Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate

Nowadays climate change problems have been more and more concerns and urgent in the real world. Especially, the energy power consumption monitoring is a considerate trend having positive effects in decreasing affecting climate change. Non-Intrusive Load Monitoring (NILM) is the best economic solution to solve the electrical consumption monitoring issue. NILM captures the electrical signals from the aggregate energy consumption, feature extraction from these signals and then learning and predicting the switch ON/OFF of appliances used these feature extracted. This paper proposed a NILM framework including data acquisition, data feature extraction, and classification model. The main contribution is to develop a new transient signal in a different aspect. The proposed transient signal is extracted from the active power signal in the low-frequency sampling rate. This transient signal is used to detect the event of household appliances. In household appliances event detection, we applied to Decision Tree and Long Short-Time Memory (LSTM) models. The average accuracies of these models achieved 92.64% and 96.85%, respectively. The computational and result experiments present the solution effectiveness for the accurate transient signal extraction in the electrical input signals.

[1]  F. Sultanem,et al.  Using appliance signatures for monitoring residential loads at meter panel level , 1991 .

[2]  Jian Liang,et al.  Load Signature Study—Part I: Basic Concept, Structure, and Methodology , 2010, IEEE Transactions on Power Delivery.

[3]  Fahad Javed,et al.  An Empirical Investigation of V-I Trajectory Based Load Signatures for Non-Intrusive Load Monitoring , 2013, IEEE Transactions on Smart Grid.

[4]  Manish Marwah,et al.  Unsupervised Disaggregation of Low Frequency Power Measurements , 2011, SDM.

[5]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[6]  Jack Kelly,et al.  Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature , 2016, ArXiv.

[7]  Steven B. Leeb,et al.  Power signature analysis , 2003 .

[8]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[9]  Hussain Shareef,et al.  Application of load monitoring in appliances’ energy management – A review , 2017 .

[10]  Howon Kim,et al.  Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature , 2017, Comput. Intell. Neurosci..

[11]  Fred Popowich,et al.  Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring , 2016, IEEE Transactions on Smart Grid.

[12]  Tommi S. Jaakkola,et al.  Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation , 2012, AISTATS.

[13]  Alex Rogers,et al.  Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types , 2012, AAAI.

[14]  Fred Popowich,et al.  Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014 , 2016, Scientific Data.

[15]  Bin Yang,et al.  An Approach for Unsupervised Non-Intrusive Load Monitoring of Residential Appliances , 2013 .

[16]  Lukas Mauch,et al.  How well can HMM model load signals , 2016 .

[17]  Hsueh-Hsien Chang,et al.  Power-Spectrum-Based Wavelet Transform for Nonintrusive Demand Monitoring and Load Identification , 2014, IEEE Transactions on Industry Applications.

[18]  Jing Liao,et al.  A low-complexity energy disaggregation method: Performance and robustness , 2014, 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG).

[19]  Sousso Kelouwani,et al.  Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach , 2018 .

[20]  Muhammad Ali Imran,et al.  Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey , 2012, Sensors.

[21]  Shubi Kaijage,et al.  A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem , 2017, ArXiv.

[22]  Jack Kelly,et al.  Neural NILM: Deep Neural Networks Applied to Energy Disaggregation , 2015, BuildSys@SenSys.

[23]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[24]  Hsueh-Hsien Chang,et al.  A New Measurement Method for Power Signatures of Nonintrusive Demand Monitoring and Load Identification , 2011, IEEE Transactions on Industry Applications.

[25]  Sekyung Han,et al.  Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System , 2015 .

[26]  Bernardete Ribeiro,et al.  An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring (NILM) Systems , 2011, ICANNGA.

[27]  Jing Liao,et al.  Non-Intrusive Load Disaggregation Using Graph Signal Processing , 2018, IEEE Transactions on Smart Grid.

[28]  Gerhard P. Hancke,et al.  Using neural networks for non-intrusive monitoring of industrial electrical loads , 1994, Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9).

[29]  Y. Ahmet Sekercioglu,et al.  Recent approaches to non-intrusive load monitoring techniques in residential settings , 2013, 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG).

[30]  Gregory D. Abowd,et al.  At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award) , 2007, UbiComp.

[31]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.