Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information

Jiang, Zhanhong; Francis, Jonathan; Sahu, Anit Kumar; Munir, Sirajum; Shelton, Charles; Rowe, Anthony; Berges, Mario

doi:10.23919/acc.2018.8431085

Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information

Journal Article · Fri Jun 01 00:00:00 EDT 2018 · Proceedings of the American Control Conference (ACC) (Online)

DOI:https://doi.org/10.23919/acc.2018.8431085· OSTI ID:1812201

Jiang, Zhanhong ^[1]; ^[2]; Sahu, Anit Kumar ^[3]; Munir, Sirajum ^[2]; Shelton, Charles ^[2]; Rowe, Anthony ^[3]; Berges, Mario ^[3]

Iowa State Univ., Ames, IA (United States); Carnegie Mellon University
Bosch Research & Technology Center North America, Pittsburgh, PA (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States)

Understanding the models that characterize the thermal dynamics in a smart building is important for the comfort of its occupants and for its energy optimization. Here, a significant amount of research has attempted to utilize thermodynamics (physical) models for smart building control, but these approaches remain challenging due to the stochastic nature of the intermittent environmental disturbances. This paper presents a novel data-driven approach for indoor thermal model inference, which combines an Autoregressive Moving Average with eXogenous inputs model (ARMAX) with a Normalized Mutual Information scheme (NMI). Based on this information-theoretic method, NMI, causal dependencies between the indoor temperature and exogenous inputs are explicitly obtained as a guideline for the ARMAX model to find the dominating inputs. For validation, we use three datasets based on building energy systems-against which we compare our method to an autoregressive model with exogenous inputs (ARX), a regularized ARMAX model, and state-space models.

View Accepted Manuscript (DOE)

Research Organization:: Carnegie Mellon Univ., Pittsburgh, PA (United States)

Sponsoring Organization:: National Science Foundation; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: EE0007682

OSTI ID:: 1812201

Report Number(s):: DOE-CMU--07682

Journal Information:: Proceedings of the American Control Conference (ACC) (Online), Journal Name: Proceedings of the American Control Conference (ACC) (Online) Vol. 2018; ISSN 2378-5861

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

References (15)

A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building Zouak, M.; Mechaqrane, A. Neural Computing & Applications, Vol. 13, Issue 1 https://doi.org/10.1007/s00521-004-0401-8	journal	April 2004
Occupancy Detection Candanedo, Luis UCI Machine Learning Repository https://doi.org/10.24432/c5x01n	dataset	January 2016
Information-Theoretic Measures for Knowledge Discovery and Data Mining Yao, Y. Y. Entropy Measures, Maximum Entropy Principle and Emerging Applications https://doi.org/10.1007/978-3-540-36212-8_6	book	January 2003
Applications of artificial neural-networks for energy systems Kalogirou, Soteris A. Applied Energy, Vol. 67, Issue 1-2 https://doi.org/10.1016/S0306-2619(00)00005-2	journal	September 2000
Modelling temperature in intelligent buildings by means of autoregressive models Ríos-Moreno, G. J.; Trejo-Perea, M.; Castañeda-Miranda, R. Automation in Construction, Vol. 16, Issue 5 https://doi.org/10.1016/j.autcon.2006.11.003	journal	August 2007
ANN-based thermal control models for residential buildings Moon, Jin Woo; Kim, Jong-Jin Building and Environment, Vol. 45, Issue 7 https://doi.org/10.1016/j.buildenv.2010.01.009	journal	July 2010
A physics-based linear parametric model of room temperature in office buildings Wu, Siyu; Sun, Jian-Qiao Building and Environment, Vol. 50 https://doi.org/10.1016/j.buildenv.2011.10.005	journal	April 2012
Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building Marvuglia, Antonino; Messineo, Antonio; Nicolosi, Giuseppina Building and Environment, Vol. 72 https://doi.org/10.1016/j.buildenv.2013.10.020	journal	February 2014
Development of room temperature and relative humidity linear parametric models for an open office using BMS data Mustafaraj, G.; Chen, J.; Lowry, G. Energy and Buildings, Vol. 42, Issue 3 https://doi.org/10.1016/j.enbuild.2009.10.001	journal	March 2010
On-line learning of indoor temperature forecasting models towards energy efficiency Zamora-Martínez, F.; Romeu, P.; Botella-Rocamora, P. Energy and Buildings, Vol. 83 https://doi.org/10.1016/j.enbuild.2014.04.034	journal	November 2014
Accurate occupancy detection of an office room from light, temperature, humidity and CO 2 measurements using statistical learning models Candanedo, Luis M.; Feldheim, Véronique Energy and Buildings, Vol. 112 https://doi.org/10.1016/j.enbuild.2015.11.071	journal	January 2016
Algorithm for optimal application of the setback moment in the heating season using an artificial neural network model Moon, Jin Woo; Jung, Sung Kwon Energy and Buildings, Vol. 127 https://doi.org/10.1016/j.enbuild.2016.06.046	journal	September 2016
A review of thermal comfort models and indicators for indoor environments Enescu, Diana Renewable and Sustainable Energy Reviews, Vol. 79 https://doi.org/10.1016/j.rser.2017.05.175	journal	November 2017
Entropy and Correlation: Some Comments Kvalseth, Tarald O. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 17, Issue 3 https://doi.org/10.1109/TSMC.1987.4309069	journal	January 1987
Comparative Evaluation of Control-Oriented Zone Temperature Prediction Modeling Strategies in Buildings Chinde, Venkatesh; Heylmun, Jeffrey C.; Kohl, Adam Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications https://doi.org/10.1115/DSCC2015-9864	conference	October 2015

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Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
97 MATHEMATICS AND COMPUTING
ARMAX
Autoregressive
Disturbances
Mutual information
Sensing
Smart buildings
State space
Thermal dynamics

Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information

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References (15)

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