A Field Study of Nonintrusive Load Monitoring Devices and Implications for Load Disaggregation

Evaluations of nonintrusive load monitoring (NILM) algorithms and technologies have mostly occurred in constrained, artificial environments. However, few field evaluations of NILM products have taken place in actual buildings under normal operating conditions. This paper describes a field evaluation...

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Main Authors: Ebony Mayhorn, Joshua Butzbaugh, Alan Meier
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/23/19/8253
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author Ebony Mayhorn
Joshua Butzbaugh
Alan Meier
author_facet Ebony Mayhorn
Joshua Butzbaugh
Alan Meier
author_sort Ebony Mayhorn
collection DOAJ
description Evaluations of nonintrusive load monitoring (NILM) algorithms and technologies have mostly occurred in constrained, artificial environments. However, few field evaluations of NILM products have taken place in actual buildings under normal operating conditions. This paper describes a field evaluation of a state-of-the-art NILM product, tested in eight homes. The match rate metric—a technique recommended by a technical advisory group—was used to measure the NILM’s success in identifying specific loads and the accuracy of the energy consumption estimates. A performance assessment protocol was also developed to address common issues with NILM mislabeling and ground-truth comparisons that have not been sufficiently addressed in past evaluations. The NILM product’s estimates were compared to the submetered consumption of eight major appliances. Overall, the product had good performance in disaggregating the energy consumption of the electric water heaters, which included both electric resistance and heat-pump water heaters, but only a fair accuracy with refrigerators, dryers, and air conditioners. The performance was poor for cooking equipment, furnace fans, clothes washers, and dishwashers. Moreover, the product was often unable to detect major loads in homes. Typically, two or more appliances were not detected in a home. At least two dryers, furnace fans, and air conditioners went undetected across the eight homes. On the other hand, the dishwasher was detected in all homes where available or monitored. The key findings were qualitatively compared to those of past field evaluations. Potential areas for improvement in NILM product performance were determined along with areas where complementary technologies may be able to aid in load-disaggregation applications.
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spelling doaj.art-356ee55ddd6e48dea50cf886263bccc12023-11-19T15:04:56ZengMDPI AGSensors1424-82202023-10-012319825310.3390/s23198253A Field Study of Nonintrusive Load Monitoring Devices and Implications for Load DisaggregationEbony Mayhorn0Joshua Butzbaugh1Alan Meier2Pacific Northwest National Laboratory, Richland, WA 99354, USAPacific Northwest National Laboratory, Richland, WA 99354, USALawrence Berkeley National Laboratory, Berkeley, CA 94720, USAEvaluations of nonintrusive load monitoring (NILM) algorithms and technologies have mostly occurred in constrained, artificial environments. However, few field evaluations of NILM products have taken place in actual buildings under normal operating conditions. This paper describes a field evaluation of a state-of-the-art NILM product, tested in eight homes. The match rate metric—a technique recommended by a technical advisory group—was used to measure the NILM’s success in identifying specific loads and the accuracy of the energy consumption estimates. A performance assessment protocol was also developed to address common issues with NILM mislabeling and ground-truth comparisons that have not been sufficiently addressed in past evaluations. The NILM product’s estimates were compared to the submetered consumption of eight major appliances. Overall, the product had good performance in disaggregating the energy consumption of the electric water heaters, which included both electric resistance and heat-pump water heaters, but only a fair accuracy with refrigerators, dryers, and air conditioners. The performance was poor for cooking equipment, furnace fans, clothes washers, and dishwashers. Moreover, the product was often unable to detect major loads in homes. Typically, two or more appliances were not detected in a home. At least two dryers, furnace fans, and air conditioners went undetected across the eight homes. On the other hand, the dishwasher was detected in all homes where available or monitored. The key findings were qualitatively compared to those of past field evaluations. Potential areas for improvement in NILM product performance were determined along with areas where complementary technologies may be able to aid in load-disaggregation applications.https://www.mdpi.com/1424-8220/23/19/8253nonintrusive load monitoringfield evaluationload disaggregationaccuracydetectioninteroperability
spellingShingle Ebony Mayhorn
Joshua Butzbaugh
Alan Meier
A Field Study of Nonintrusive Load Monitoring Devices and Implications for Load Disaggregation
Sensors
nonintrusive load monitoring
field evaluation
load disaggregation
accuracy
detection
interoperability
title A Field Study of Nonintrusive Load Monitoring Devices and Implications for Load Disaggregation
title_full A Field Study of Nonintrusive Load Monitoring Devices and Implications for Load Disaggregation
title_fullStr A Field Study of Nonintrusive Load Monitoring Devices and Implications for Load Disaggregation
title_full_unstemmed A Field Study of Nonintrusive Load Monitoring Devices and Implications for Load Disaggregation
title_short A Field Study of Nonintrusive Load Monitoring Devices and Implications for Load Disaggregation
title_sort field study of nonintrusive load monitoring devices and implications for load disaggregation
topic nonintrusive load monitoring
field evaluation
load disaggregation
accuracy
detection
interoperability
url https://www.mdpi.com/1424-8220/23/19/8253
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