A Comprehensive Survey for Non-Intrusive Load Monitoring

Energy-saving and efficiency are as important as benefiting from new energy sources to supply increasing energy demand globally. Energy demand and resources for energy saving should be managed effectively. Therefore, electrical loads need to be monitored and controlled. Demand-side energy management plays a vital role in achieving this objective. Energy management systems schedule an optimal operation program for these loads by obtaining more accurate and precise residential and commercial loads information. Different intellegent measurement applications and machine learning algorithms have been proposed for the measurement and control of electrical devices/loads used in buildings. Of these, nonintrusive load monitoring (NILM) is widely used to monitor loads and gather precise information about devices without affecting consumers. NILM is a load monitoring method that uses a total power or current signal taken from a single point in residential and commercial buildings. Therefore, its installation and maintenance costs are low compared to other load monitoring methods. This method consists of signal processing and machine learning processes such as event detection (optional), feature extraction and device identification after the total power or current signal is acquired. Up to now, many techniques have been proposed for each processes in the literature. In this paper, techniques used in NILM systems are classified and a comprehensive review is presented.

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[1] Chua K, Chou S, Yang W, Yan J. Achieving better energy-efficient air conditioning – a review of technologies and strategies. Applied Energy 2013; 104: 87-104. doi: 10.1016/j.apenergy.2012.10.037
[2] Fischer C. Feedback on household electricity consumption: a tool for saving energy. Energy Efficiency 2008; 1: 79-104. doi: 10.1007/s12053-008-9009-7
[3] Karjalainen S. Consumer preferences for feedback on household electricity consumption. Energy and Buildings 2011; 43: 458 – 467. doi: 10.1016/j.enbuild.2010.10.010
[4] Peschiera G, Taylor JE, Siegel JA. Response–relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data. Energy and Buildings 2010; 42: 1329 – 1336. doi: 10.1016/j.enbuild.2010.03.001
[5] Selamogullari US, Elma O. A smart transformer application for voltage-controlled home energy management system. Journal of the Faculty of Engineering and Architecture of Gazi University 2018; 33: 1543 – 1556. doi: 10.17341/gazimmfd.416450
[6] Pau G, Collotta M, Ruano A, Qin J. Smart home energy management. Energies 2017; 10: 39970-39974. doi: 10.3390/en10030382
[7] Sundramoorthy V, Cooper G, Linge N, Liu Q. Domesticating energy-monitoring systems: Challenges and design concerns. IEEE Pervasive Computing 2011; 10: 20-27. doi: 10.1109/MPRV.2010.73
[8] Berges M, Goldman E, Matthews HS, Soibelman L, Anderson K. User-centered nonintrusive electricity load monitoring for residential buildings. Journal of Computing in Civil Engineering 2011; 25: 471-480. doi: 10.1111/j.1530- 9290.2010.00280.x
[9] Wong Y, Sekercioglu A, Drummond T, Wong V. Recent approaches to non-intrusive load monitoring techniques in residential settings. IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) 2013; :73-79. doi:10.1109/CIASG.2013.6611501
[10] Lu T, Xu Z, Huang B. An Event-Based Nonintrusive Load Monitoring Approach: Using the Simplified Viterbi Algorithm. IEEE Pervasive Computing 2017; 16: 54-61. doi: 10.1109/MPRV.2017.3971125
[11] Kim H, Marwah M, Arlitt MF, Lyon G, Han J. Unsupervised Disaggregation of Low Frequency Power Measurements. Proc. SIAM Conf. Data Mining 2011; 11: 747-758. doi: 10.1137/1.9781611972818.64
[12] Kolter J, Jaakkola T. Approximate inference in additive factorial HMMs with application to energy disaggregation. In: International Conference on Artificial Intelligence and Statistics 2012; 22: 1472-1482.
