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

Journal Article · Sat Feb 22 00:00:00 EST 2020 · Energy and Buildings

DOI:https://doi.org/10.1016/j.enbuild.2020.109892· OSTI ID:1659957

Park, June Young ^[1]; Wilson, Eric ^[2]; Parker, Andrew ^[2]; Nagy, Zoltan ^[3]

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1. 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
2. National Renewable Energy Lab. (NREL), Golden, CO (United States). Buildings and Thermal Sciences Center
3. Univ. of Texas, Austin, TX (United States). Dept. of Civil, Architectural and Environmental Engineering

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 1659957
• Alternate ID(s): OSTI ID: 1604820
• Report Number(s): NREL/JA-5500-75533; MainId:6772; UUID:b14272ea-3011-ea11-9c2a-ac162d87dfe5; MainAdminID:13692
• Journal Information: Energy and Buildings, Journal Name: Energy and Buildings Journal Issue: C Vol. 215; ISSN 0378-7788
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

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