Scalable tuning of building models to hourly data

Garrett, Aaron; New, Joshua Ryan

doi:10.1016/j.energy.2015.03.014

Title: Scalable tuning of building models to hourly data

Abstract

Energy models of existing buildings are unreliable unless calibrated so they correlate well with actual energy usage. Manual tuning requires a skilled professional, is prohibitively expensive for small projects, imperfect, non-repeatable, non-transferable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and cheap/ubiquitous sensors are beginning to make available today. A scalable, automated methodology is needed to quickly and intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-based capabilities into the marketplace for actualized energy savings. The "Autotune'' project is a novel, model-agnostic methodology which leverages supercomputing, large simulation ensembles, and big data mining with multiple machine learning algorithms to allow automatic calibration of simulations that match measured experimental data in a way that is deployable on commodity hardware. This paper shares several methodologies employed to reduce the combinatorial complexity to a computationally tractable search problem for hundreds of input parameters. Furthermore, accuracy metrics are provided which quantify model error to measured data for either monthly or hourly electrical usage from a highly-instrumented, emulated-occupancy research home.

Authors:

Garrett, Aaron ^[1]; New, Joshua Ryan ^[2]

Jacksonville State Univ., Jacksonville, AL (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:: Tue Mar 31 00:00:00 EDT 2015

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Org.:: USDOE Office of Science (SC); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

OSTI Identifier:: 1185507

Alternate Identifier(s):: OSTI ID: 1247854

Grant/Contract Number:: AC05-00OR22725; CEBT105; BT0201000; DEAC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Energy (Oxford)

Additional Journal Information:: Journal Name: Energy (Oxford); Journal Volume: 84; Journal ID: ISSN 0360-5442

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 97 MATHEMATICS AND COMPUTING; autotune; energy plus; calibration; optimization; evolutionary computation

Citation Formats


                    Garrett, Aaron, and New, Joshua Ryan. Scalable tuning of building models to hourly data.  United States: N. p., 2015. 
Web.  doi:10.1016/j.energy.2015.03.014.

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                    Garrett, Aaron, & New, Joshua Ryan. Scalable tuning of building models to hourly data.  United States.  https://doi.org/10.1016/j.energy.2015.03.014

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                    Garrett, Aaron, and New, Joshua Ryan. Tue .  
"Scalable tuning of building models to hourly data".  United States.  https://doi.org/10.1016/j.energy.2015.03.014.  https://www.osti.gov/servlets/purl/1185507.

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@article{osti_1185507,

  title        = {Scalable tuning of building models to hourly data},

  author       = {Garrett, Aaron and New, Joshua Ryan},

  abstractNote = {Energy models of existing buildings are unreliable unless calibrated so they correlate well with actual energy usage. Manual tuning requires a skilled professional, is prohibitively expensive for small projects, imperfect, non-repeatable, non-transferable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and cheap/ubiquitous sensors are beginning to make available today. A scalable, automated methodology is needed to quickly and intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-based capabilities into the marketplace for actualized energy savings. The "Autotune'' project is a novel, model-agnostic methodology which leverages supercomputing, large simulation ensembles, and big data mining with multiple machine learning algorithms to allow automatic calibration of simulations that match measured experimental data in a way that is deployable on commodity hardware. This paper shares several methodologies employed to reduce the combinatorial complexity to a computationally tractable search problem for hundreds of input parameters. Furthermore, accuracy metrics are provided which quantify model error to measured data for either monthly or hourly electrical usage from a highly-instrumented, emulated-occupancy research home.},

  doi          = {10.1016/j.energy.2015.03.014},

  journal      = {Energy (Oxford)},

  number       = ,

  volume       = 84,

  place        = {United States},

  year         = {Tue Mar 31 00:00:00 EDT 2015},

  month        = {Tue Mar 31 00:00:00 EDT 2015}

}

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Works referenced in this record:

Effectiveness of single and multiple energy retrofit measures on the energy consumption of office buildings
journal, August 2011

Chidiac, S. E.; Catania, E. J. C.; Morofsky, E.
Energy, Vol. 36, Issue 8
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History and development of validation with the ESP-r simulation program
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Murphy, Gavin Bruce; Counsell, John; Allison, John
Energy, Vol. 57
DOI: 10.1016/j.energy.2013.06.015

A global model for residential energy use: Uncertainty in calibration to regional data
journal, January 2010

van Ruijven, Bas; de Vries, Bert; van Vuuren, Detlef P.
Energy, Vol. 35, Issue 1
DOI: 10.1016/j.energy.2009.09.019

An introduction to simulated evolutionary optimization
journal, January 1994

Fogel, D. B.
IEEE Transactions on Neural Networks, Vol. 5, Issue 1
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What is evolutionary computation?
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Fogel, D. B.
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Works referencing / citing this record:

Building simulation: Ten challenges
journal, April 2018

Hong, Tianzhen; Langevin, Jared; Sun, Kaiyu
Building Simulation, Vol. 11, Issue 5
DOI: 10.1007/s12273-018-0444-x

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Similar Records

Title: Scalable tuning of building models to hourly data

Abstract

Citation Formats

Effectiveness of single and multiple energy retrofit measures on the energy consumption of office buildings journal, August 2011

Accuracy of home energy rating systems journal, April 2000

History and development of validation with the ESP-r simulation program journal, April 2008

Calibrating a combined energy systems analysis and controller design method with empirical data journal, August 2013

A global model for residential energy use: Uncertainty in calibration to regional data journal, January 2010

An introduction to simulated evolutionary optimization journal, January 1994

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Building simulation: Ten challenges journal, April 2018

Effectiveness of single and multiple energy retrofit measures on the energy consumption of office buildings
journal, August 2011

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journal, April 2000

History and development of validation with the ESP-r simulation program
journal, April 2008

Calibrating a combined energy systems analysis and controller design method with empirical data
journal, August 2013

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journal, January 2010

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journal, January 1994

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journal, February 2000

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journal, April 2018