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A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

Zaixun Ling, Qian Tao, Jingwen Zheng, Ping Xiong, Manjia Liu, Ziwei Xiao, Wenjie Gang
2021 Buildings  
system; lighting system; plug-in system; and elevator system.  ...  The proposed method can help improve the energy efficiency of building service systems during the operation or renovation stage.  ...  A NIM framework was developed to identify the energy consumption of HVAC systems [26] .  ... 
doi:10.3390/buildings11100449 fatcat:ytfu6kozf5aatbwlh6pgjfujsu

Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

Ahmed Zoha, Alexander Gluhak, Muhammad Imran, Sutharshan Rajasegarar
2012 Sensors  
This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing.  ...  strategies for optimal energy utilization.  ...  Furthermore, we would like to thank our colleague Colin O'Reilly for his help with the editing of our manuscript.  ... 
doi:10.3390/s121216838 pmid:23223081 pmcid:PMC3571813 fatcat:3kfnjwysi5gxpeicygvc73gp4e

A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem [article]

Anthony Faustine, Nerey Henry Mvungi, Shubi Kaijage, Kisangiri Michael
2017 arXiv   pre-print
This paper presents an up to date overview of NILM system and its associated methods and techniques for energy disaggregation problem.  ...  Furthermore, we review several performance metrics used by NILM researcher to evaluate NILM algorithms and discuss existing benchmarking framework for direct comparison of the state of the art NILM algorithms  ...  Acknowledgments The authors would like to thank Tanzania Communication Authority (TCRA) for supporting this research.  ... 
arXiv:1703.00785v3 fatcat:qw6gps5rjnaj7bauwommnovcfe

A Nonintrusive Load Monitoring Based on Multi-Target Regression Approach

Bundit Buddhahai, Stephen Makonin
2021 IEEE Access  
for multi-target regression model could provide the best disaggregation performance.  ...  This paper proposes an experimental design process for the application of energy disaggregation using multitarget regression, a new data learning approach in this application area.  ...  True and predicted power of appliances with simultaneous power ON activations for AMPds2 data set TABLE VI LABELS DISAGGREGATION ACCURACY AND PERFORMANCE COMPARISON FOR AMPDS2 DATA SET Disaggregation accuracy  ... 
doi:10.1109/access.2021.3133292 fatcat:pw7w2qdqynbbvcwzydefvyd5k4

Energy Disaggregation Using Principal Component Analysis Representation [chapter]

Pierre V. Dantas, Waldir Sabino S. Júnior, Celso B. Carvalho
2020 Frontiers in Artificial Intelligence and Applications  
This approach could be applied to audio signals, health care, home automation, ubiquitous systems and energy systems.  ...  It may be unworkable to individually measure the energy consumption of loads in a system simultaneously and, through disaggregation, we can make an inference using a main meter.  ...  We performed the PCA for each sample in the test set, obtaining a representation that allows comparison with the dictionary content obtained in the first step.  ... 
doi:10.3233/faia200766 fatcat:clb5tkzzezdrhlt7ehgdwmumce

Neural Fourier Energy Disaggregation

Christoforos Nalmpantis, Nikolaos Virtsionis Gkalinikis, Dimitris Vrakas
2022 Sensors  
Deploying energy disaggregation models in the real-world is a challenging task.  ...  The proposed architecture performs on par with two popular strong baseline models.  ...  For a fair comparison the best environmental setup is found for each of the four models that are compared. Then, utilizing the benchmark framework of Symeonidis et al.  ... 
doi:10.3390/s22020473 pmid:35062434 pmcid:PMC8779842 fatcat:3xbu4gql2nc4xlfnwbt4swnlf4

Energy Prediction using Spatiotemporal Pattern Networks [article]

Zhanhong Jiang, Chao Liu, Adedotun Akintayo, Gregor Henze, Soumik Sarkar
2017 arXiv   pre-print
In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.  ...  This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems.  ...  For non-intrusive load monitoring, energy disaggregation performance of the proposed STPN framework with and without a convex pro-gramming step is evaluated.  ... 
arXiv:1702.01125v1 fatcat:awlr4mduzbdsfma5vzffxd4abq

Energy Disaggregation using Variational Autoencoders [article]

Antoine Langevin, Marc-André Carbonneau, Mohamed Cheriet, Ghyslain Gagnon
2021 arXiv   pre-print
Recent disaggregation algorithms have significantly improved the performance of NILM systems.  ...  In this paper we address these issues and propose an energy disaggregation approach based on the variational autoencoders framework.  ...  Particularly, [19, 20] propose approaches based on the GAN framework for the energy disaggregation.  ... 
arXiv:2103.12177v2 fatcat:jrthuvgjuze7dng6esxiuoxw2q