[13] Parson O, Ghosh S, Weal M, Rogers A. Non-intrusive load monitoring using prior models of general appliance types. In Proceeding of the twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), Toronto, Canada; 2012.
[14] Zoha A, Gluhak A, Imran MA, Rajasegarar S. Nonintrusive Load Monitoring approaches for disaggregated energy sensing: A survey. Sensors 2012; 12: 16838–16866. doi: 10.3390/s121216838
[15] Hart GW. Nonintrusive appliance load monitoring. Proceedings of the IEEE 1992; 80: 1870–1891. doi: 10.1109/5.192069
[16] Ruano A, Hernandez A, Urena J, Ruano M, Garcia J. NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review. Energies 2019; 12: 2203. doi: 10.3390/en12112203
[17] Zeifman M, Roth K. Nonintrusive appliance load monitoring: Review and Outlook. IEEE Transactions on Consumer Electronics 2011; 57: 76-84. doi: 10.1109/TCE.2011.5735484
[18] He D, Du L, Yang Y, Harley R, Habetler T. Front-end electronic circuit topology analysis for model-driven classification and monitoring of appliance loads in smart buildings. IEEE Trans. Smart Grid 2012; 3 (4): 2286- 2293. doi:10.1109/TSG.2012.2219327
[19] Nait Meziane M, Ravier P, Lamarque G, Le Bunete J, Raingeaud Y. High accuracy event detection for nonintrusive load monitoring. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA; 2017. pp. 2452–2456.
[20] Anderson KD, Berges ME, Ocneanu A, Benitez D, Moura JM. Event detection for Non Intrusive load monitoring. In: IECON Proceedings (Industrial Electronics Conference); Montreal, QC, Canada; 2012. pp. 3312– 3317.
[21] Hart, G.W. Prototype Nonintrusive Appliance Load Monitor. Technical Report Progress Report. MIT Energy Laboratory 1985.
[22] Sultanem, F. Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transactions on Power Delivery 1991; 6: 1380–1385. doi: 10.1109/61.97667
[23] Liang J, Ng SK, Kendall G, Cheng JW. Load signature studypart I: Basic concept, structure, and methodology. IEEE Transactions on Power Delivery 2009; 25 (6): 551-560. doi: 10.1109/TPWRD.2009.2033799
[24] Weiss M, Helfenstein A, Mattern F, Staake T. Leveraging smart meter data to recognize home appliances. In: IEEE International Conference on Pervasive Computing and Communications; Lugano, Switzerland; 2012. pp. 190–197.
[25] Meehan P, McArdle C, Daniels S. An efficient, scalable time-frequency method for tracking energy usage of domestic appliances using a two-step classification algorithm. Energies 2014; 7; 7041– 7066. doi: 10.3390/en7117041
[26] Dong M. Decomposition Techniques for Power System Load Analysis. PhD, University of Alberta, Department of Electrical and Computer Engineering, Alberta, Canada, 2013.
[27] Lu M, Li Z. A hybrid event detection approach for nonintrusive load monitoring. IEEE Transactions on Smart Grid 2020; 11: 528–540. doi: 10.1109/TSG.2019.2924862
[28] Luo D, Norford L, Shaw S, Leeb SB, Danks R et al. Monitoring hvac equipment electrical loads from a centralized location methods and field test results. ASHRAE Transactions 2002; 108: 841–857.
[29] Jin Y, Tebekaemi E, Berges M, Soibelman L. Robust adaptive event detection in non-intrusive load monitoring for energy aware smart facilities. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Prague, Czech Republic; 2011. pp. 4340–4343.
[30] Nguyen K, Dekneuvel E, Nicoll B, Zammit O, Van C et al. Event detection and disaggregation algorithms for nialm system. In: International Workshop on Non-Intrusive Load Monitoring (NILM); 2014.
[31] Zhu Z, Zhang S, Wei Z, Yin B, Huang X. A novel CUSUM-based approach for event detection in smart metering. In: IOP Conference Series: Materials Science and Engineering; Prague, Czech Republic; 2018.
