient problem
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2012
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Online simultaneous state estimation and parameter adaptation for building predictive control
Mehdi Maasoumy, Barzin Moridian, Meysam Razmara, Mahdi Shahbakhti, and Alberto Sangiovanni-Vincentelli · 2013
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Beyond Theory: the Challenge of Implementing Model Predictive Control in Buildings
J. Cigler, D. Gyalistras, J. Široký, V. Tiet, and L. Ferkl · 2013
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Use of partial least squares within the control relevant identification for buildings
Samuel Prívara, Jiří Cigler, Zdeněk Váňa, Frauke Oldewurtel, and Eva Žáčeková · 2013
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Decentralized predictive thermal control for buildings
Vikas Chandan and Andrew G Alleyne · 2014
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Modelica buildings library
M. Wetter, W. Zuo, T. Nouidui, and X. Pang · 2014
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Review of modeling methods for HVAC systems
Abdul Afram and Farrokh Janabi-Sharifi · 2014
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Model predictive control for energy-efficient buildings: An airport terminal building study
Hao Huang, Lei Chen, and Eric Hu · 2014
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Quality of grey-box models and identified parameters as function of the accuracy of input and observation signals
Glenn Reynders, Jan Diriken, and Dirk Saelens · 2014
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Modeling environment for model predictive control of buildings
T. Zakula, P.R. Armstrong, and L. Norford · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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On dynamic mode decomposition: Theory and applications
Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg, Steven L. Brunton, and J. Nathan Kutz · 2014
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Building energy performance metrics - supporting energy efficiency progress in major economies
IEA International Energy Agency and International Partnership for Energy Efficiency Cooperation · 2015
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Comparisons of inverse modeling approaches for predicting building energy performance
Yuna Zhang, Zheng O’Neill, Bing Dong, and Godfried Augenbroe · 2015
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A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings
Hao Huang, Lei Chen, and Eric Hu · 2015
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Constrained convolutional neural networks for weakly supervised segmentation
Deepak Pathak, Philipp Krähenbühl, and Trevor Darrell · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2018
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Learning nonlinear state-space models using deep autoencoders
D. Masti and A. Bemporad · 2018
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Deep state space models for time series forecasting
Syama S. Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski · 2018
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Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Tackling climate change with machine learning
David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Körding, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, and Yoshua Bengio · 2019
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Dual estimation: Constructing building energy models from data sampled at low rate
Simone Baldi, Shuai Yuan, Petr Endel, and Ondrej Holub · 2016
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Toolbox for development and validation of grey-box building models for forecasting and control
R. De Coninck, F. Magnusson, J. Åkesson, and L. Helsen · 2016
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Structured inference networks for nonlinear state space models
Rahul G. Krishnan, Uri Shalit, and David Sontag · 2016
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Nonlinear systems identification using deep dynamic neural networks
Olalekan P. Ogunmolu, Xuejun Gu, Steve B. Jiang, and Nicholas R. Gans · 2016
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Bridging nonlinearities and stochastic regularizers with gaussian error linear units
Dan Hendrycks and Kevin Gimpel · 2016
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Comparison of model predictive control performance using grey-box and white-box controller models
Damien Picard, Maarten Sourbron, Filip Jorissen, Jiri Cigler, Lukás Ferkl, and Lieve Helsen · 2016
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Impact of the controller model complexity on model predictive control performance for buildings
D. Picard, J. Drgoňa, M. Kvasnica, and L. Helsen · 2017
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Building information modelling based building energy modelling: A review
Hao Gao, Christian Koch, and Yupeng Wu · 2019
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Automated data-driven modeling of building energy systems via machine learning algorithms
Martin Rätz, Amir Pasha Javadi, Marc Baranski, Konstantin Finkbeiner, and Dirk Müller · 2019
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Advancing non-convex and constrained learning: Challenges and opportunities
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Hoel Kervadec, Jose Dolz, Jing Yuan, Christian Desrosiers, Eric Granger, and Ismail Ben Ayed · 2019
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Sam Greydanus, Misko Dzamba, and Jason Yosinski · 2019
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Deep lagrangian networks: Using physics as model prior for deep learning
Michael Lutter, Christian Ritter, and Jan Peters · 2019
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DeepXDE: A deep learning library for solving differential equations
Lu Lu, Xuhui Meng, Zhiping Mao, and George E. Karniadakis · 2019
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IMEXnet: A forward stable deep neural network
Eldad Haber, Keegan Lensink, Eran Treister, and Lars Ruthotto · 2019
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Learning stable deep dynamics models
J. Zico Kolter and Gaurav Manek · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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All you need to know about model predictive control for buildings
Ján Drgoňa, Javier Arroyo, Iago Cupeiro Figueroa, David Blum, Krzysztof Arendt, Donghun Kim, Enric Perarnau Ollé, Juraj Oravec, Michael Wetter, Draguna L. Vrabie, and Lieve Helsen · 2020
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Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration
Ján Drgoňa, Damien Picard, and Lieve Helsen · 2020
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Application and characterization of metamodels based on artificial neural networks for building performance simulation: A systematic review
Nadia D. Roman, Facundo Bre, Victor D. Fachinotti, and Roberto Lamberts · 2020
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Identification of multi-zone grey-box building models for use in model predictive control
Javier Arroyo, Fred Spiessens, and Lieve Helsen · 2020
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Aggregation and data driven identification of building thermal dynamic model and unmeasured disturbance
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Linearly constrained neural networks
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Constrained neural ordinary differential equations with stability guarantees
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Constrained physics-informed deep learning for stable system identification and control of unknown linear systems
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Data-driven predictive control for unlocking building energy flexibility: A review
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