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Title: A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings

Abstract

Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual energy audits of readily available 15-minute-interval building electricity consumption are being explored to provide quick, inexpensive, and useful insights into building operation characteristics. A cross sectional analysis of six buildings in two different climate zones provides methods for data cleaning, population-based building comparisons, and relationships (correlations) of weather and electricity consumption. Data cleaning methods have been developed to categorize and appropriately filter or correct anomalous data including outliers, missing data, and erroneous values (resulting in < 0.5% anomalies). The utility of a cross-sectional analysis of a sample set of building's electricity consumption is found through comparisons of baseload, daily consumption variance, and energy use intensity. Correlations of weather and electricity consumption 15-minute interval datasets show important relationships for the heating and cooling seasons using computed correlations of a Time-Specific-Averaged- Ordered Variable (exterior temperature) and corresponding averaged variables (electricity consumption)(TSAOV method). The TSAOV method is unique as it introduces time of day as a third variable while also minimizing randomness in both correlated variables through averaging. This study found that many of the pair-wise linear correlation analyses lacked strong relationships,more » prompting the development of the new TSAOV method to uncover the causal relationship between electricity and weather. We conclude that a combination of varied HVAC system operations, building thermal mass, plug load use, and building set point temperatures are likely responsible for the poor correlations in the prior studies, while the correlation of time-specific-averaged-ordered temperature and corresponding averaged variables method developed herein adequately accounts for these issues and enables discovery of strong linear pair-wise correlation R values. TSAOV correlations lay the foundation for a new approach to building studies, that mitigates plug load interferences and identifies more accurate insights into weather-energy relationship for all building types. Over all six buildings analyzed the TSAOV method reported very significant average correlations per building of 0.94 to 0.82 in magnitude. Our rigorous statistics-based methods applied to 15- minute-interval electricity data further enables virtual energy audits of buildings to quickly and inexpensively inform energy savings measures.« less

Authors:
ORCiD logo [1];  [1];  [2];  [3];  [4];  [5]
  1. Case Western Reserve Univ., Cleveland, OH (United States). Case School of Engineering
  2. Case Western Reserve Univ., Cleveland, OH (United States). Case School of Engineering, Dept. of Electrical Engineering and Computer Science
  3. Case Western Reserve Univ., Cleveland, OH (United States). Case School of Engineering, Great Lakes Energy Inst.
  4. Case Western Reserve Univ., Cleveland, OH (United States). Case School of Engineering, Dept. of Materials Science and Engineering; Case Western Reserve Univ., Cleveland, OH (United States). Case School of Engineering, Solar Durability and Lifetime Extension Center
  5. Case Western Reserve Univ., Cleveland, OH (United States). Case School of Engineering; Case Western Reserve Univ., Cleveland, OH (United States). Case School of Engineering, Great Lakes Energy Inst.
Publication Date:
Research Org.:
Case Western Reserve Univ., Cleveland, OH (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
OSTI Identifier:
1417016
Grant/Contract Number:  
AR0000668
Resource Type:
Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 12; Journal Issue: 10; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats

Pickering, Ethan M., Hossain, Mohammad A., Mousseau, Jack P., Swanson, Rachel A., French, Roger H., and Abramson, Alexis R. A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings. United States: N. p., 2017. Web. doi:10.1371/journal.pone.0187129.
Pickering, Ethan M., Hossain, Mohammad A., Mousseau, Jack P., Swanson, Rachel A., French, Roger H., & Abramson, Alexis R. A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings. United States. doi:10.1371/journal.pone.0187129.
Pickering, Ethan M., Hossain, Mohammad A., Mousseau, Jack P., Swanson, Rachel A., French, Roger H., and Abramson, Alexis R. Tue . "A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings". United States. doi:10.1371/journal.pone.0187129. https://www.osti.gov/servlets/purl/1417016.
@article{osti_1417016,
title = {A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings},
author = {Pickering, Ethan M. and Hossain, Mohammad A. and Mousseau, Jack P. and Swanson, Rachel A. and French, Roger H. and Abramson, Alexis R.},
abstractNote = {Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual energy audits of readily available 15-minute-interval building electricity consumption are being explored to provide quick, inexpensive, and useful insights into building operation characteristics. A cross sectional analysis of six buildings in two different climate zones provides methods for data cleaning, population-based building comparisons, and relationships (correlations) of weather and electricity consumption. Data cleaning methods have been developed to categorize and appropriately filter or correct anomalous data including outliers, missing data, and erroneous values (resulting in < 0.5% anomalies). The utility of a cross-sectional analysis of a sample set of building's electricity consumption is found through comparisons of baseload, daily consumption variance, and energy use intensity. Correlations of weather and electricity consumption 15-minute interval datasets show important relationships for the heating and cooling seasons using computed correlations of a Time-Specific-Averaged- Ordered Variable (exterior temperature) and corresponding averaged variables (electricity consumption)(TSAOV method). The TSAOV method is unique as it introduces time of day as a third variable while also minimizing randomness in both correlated variables through averaging. This study found that many of the pair-wise linear correlation analyses lacked strong relationships, prompting the development of the new TSAOV method to uncover the causal relationship between electricity and weather. We conclude that a combination of varied HVAC system operations, building thermal mass, plug load use, and building set point temperatures are likely responsible for the poor correlations in the prior studies, while the correlation of time-specific-averaged-ordered temperature and corresponding averaged variables method developed herein adequately accounts for these issues and enables discovery of strong linear pair-wise correlation R values. TSAOV correlations lay the foundation for a new approach to building studies, that mitigates plug load interferences and identifies more accurate insights into weather-energy relationship for all building types. Over all six buildings analyzed the TSAOV method reported very significant average correlations per building of 0.94 to 0.82 in magnitude. Our rigorous statistics-based methods applied to 15- minute-interval electricity data further enables virtual energy audits of buildings to quickly and inexpensively inform energy savings measures.},
doi = {10.1371/journal.pone.0187129},
journal = {PLoS ONE},
number = 10,
volume = 12,
place = {United States},
year = {2017},
month = {10}
}

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