[32] Wild B, Barsim KS, Yang B. A new unsupervised event detector for non-intrusive load monitoring. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP); Orlando, FL, USA; 2015. pp. 73-77.
[33] Pereira L. Developing and evaluating a probabilistic event detector for non-intrusive load monitoring. In: Sustainable Internet and ICT for Sustainability (SustainIT); Funchal, Portugal; 2017. pp. 1-10.
[34] Gonzalez MEB. A Framework for Enabling EnergyAware Facilities through Minimally-Intrusive Approaches. PhD, Carnegie Mellon University, Schenley Park Pittsburgh, PAUnited States, 2010.
[35] Pereira L, Quintal F, Goncalves R, Nunes N. Sustdata: A public dataset for ict4s electric energy research. In: International Conference on ICT for Sustainability; Stockholm, Sweden; 2014.
[36] Norford LK, Leeb SB. Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy and Buildings 1996; 24; 51–64. doi: 10.1016/0378-7788(95)00958-2
[37] Alcalá JM, Ureña J, Hernández A. Event-based detector for non-intrusive load monitoring based on the Hilbert Transform. In: IEEE International Conference on Emerging Technologies and Factory Automation; Barcelona, Spain; 2014. pp. 14-17.
[38] Streubel R, Yang B. Identification of electrical appliances via analysis of power consumption. In: International Universities Power Engineering Conference (UPEC); Barcelona, Spain; 2012. pp. 1-6.
[39] Barsim KS, Streubel R, Yang B. Unsupervised adaptive event detection for building-level energy disaggregation. In: Power and Energy Student Summit (PESS); Stuttgart, Germany; 2014.
[40] Zheng Z, Chen H, Luo X. A supervised event-based nonintrusive load monitoring for non-linear appliances. Sustainability 2018; 10. doi: 10.3390/su10041001
[41] Armel C, Gupta A, Shrimali G, Albert A. Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy 2013; 52: 213–234. doi:10.1016/j.enpol.2012.08.062
[42] Chang HH. Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses. Energies 2012; 5: 4569–4589. doi: 10.3390/en5114569
[43] Duarte C, Delmar P, Goossen KW, Barner K, Gomez-Luna E. Non-intrusive load monitoring based on switching voltage transients and wavelet transforms. In: Future of Instrumentation International Workshop (FIIW); Gatlinburg, TN, USA; 2012.
[44] Farinaccio L, Zmeureanu R. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy and Buildings 1999; 30: 245–259. doi: 10.1016/S0378-7788(99)00007-9
[45] Marceau M, Zmeureanu R. Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings. Energy Conversion and Management 2000; 41: 1389–1403. doi: 10.1016/S0196-8904(99)00173-9
[46] Powers JT, Margossian B, Smith BA. Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data. IEEE Computer Applications in Power 1991; 4: 42–47. doi: 10.1109/67.75875
[47] Belley C, Gaboury S, Bouchard B, Bouzouane A. An efficient and inexpensive method for activity recognition within a smart home based on load signatures of appliances. Pervasive and Mobile Computing 2014; 12: 58-78. doi: 10.1016/j.pmcj.2013.02.002
[48] Nguyen M, Alshareef S, Gilani A, Morsi WG. A novel feature extraction and classification algorithm based on power components using single-point monitoring for NILM. In: Canadian Conference on Electrical and Computer Engineering; Halifax, NS, Canada; 2015. pp. 37-40.
[49] Srinivasan D, Ng WS, Liew AC. Neural-network-based signature recognition for harmonic source identification. IEEE Transactions on Power Delivery 2006; 21: 398-405. doi: 10.1109/TPWRD.2005.852370
[50] Najmeddine H, El Khamlichi DK, Pasquier C, Faure C, Kerroum K et al. State of art on load monitoring methods. In: IEEE 2nd International Power and Energy Conference; Johor Bahru, Malaysia; 2008. pp. 1256–1258.
