The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Park, June Young; Wilson, Eric; Parker, Andrew; Nagy, Zoltan

doi:10.1016/j.enbuild.2020.109892

Title: The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

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Abstract

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences inmore »« less

Authors:

Park, June Young ^[1];

^[2];

^[2]; Nagy, Zoltan ^[3]

National Renewable Energy Lab. (NREL), Golden, CO (United States). Buildings and Thermal Sciences Center; Univ. of Texas, Austin, TX (United States). Dept. of Civil, Architectural and Environmental Engineering
National Renewable Energy Lab. (NREL), Golden, CO (United States). Buildings and Thermal Sciences Center
Univ. of Texas, Austin, TX (United States). Dept. of Civil, Architectural and Environmental Engineering

Publication Date:: Sat Feb 22 00:00:00 EST 2020

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

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

OSTI Identifier:: 1659957

Alternate Identifier(s):: OSTI ID: 1604820

Report Number(s):: NREL/JA-5500-75533
Journal ID: ISSN 0378-7788; MainId:6772;UUID:b14272ea-3011-ea11-9c2a-ac162d87dfe5;MainAdminID:13692

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: Energy and Buildings

Additional Journal Information:: Journal Volume: 215; Journal Issue: C; Journal ID: ISSN 0378-7788

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; buildings; data mining; discord detection; load profile; portfolio analysis; smart meter

Citation Formats


                    Park, June Young, Wilson, Eric, Parker, Andrew, and Nagy, Zoltan. The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio.  United States: N. p., 2020. 
Web.  doi:10.1016/j.enbuild.2020.109892.

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                    Park, June Young, Wilson, Eric, Parker, Andrew, & Nagy, Zoltan. The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio.  United States.  https://doi.org/10.1016/j.enbuild.2020.109892

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                    Park, June Young, Wilson, Eric, Parker, Andrew, and Nagy, Zoltan. Sat .  
"The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio".  United States.  https://doi.org/10.1016/j.enbuild.2020.109892.  https://www.osti.gov/servlets/purl/1659957.

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@article{osti_1659957,

  title        = {The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio},

  author       = {Park, June Young and Wilson, Eric and Parker, Andrew and Nagy, Zoltan},

  abstractNote = {Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.},

  doi          = {10.1016/j.enbuild.2020.109892},

  journal      = {Energy and Buildings},

  number       = C,

  volume       = 215,

  place        = {United States},

  year         = {Sat Feb 22 00:00:00 EST 2020},

  month        = {Sat Feb 22 00:00:00 EST 2020}

}

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