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

Abstract

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 homesmore » with significantly high disaggregation accuracy.« less

Authors:
 [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)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
National Science Foundation (NSF); USDOE
OSTI Identifier:
1415126
Report Number(s):
NREL/JA-5500-70719
Journal ID: ISSN 0306-2619
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 211; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; non-intrusive load monitoring (NILM); spatiotemporal pattern network (STPN); multivariate time-series

Citation Formats

Liu, Chao, Akintayo, Adedotun, Jiang, Zhanhong, Henze, Gregor P., and Sarkar, Soumik. Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network. United States: N. p., 2017. Web. doi:10.1016/j.apenergy.2017.12.026.
Liu, Chao, Akintayo, Adedotun, Jiang, Zhanhong, Henze, Gregor P., & Sarkar, Soumik. Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network. United States. doi:10.1016/j.apenergy.2017.12.026.
Liu, Chao, Akintayo, Adedotun, Jiang, Zhanhong, Henze, Gregor P., and Sarkar, Soumik. Mon . "Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network". United States. doi:10.1016/j.apenergy.2017.12.026.
@article{osti_1415126,
title = {Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network},
author = {Liu, Chao and Akintayo, Adedotun and Jiang, Zhanhong and Henze, Gregor P. and Sarkar, Soumik},
abstractNote = {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.},
doi = {10.1016/j.apenergy.2017.12.026},
journal = {Applied Energy},
number = C,
volume = 211,
place = {United States},
year = {Mon Dec 18 00:00:00 EST 2017},
month = {Mon Dec 18 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
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