[51] Dong M, Meira PC, Xu W, Chung CY. Non-intrusive signature extraction for major residential loads. IEEE Transactions on Smart Grid 2013; 4: 1421-1430. doi: 10.1109/TSG.2013.2245926
[52] Chang HH, Lee MC, Lee WJ, Chien CL, Chen N. Feature Extraction-Based Hellinger Distance Algorithm for Nonintrusive Aging Load Identification in Residential Buildings. IEEE Transactions on Industry Applications 2016; 52: 2031–2039. doi: 10.1109/IAS.2015.7356778
[53] Ruzzelli A, Nicolas C, Schoofs A, O’Hare G. Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor. IEEE SECON 2010. pp. 1 – 9. doi: 10.1109/SECON.2010.5508244
[54] Figueiredo M, De Almeida A, Ribeiro B. Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems. Neurocomputing 2012; 96: 66-73. doi: 10.1016/j.neucom.2011.10.037
[55] Lam HY, Fung GS, Lee WK. A novel method to construct taxonomy electrical appliances based on load signatures. IEEE Transactions on Consumer Electronics 2014; 53: 653-660. doi: 10.1109/TCE.2007.381742
[56] Hassan T, Javed F, Arshad N. An empirical investigationof V-I trajectory based load signatures for non-intrusive load monitoring. IEEE Transactions on Smart Grid 2014; 5: 870-878. doi: 10.1109/TSG.2013.2271282
[57] Wang AL, Chen BX, Wang CG, Hua DD. Nonintrusive load monitoring algorithm based on features of V–I trajectory. Electric Power Systems Research 2018; 157: 134-144. doi: 10.1016/j.epsr.2017.12.012
[58] Baets LD, Ruyssinck J, Develder C, Dhaene T, Deschrijver D. Appliance classification using VI trajectories and convolutional neural networks. Energy and Buildings 2018; 158: 32-36. doi: 10.1016/j.enbuild.2017.09.087
[59] Teshome DF, Huang TD, Member KLS. Distinctive Load Feature Extraction Based on Fryze’s Time-Domain Power Theory. IEEE Power and Energy Technology Systems Journal 2016; 3: 60-70. doi: 10.1109/JPETS.2016.2559507
[60] Eckmann JP, Kamphorst S, Ruelle D. Recurrence plots of dynamical systems. Europhysics Letters 1987; 4: 973–977.
[61] Popescu F, Enache F, Vizitiu IC, Ciotîrnae P. Recurrence plot analysis for characterization of appliance load signature. In: International Conference on Communications (COMM); Bucharest, Romania; 2014. pp. 1-4.
[62] Faustine A, Pereira L. Improved appliance classification in non-intrusive load monitoring using weighted recurrence graph and convolutional neural networks. Energies 2020; 13. doi: 10.3390/en13133374
[63] Faustine A, Pereira L, Klemenjak C. Adaptive weighted recurrence graphs for appliance recognition in non-intrusive load monitoring. IEEE Transactions on Smart Grid 2021; 12: 398-406. doi: 10.1109/TSG.2020.3010621
[64] Leeb SB, Kirtley JL. A multiscale transient event detector for nonintrusive load monitoring. In: 19th Annual Conference of IEEE Industrial Electronics; Maui, HI, USA; 1993. pp. 354-359.
[65] Leeb SB, LeVan MS, Kirtley Jr JL, Sweeney JP. Development and validation of a transient event detector. AMP Journal of Technology 1993; 3: 69-74. doi: 10.1.1.203.5538
[66] Cole AI, Albicki A. Data extraction for effective nonintrusive identification of residential power loads. In: IEEE Instrumentation and Measurement Technology Conference; St. Paul, MN, USA; 1998. pp. 812-815.
