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Title: Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

Journal Article · · Applied Energy
 [1];  [1];  [1];  [2];  [1]
  1. Iowa State Univ., Ames, IA (United States)
  2. Univ. of Colorado, Boulder, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
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Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1415126
Report Number(s):
NREL/JA-5500-70719
Journal Information:
Applied Energy, Vol. 211, Issue C; ISSN 0306-2619
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 53 works
Citation information provided by
Web of Science

References (31)

A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring journal January 2017
Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy journal June 2014
Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks journal May 2017
A hybrid Genetic Algorithm and Monte Carlo simulation approach to predict hourly energy consumption and generation by a cluster of Net Zero Energy Buildings journal October 2016
Nonintrusive appliance load monitoring: Review and outlook journal February 2011
Nonintrusive appliance load monitoring journal January 1992
Load Signature Study—Part I: Basic Concept, Structure, and Methodology journal April 2010
Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring journal November 2016
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
  • Kelly, Jack; Knottenbelt, William
  • BuildSys '15: The 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments https://doi.org/10.1145/2821650.2821672
conference November 2015
An energy estimation framework for event-based methods in Non-Intrusive Load Monitoring journal January 2015
Toward Non-Intrusive Load Monitoring via Multi-Label Classification journal January 2017
Nonintrusive Load Monitoring Using Wavelet Design and Machine Learning journal January 2016
Non-intrusive load monitoring based on low frequency active power measurements journal January 2016
NILMTK: an open source toolkit for non-intrusive load monitoring conference January 2014
Residential Appliances Identification and Monitoring by a Nonintrusive Method journal March 2012
An Advanced Home Energy Management System Facilitated by Nonintrusive Load Monitoring With Automated Multiobjective Power Scheduling journal July 2015
An Empirical Investigation of V-I Trajectory Based Load Signatures for Non-Intrusive Load Monitoring journal March 2014
Ten questions concerning integrating smart buildings into the smart grid journal November 2016
Methodology of comprehensive building energy performance diagnosis for large commercial buildings at multiple levels journal May 2016
Towards non-intrusive thermal load Monitoring of buildings: BES calibration journal April 2017
Measuring the energy intensity of domestic activities from smart meter data journal December 2016
Understanding usage patterns of electric kettle and energy saving potential journal June 2016
Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns journal May 2008
Sensor Fusion for Fault Detection and Classification in Distributed Physical Processes journal December 2014
An Unsupervised Spatiotemporal Graphical Modeling Approach to Anomaly Detection in Distributed CPS conference April 2016
An unsupervised training method for non-intrusive appliance load monitoring journal December 2014
Energy prediction using spatiotemporal pattern networks journal November 2017
An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling journal October 2017
Bridge damage detection using spatiotemporal patterns extracted from dense sensor network journal November 2016
Image Quality Assessment: From Error Visibility to Structural Similarity journal April 2004
Global geometric similarity scheme for feature selection in fault diagnosis journal June 2014

Cited By (2)

A nonintrusive power load monitoring method using coupled allocation mechanism journal June 2019
A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances journal March 2018

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Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
non-intrusive load monitoring (NILM)
spatiotemporal pattern network (STPN)
multivariate time-series