A Dynamical Systems Approach to Energy Disaggregation [article]

Roy Dong and Lillian Ratliff and Henrik Ohlsson and S. Shankar Sastry
2013 arXiv   pre-print
In this paper, we present a novel framework to perform the energy disaggregation task.  ...  Energy disaggregation, also known as non-intrusive load monitoring (NILM), is the task of separating aggregate energy data for a whole building into the energy data for individual appliances.  ...  The authors would like to thank Aaron Bestick for his advice and many helpful discussions.  ... 
arXiv:1304.0789v1 fatcat:5ish7m7zojb2hgjssse5kumsoq

Pre-Processing of Energy Demand Disaggregation Based Data Mining Techniques for Household Load Demand Forecasting

Ahmed F. Ebrahim, Osama A. Mohammed
2018 Inventions  
This paper introduces an innovative methodology to enhance household demand forecasting based on energy disaggregation for Short Term Load Forecasting.  ...  This approach is constructed from Feed-Forward Artificial Neural Network forecaster and a pre-processing stage of energy disaggregation.  ...  Department of Energy and the Office of Naval Research. Conflicts of Interest: The authors declare no conflict of interest. Inventions 2018, 3, 45  ... 
doi:10.3390/inventions3030045 fatcat:no2gw5nkgbdi5isquuto45vinm

A Comprehensive Review on the NILM Algorithms for Energy Disaggregation [article]

Akriti Verma, Adnan Anwar, M. A. Parvez Mahmud, Mohiuddin Ahmed, Abbas Kouzani
2021 arXiv   pre-print
Many other approaches to perform energy disaggregation has been adapted such as deep neural network architectures and big data approach for household energy disaggregation.  ...  This paper provides a survey of the effective NILM system frameworks and reviews the performance of the benchmark algorithms in a comprehensive manner.  ...  The presence of nilmtk-contrib [5] , an open-source, implementation of the energy disaggregation problem, has unfolded the means for comparisons of the different algorithms executing energy disaggregation  ... 
arXiv:2102.12578v2 fatcat:hnbkxaq3gngabdfualkftu372a

A dynamical systems approach to energy disaggregation

Roy Dong, Lillian Ratliff, Henrik Ohlsson, S. Shankar Sastry
2013 52nd IEEE Conference on Decision and Control  
In this paper, we present a novel framework to perform the energy disaggregation task.  ...  Ahstract-Energy disaggregation, also known as non intrusive load monitoring (NILM), is the task of separating aggregate energy data for a whole building into the energy data for individual appliances.  ...  ACKNOWLEDGMENTS The authors would like to thank Aaron Bestick for his advice and many helpful discussions.  ... 
doi:10.1109/cdc.2013.6760891 dblp:conf/cdc/DongROS13 fatcat:2qbsv7l6i5e2dezgvk6ii74izu

EnTrack

Seokjun Lee, Wonwoo Jung, Yohan Chon, Hojung Cha
2015 Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '15  
In this framework, the energy consumption of system services is unclear and has not been comprehensively studied.  ...  Energy accounting is an essential requirement for optimizing energy consumption on mobile devices.  ...  Comparisons of energy consumption of system services for Firefox application.  ... 
doi:10.1145/2750858.2807531 dblp:conf/huc/LeeJCC15 fatcat:cyqvzqsigfhjvgdi3lk7k43tiy

An Extreme Learning Machine Approach to Effective Energy Disaggregation

Valerio Salerno, Graziella Rabbeni
2018 Electronics  
Data-driven procedures based on Factorial Hidden Markov Models (FHMMs) have produced remarkable results on energy disaggregation.  ...  amounts of training data covering as many operation conditions as possible need to be collected to attain top performances.  ...  In the next section, a brief overview of related work, which addresses the energy disaggregation task under the machine learning framework, will be given.  ... 
doi:10.3390/electronics7100235 fatcat:7d7evafycfgovpd2quajbcqary

Non-Intrusive Energy Disaggregation Using Non-Negative Matrix Factorization With Sum-to-k Constraint

Alireza Rahimpour, Hairong Qi, David Fugate, Teja Kuruganti
2017 IEEE Transactions on Power Systems  
Energy disaggregation can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework.  ...  Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-the-art decomposition-based disaggregation algorithms.  ...  For comparison purpose, we apply the Elastic Net method for the energy disaggregation task and analyze the results in Section III. III.  ... 
doi:10.1109/tpwrs.2017.2660246 fatcat:emleng4hgvap5gs7oqx66562jy
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