[67] Cole AI, Albicki, A. Algorithm for nonintrusive identification of residential appliances. In: IEEE International Symposium on Circuits and Systems (ISCAS); Monterey, CA, USA; 1998. pp. 338-341.
[68] Shaw SR, Leeb SB, Norford LK, Cox RW. Nonintrusive load monitoring and diagnostics in power systems. IEEE Transactions on Instrumentation and Measurement 2008; 57: 1445-1454. doi: 10.1109/TIM.2008.917179
[69] Chan WL, So ATP, Lai LL. Harmonics load signature recognition by wavelets transforms. In: International Conference on Electric Utility Deregulation and Restructuring and Power Technologies; London, UK; 2000. pp. 666-671.
[70] Chang H, Chen K, Tsai Y, Lee W. A New Measurement Method for Power Signatures of Nonintrusive Demand Monitoring and Load Identification. IEEE Transactions on Industry Applications 2012; 48: 764-771. doi: 10.1109/TIA.2011.2180497
[71] Su Y, Lian K, Chang H. Feature Selection of Non-intrusive Load Monitoring System Using STFT and Wavelet Transform. In: IEEE 8th International Conference on e-Business Engineering; Beijing, China; 2011. pp. 293-298.
[72] Gray M, Morsi WG. Application of wavelet-based classification in non-intrusive load monitoring. In: Canadian Conference on Electrical and Computer Engineering; Halifax, NS, Canada; 2015. pp. 41-45.
[73] Kim H, Marwah M, Arlitt M, Lyon G, Han J. Unsupervised disaggregation of low frequency power measurements. In: 11th SIAM International Conference on Data Mining; Mesa, Arizona, USA; 2011. pp. 747-758.
[74] Zeifman M. Disaggregation of home energy display data using probabilistic approach. IEEE Transactions on Consumer Electronics 2012; 58: 23-31. doi: 10.1109/TCE.2012.6170051
[75] Koutitas GC, Tassiulas L. Low Cost Disaggregation of Smart Meter Sensor Data. IEEE Sensors Journal 2016; 16. doi: 10.1109/JSEN.2015.2501422
[76] Wang Z, Zheng G. Residential appliances identification and monitoring by a nonintrusive method. IEEE Transactions on Smart Grid 2012; 3: 80-92. doi: 10.1109/TSG.2011.2163950
[77] Himeur Y, Alsalemi A, Bensaali, F, Amira A. Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k- nearest neighbors classifier. Sustainable Cities and Society 2021; 67. doi: 10.1016/j.scs.2021.102764
[78] Chowdhury D, Hasan M, Rahman Khan MZ. Statistical features extraction from current envelopes for non-intrusive appliance load monitoring. In: 2020 SoutheastCon; Raleigh, NC, USA; 2020. pp. 1-5.
[79] Chang HH, Lin CL. A new method for load identification of nonintrusive energy management system in smart home. In: IEEE 7th International Conference on E-Business Engineering; Shanghai, China; 2010. pp.351-357.
[80] Chang H, Lin L, Chen N, Lee W. Particle-swarm optimization-based nonintrusive demand monitoring and load identification in smart meters. IEEE Transactions on Industry Applications 2013; 49: 2229–2236 doi: 10.1109/TIA.2013.2258875
[81] Gao J, Giri S, Kara E, Bergés M. Plaid: A public dataset of high-resolution electrical appliance measurements for load identification research. Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings 2014; 198–199. doi: 10.1145/2674061.2675032
[82] Kahl M, Haq A, Kriechbaumer T, Jacobsen HA. Whited- a worldwide household and industry transient energy data set. In: International Workshop on Non-Intrusive Load Monitoring; 2016.
[83] Dongsong Z, Qi M. A load identification algorithm based on SVM. In: First International Conference on Electronics Instrumentation and Information Systems (EIIS); Harbin, China; 2017. pp. 1-5.
