Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation

Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task smart meter have been used for load forecasting, reduction of consumer bills as well as reduction of grid distortions. Except energy consumption smart meters can be used to disaggregate energy consumption on device level. In this paper we investigate the potential of identifying the multimedia content played by a TV or monitor device using the central house's smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture is based on elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms were used with the best achieved video content identification accuracy being 93.6% using the MVM algorithm.

[1]  Michael Zeifman,et al.  Disaggregation of home energy display data using probabilistic approach , 2012, IEEE Transactions on Consumer Electronics.

[2]  Wenpeng Luan,et al.  Dynamic time warping based non-intrusive load transient identification , 2017 .

[3]  Bernardete Ribeiro,et al.  Electrical Signal Source Separation Via Nonnegative Tensor Factorization Using On Site Measurements in a Smart Home , 2014, IEEE Transactions on Instrumentation and Measurement.

[4]  B.-H. Juang,et al.  On the hidden Markov model and dynamic time warping for speech recognition — A unified view , 1984, AT&T Bell Laboratories Technical Journal.

[5]  Stephen Makonin,et al.  Investigating the switch continuity principle assumed in Non-Intrusive Load Monitoring (NILM) , 2016, 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[6]  Feng Wang,et al.  Experimenting Motion Relativity for Action Recognition with a Large Number of Classes , 2013 .

[7]  Christoph Ruland,et al.  Security issues in smart metering systems , 2015, 2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE).

[8]  Liang Zhang,et al.  Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting , 2020, IEEE Access.

[9]  Fred Popowich,et al.  AMPds: A public dataset for load disaggregation and eco-feedback research , 2013, 2013 IEEE Electrical Power & Energy Conference.

[10]  Pascal A. Schirmer,et al.  Robust energy disaggregation using appliance-specific temporal contextual information , 2020, EURASIP J. Adv. Signal Process..

[11]  Qiang Wang,et al.  Elastic Partial Matching of Time Series , 2005, PKDD.

[12]  Fan-Ren Chang,et al.  Network Time Protocol Based Time-Varying Encryption System for Smart Grid Meter , 2011, 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications Workshops.

[13]  Pascal A. Schirmer,et al.  Energy Disaggregation Using Fractional Calculus , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Yu Shimizu,et al.  Prediction of weather dependent energy consumption of residential housings , 2017, 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA).

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

[16]  Lalit Goel,et al.  A Two-Layer Energy Management System for Microgrids With Hybrid Energy Storage Considering Degradation Costs , 2018, IEEE Transactions on Smart Grid.

[17]  Tobias J. Oechtering,et al.  Privacy-preserving energy flow control in smart grids , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Rammohan Mallipeddi,et al.  Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances , 2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[19]  Marco Cuturi,et al.  Soft-DTW: a Differentiable Loss Function for Time-Series , 2017, ICML.

[20]  Luluwah Al-Fagih,et al.  A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors , 2018, Dynamic Games and Applications.

[21]  Jiqiang Liu,et al.  Privacy Protection Scheme Based on Remote Anonymous Attestation for Trusted Smart Meters , 2018, IEEE Transactions on Smart Grid.

[22]  Naima Kaabouch,et al.  Cyber security in the Smart Grid: Survey and challenges , 2013, Comput. Networks.

[23]  F. Itakura,et al.  Minimum prediction residual principle applied to speech recognition , 1975 .

[24]  Pascal A. Schirmer,et al.  Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors , 2020, Energies.

[25]  Qiang Wang,et al.  An elastic partial shape matching technique , 2007, Pattern Recognit..

[26]  Jingkun Gao,et al.  A feasibility study of automated plug-load identification from high-frequency measurements , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

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

[28]  Yu-Hsiu Lin,et al.  An Advanced Home Energy Management System Facilitated by Nonintrusive Load Monitoring With Automated Multiobjective Power Scheduling , 2015, IEEE Transactions on Smart Grid.

[29]  Pierluigi Mancarella,et al.  Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints , 2015, IEEE Transactions on Smart Grid.

[30]  Samuel Cheng,et al.  A Generic Optimisation-Based Approach for Improving Non-Intrusive Load Monitoring , 2019, IEEE Transactions on Smart Grid.

