Quality Control Methods for Advanced Metering Infrastructure Data

Journal Article · 28 January 2021 · Smart Cities

DOI: https://doi.org/10.3390/smartcities4010012 · OSTI ID:1763476

Garrison, Eric ^[1]; New, Joshua  ^[2]

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1. Univ. of Tennessee, Knoxville, TN (United States)
2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Electricity (OE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1763476
• Journal Information: Smart Cities, Journal Name: Smart Cities Journal Issue: 1 Vol. 4; ISSN 2624-6511
• Publisher: MDPICopyright Statement
• Country of Publication: United States
• Language: English