[84] Laughman C, Lee K, Cox R, Shaw S, Leeb S et al. Power signature analysis. IEEE Power and Energy Magazine 2003; 1: 56-63. doi: 10.1109/MPAE.2003.1192027
[85] Basu K, Debusschere V, Bacha S, Maulik U, Bondyopadhyay S. Nonintrusive load monitoring: A temporal multilabel classification approach. IEEE Transactions on Industrial Informatics 2015; 11: 262-270. doi: 10.1109/TII.2014.2361288
[86] Kaliberda M, Lytvynenko L, Pogarsky S. Method of singular integral equations in diffraction by semi-infinite grating: H-polarization case. Turkish Journal of Electrical Engineering & Computer Sciences 2017; 25 (6): 4496-4509. doi: 10.3906/elk-1703-170
[87] Zhang X, Luan Z, Zhang Z. Steady state load decomposition method combining template matching with k-nearest neighbor algorithms. In: International Conference on Smart Grid and Clean Energy Technologies; Kajang, Malaysia; 2018. pp. 250–254.
[88] Mueller JA, Kimball JW. Accurate Energy Use Estimation for Nonintrusive Load Monitoring in Systems of Known Devices. IEEE Transactions on Smart Grid 2018; 9: 2797–2808. doi: 10.1109/TSG.2016.2620120
[89] Ridi A, Gisler C, Hennebert J. Appliance and state recognition using Hidden Markov Models. In: International Conference on Data Science and Advanced Analytics (DSAA); Shanghai, China; 2014. pp. 270–276.
[90] Berges M, Goldman E, Matthews HS, Learning systems for electric consumption of buildings. In: Proceedings of the 2009 ASCE International Workshop on Computing in Civil Engineering; 2009. pp. 1-10.
[91] Liao J, Elafoudi G, Stankovic L, Stankovic V. Nonintrusive appliance load monitoring using low-resolution smart meter data. In: IEEE International Conference on Smart Grid Communications; Venice, Italy; 2014. pp. 535-540.
[92] Stankovic L, Stankovic V, Liao J, Wilson C. Measuring the energy intensity of domestic activities from smart meter data. Applied Energy 2016; 183: 1565-1580. doi: 10.1016/j.apenergy.2016.09.087
[93] Guedes JD, Ferreira DD, Barbosa BH. A non-intrusive approach to classify electrical appliances based on higher-order statistics and genetic algorithm. Electric Power Systems Research 2016; 140: 65-69. doi: 10.1016/j.epsr.2016.06.042
[94] Liu Y, Wang X, Zhao L, Liu Y. Admittance-based load signature construction for non-intrusive appliance load monitoring. Energy and Buildings 2018; 171: 209-219. doi: 10.1016/j.enbuild.2018.04.049
[95] De Baets L, Dhaene T, Deschrijver D, Develder C, Berges M. VI-Based Appliance Classification Using Aggregated Power Consumption Data. In: IEEE International Conference on Smart Computing (SMARTCOMP); Taormina, Italy; 2014. pp. 179-186.
[96] Meehan P, McArdle C, Daniels S. An Efficient, Scalable Time-Frequency Method for Tracking Energy Usage of Domestic Appliances Using a Two-Step Classification Algorithm. Energies 2014; 7: 7041-7066. doi: 10.3390/en7117041
[97] Yang C, Soh C, Yap V. A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency. Energy Efficiency 2018; 11. doi:10.1007/s12053-017-9561-0.
[98] Marchiori A, Hakkarinen D, Han Q, Earle L. Circuit-Level Load Monitoring for Household Energy Management. Pervasive Computing 2011; 10: 40 – 48. doi: 10.1109/MPRV.2010.72
[99] Hock D, Kappes M, Ghita B. Non-Intrusive Appliance Load Monitoring using Genetic Algorithms. IOP Conference Series: Materials Science and Engineering 2018; 366. doi: 10.1088/1757-899X/366/1/012003
[100] Egarter D, Sobe A, Elmenreich W. Evolving non-intrusive load monitoring. Applications of Evolutionary Computation 2013; 7835. doi: 10.1007/978-3-642-37192-9-19
[101] Suzuki K, Inagaki S, Suzuki T, Nakamura H, Ito K. Nonintrusive appliance load monitoring based on integer programming. In: SICE Annual Conference; Tokyo, Japan; 2008. pp. 2742–2747.