[31]  Pascal A. Schirmer,et al.  Residential Energy Consumption Prediction Using Inter-Household Energy Data and Socioeconomic Information , 2021, 2020 28th European Signal Processing Conference (EUSIPCO).

[32]  Nikolaos Doulamis,et al.  Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[33]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[34]  Arif Sarwat,et al.  A survey on security assessment of metering infrastructure in Smart Grid systems , 2015, SoutheastCon 2015.

[35]  Pascal A. Schirmer,et al.  Energy Disaggregation from Low Sampling Frequency Measurements Using Multi-Layer Zero Crossing Rate , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[36]  Naima Kaabouch,et al.  Cyber-security in smart grid: Survey and challenges , 2018, Comput. Electr. Eng..

[37]  Pascal A. Schirmer,et al.  Optimal Interleaved Modulation for DC- Link Loss Optimization in Six-Phase Drives , 2019, 2019 IEEE 13th International Conference on Power Electronics and Drive Systems (PEDS).

[38]  Marilyn A. Brown,et al.  Smart meter deployment in Europe: A comparative case study on the impacts of national policy schemes , 2017 .

[39]  Marco Cuturi,et al.  Fast Global Alignment Kernels , 2011, ICML.

[40]  Nora Cuppens-Boulahia,et al.  A privacy-aware access control model for distributed network monitoring , 2013, Comput. Electr. Eng..

[41]  Ivan V. Bajic,et al.  Wavenilm: A Causal Neural Network for Power Disaggregation from the Complex Power Signal , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[42]  Hairong Qi,et al.  Non-Intrusive Energy Disaggregation Using Non-Negative Matrix Factorization With Sum-to-k Constraint , 2017, IEEE Transactions on Power Systems.

[43]  Patrick D. McDaniel,et al.  Protecting consumer privacy from electric load monitoring , 2011, CCS '11.

[44]  Jing Liao,et al.  Non-intrusive appliance load monitoring using low-resolution smart meter data , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[45]  Matthew J. Johnson,et al.  Bayesian nonparametric hidden semi-Markov models , 2012, J. Mach. Learn. Res..

[46]  Christoph Klemenjak,et al.  Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring , 2020, 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

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

[48]  Jacques Klein,et al.  Profiling household appliance electricity usage with N-gram language modeling , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[49]  Hong Cheng,et al.  Image-to-Class Dynamic Time Warping for 3D hand gesture recognition , 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

[50]  Ulrich Greveler,et al.  Multimedia Content Identification Through Smart Meter Power Usage Profiles , 2012 .

[51]  Randal S. Olson,et al.  Relief-Based Feature Selection: Introduction and Review , 2017, J. Biomed. Informatics.

[52]  Fred Popowich,et al.  Efficient Sparse Matrix Processing for Nonintrusive Load Monitoring ( NILM ) , 2014 .

[53]  Pascal A. Schirmer,et al.  Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation , 2019, Sustainability.

[54]  Lillian J. Ratliff,et al.  Energy Disaggregation and the Utility-Privacy Tradeoff , 2018 .

[55]  Tomoko Matsui,et al.  A Kernel for Time Series Based on Global Alignments , 2006, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[56]  Haidang Phan,et al.  An Application Study on Road Surface Monitoring Using DTW Based Image Processing and Ultrasonic Sensors , 2020 .

[57]  Jack Kelly,et al.  The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes , 2014, Scientific Data.

[58]  Silvia Santini,et al.  The ECO data set and the performance of non-intrusive load monitoring algorithms , 2014, BuildSys@SenSys.

[59]  Aggelos S. Bouhouras,et al.  A NILM algorithm with enhanced disaggregation scheme under harmonic current vectors , 2019, Energy and Buildings.

[60]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[61]  Michael Paraskevas,et al.  Energy Disaggregation Using Elastic Matching Algorithms , 2020, Entropy.

[62]  Iosif Mporas,et al.  Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages , 2021, IEEE Access.

[63]  Waleed H. Abdulla,et al.  Cross-words reference template for DTW-based speech recognition systems , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[64]  Vladimir Stankovic,et al.  Power Disaggregation for Low-sampling Rate Data , 2014 .