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Control limits for building energy end use based on frequency analysis and quantile regression

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Abstract

For buildings designed to meet aggressive energy goals, there is a need for tools to assist in the monitoring and maintenance of performance once the building is in operation. In particular, dashboard visualizations that show real-time and historic end use energy consumption alongside expected performance are powerful tools for both occupant engagement and the identification of operational issues. This article focuses on two related approaches to calculating upper and lower control limits for acceptable ranges of end use, which use a combination of modeled and measured usage data to generate realistic energy-conservative control limits. The first approach centers on the analysis of frequency distributions for end use consumption as functions of a main effect variable, while the second approach uses multivariate quantile regression based on principal components to generate control limits from all available measured variables.

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Abbreviations

BIC:

Bayesian information criterion

CSV:

Comma separated values file

LCL:

Lower control limit

LQM:

Lower quartile modeled

LQO:

Lower quartile observed

NREL:

National Renewable Energy Laboratory

OLS:

Ordinary least squares

PC:

Principal component

PCA:

Principal component analysis

PV:

Photovoltaic

RSF:

Research Support Facility

TMY3:

Typical Meteorological Year v3 weather data

UCL:

Upper control limit

UQM:

Upper quartile modeled

UQO:

Upper quartile observed

WE:

Weekend

WD:

Weekday

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Authors and Affiliations

  1. National Renewable Energy Laboratory, Golden, CO, USA

    Gregor P. Henze, Shanti Pless, Anya Petersen & Nicholas Long

  2. University of Colorado, Boulder, CO, USA

    Gregor P. Henze & Alexander T. Scambos

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  1. Gregor P. Henze
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  2. Shanti Pless
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  3. Anya Petersen
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  4. Nicholas Long
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  5. Alexander T. Scambos
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Correspondence to Gregor P. Henze.

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Henze, G.P., Pless, S., Petersen, A. et al. Control limits for building energy end use based on frequency analysis and quantile regression. Energy Efficiency 8, 1077–1092 (2015). https://doi.org/10.1007/s12053-015-9342-6

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  • DOI: https://doi.org/10.1007/s12053-015-9342-6

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