[102] Bhotto MZA, Makonin S, Bajić IV. Load disaggregation based on aided linear integer programming. IEEE Transactions onCircuits and Systems II: Express Briefs 2017; 64: 792-796. doi: 10.1109/TCSII.2016.2603479
[103] De Baets L, Ruyssinck J, Develder C, Dhaene T, Deschrijver D. On the Bayesian optimization and robustness of event detection methods in NILM. Energy and Buildings 2017; 145: 57-66. doi: 10.1016/j.enbuild.2017.03.061
[104] Machlev R, Belikov J, Beck Y, Levron Y. MO-NILM: A multi-objective evolutionary algorithm for NILM classification. Energy and Buildings 2019; 199: 134-144. doi: 10.1016/j.enbuild.2019.06.046
[105] Gonçalves H, Ocneanu A, Bergés M, Fan RH. Unsupervised disaggregation of appliances using aggregated consumption data. In: KDD 2011 Workshop on Data Mining Applications for Sustainability; 2011. pp. 21-24.
[106] Liu Q, Kamoto, KM, Liu X, Sun M, Linge N. Low Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models. IEEE Transactions on Consumer Electronics 2019; 65: 28-37. doi: 10.1109/TCE.2019.2891160
[107] Ghahramani Z, Jordan MI. Factorial Hidden Markov Models. Machine Learning 1997; 245-273. doi: 10.1023/A:1007425814087
[108] Aiad M, Lee PH. Unsupervised approach for load disaggregation with devices interactions. Energy and Buildings 2016; 116; 96-103. doi: 10.1016/j.enbuild.2015.12.043
[109] Zhao B, Stankovic L, Stankovic V. On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing. IEEE Access 2016; 4: 1784–1799. doi: 10.1109/ACCESS.2016.2557460
[110] He K, Stankovic L, Liao J, Stankovic V. Non-intrusive load disaggregation using graph signal processing. IEEE Transactions on Smart Grid 2018; 9: 1739–1747. doi: 10.1109/TSG.2016.2598872
[111] Iwayemi A, Zhou C. SARAA: Semi-Supervised Learning for Automated Residential Appliance Annotation. IEEE Transactions on Smart Grid 2017; 8: 779–786. doi: 10.1109/TSG.2015.2498642
[112] Gillis J, Morsi WG. Non-intrusive load monitoring using orthogonal wavelet analysis. In: Canadian Conference on Electrical and Computer Engineering; Vancouver, BC, Canada; 2016. pp. 1-5.
[113] Renaux D, Pottker F, Ancelmo H,Lazzaretti A, Erig LC, Linhares R et al. A Dataset for Non-Intrusive Load Monitoring: Design and Implementation. Energies 2020; 20: 1-35. doi: 110.3390/en13205371
[114] Klemenjak C, Kovatsch C, Herold Manuel, Elmenreich W. A synthetic energy dataset for non-intrusive load monitoring in households. Scientific Data 2020. doi: 10.1038/s41597-020-0434-6
[115] Kolter JZ, Matthew JJ. REDD: A public data set for energy disaggregation research. Artif. Intell. 2011; 25.
[116] Anderson K, Ocneanu A, Carlson DR, Rowe A, Bergés M. BLUED: A fully labeled public dataset for event-based non-intrusive load monitoring research In: Procededings of the 2nd KDD Workshop on Data Mining Applications in Sustainability (SustKDD); ACM, Beijing, China; 2012. pp. 1-5.
[117] Reinhardt A, Baumann P, Burgstahler D, Hollick M, Chonov H et al. On the accuracy of appliance identification based on distributed load metering data. In: 2012 Sustainable Internet and ICT for Sustainability (SustainIT) 2012. pp. 1-9.
[118] Monacchi A, Egarter D, Elmenreich W, D’Alessandro S, Tonello A. An energy consumption dataset of households in Italy and Austria. In: 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm); 2014. pp. 511–516.
[119] Kelly J, Knottenbelti W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. In: Scientific Data 2 2015; 2: doi: 10.1038/sdata.2015.7
[120] Picon T, Meziane MN, Ravier P, Lamarque G, Novello C et al. COOLL: controlled on/off loads library, a public dataset of high-sampled electrical signals for appliance identification; 2016.
[121] Ribeiro M, Pereira L, Quintal F, Nunes N. SustDataED: A Public Dataset for Electric Energy Disaggregation Research. In Proceedings of the 2016 ICT for Sustainability; Amsterdam, Netherlands; 2016; pp. 244–245.
[122] Kriechbaumer T, Jacobsen HA. BLOND, a building-level office environment dataset of typical electrical appliances. Scientific Data 2018; 5: doi: 10.1038/sdata.2018.48
[123] Xinmei Y, Peng H, Yao D, Rosemary A, Vandana R et all. Residential Electrical Load Monitoring and Modeling – State of the Art and Future Trends for Smart Homes and Grids. Electric Power Components and Systems 2020; 48: 1-19. doi:10.1080/15325008.2020.1834019
[124] Pereira L, Nunes N. Performance evaluation in non-intrusive load monitoring: Datasets, metrics, and tools-A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2018; 8. doi:10.1002/widm.1265
[125] Chang HH, Lee MC, Lee WJ, Chien CL, Chen N. Feature Extraction-Based Hellinger Distance Algorithm for Nonintrusive Aging Load Identification in Residential Buildings. IEEE Transactions on Industry Applications 2016; 52: 2031–2039. doi: 10.1109/IAS.2015.7356778
[126] Faustine A, Mvungi NH, Kaijage SF, Kisangiri M. A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem. ArXiv 2017; abs/1703.00785.
[127] Altrabalsi H, Stankovic V, Liao J, Stankovic L. Low-complexity energy disaggregation using appliance load modelling. AIMS Energy 2016; 4 (1): 1-21; doi: 10.3934/energy.2016.1.1
[128] Makonin S, Popowich F. Nonintrusive load monitoring (NILM) performance evaluation. Springer Energy Efficiency 2014; 8 (4): 809-814; doi: 10.1007/s12053-014-9306-2
[129] Liang J, Ng SK, Kendall G, Cheng JW. Load signature studypart I: Basic concept, structure, and methodology. IEEE Transactions on Power Delivery 2009; 25 (6): 551-560. doi: 10.1109/TPWRD.2009.2033799
[130] Ziwei X, Wenjie G, Jiaqi Y, Ying Z, Cheng F. Cooling Load Disaggregation Using a NILM Method Based on Random Forest for Smart Buildings. Sustainable Cities and Society 2021; 74: 103202; doi: 10.1016/j.scs.2021.103202
[131] Mueller JA, Kimball JW. Accurate Energy Use Estimation for Nonintrusive Load Monitoring in Systems of Known Devices. IEEE Transactions on Smart Grid 2018; 9: 2797–2808. doi: 10.1109/TSG.2016.2620120
[132] Qi L, Kondwani K, Xiaodong L, Mingxu S, Nigel L. Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models. IEEE Transactions on Consumer Electronics 2019; 1 (1): 1-1. doi: 10.1109/TCE.2019.2891160
[133] Desai S, Alhadad R, Mahmood A, Chilamkurti N, Rho S. Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm. Sensors 2019; 19 (23): 5236 doi:10.3390/s19235236