• Divya M. Department of Electrical Engineering Fr. C. Rodrigues Institute of Technology, Navi Mumbai



Energy disaggregation, Non-Intrusive Load Monitoring (NILM), Smart meters, Data granularity


Smart meter technology presents an opportunity to gain better insights into consumer appliance usage and consumption behaviour.  Load monitoring can provide valuable data on appliance specific energy consumption statistics which in turn will be useful for the consumer to evolve an optimum energy utilization strategy. In the utility point of view, the data acquired in this manner could be used to evolve better target demand side management programs, including demand response and energy efficiency. Non-intrusive load monitoring (NILM) is a consumer energy disaggregation technique that segregates individual appliance energy consumption from the total energy measured at the mains. Unlike intrusive load monitoring, it does not require separate meters to measure individual device consumption. This field has garnered lot of research interest recently, owing to emergence of smart grid technologies and advances in smart metering. Machine learning algorithms are predominantly used to solve NILM problems. In view of concerns regarding customer privacy and economics, low frequency smart meters are preferred. There are many challenges involved in using low granularity data for NILM algorithms. This work summarizes the current state of the art of NILM methods for low rate smart meter data. The limitations of the present methods and scope for future work are also presented.


Download data is not yet available.


“Smart meters,” Energy Efficiency Services Limited, [Online]. Available: [Accessed 31 July 2020].

G. W. Hart, “Prototype non-intrusive applicance load monitor,” MIT Energy Laboratory and Electric Power Research Institute, 1985.

J. R. Herrero, Á. L. Murciego, A. L. Barriuso and D. H. d. l. Iglesia, “Non Intrusive Load Monitoring (NILM): A State of the Art,” in 15th International Conference, PAAMS 2017, 2017.

N. Batra, J. Kelly, O. Parson, H. Dutta, W. Knottenbelt, A. Rogers, A. Singh and M. Srivastava, “NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring,” in fifth International Conference on Future Energy Systems (ACM e-Energy)2014, 2014.

H. Liu, Non-intrusive Load Monitoring: Theory, Technologies and Applications, Springer Singapore, 2019.

G. W. Hart, “Nonintrusive appliance load monitoring,” Proceedings of the IEEE, vol. 80, no. 12, pp. 1870 - 1891, 1992.

L. Pereira and N. Nunes, “Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review,” WIREs Data Mining and Knowledge Discovery , vol. 8, no. 6, pp. 1-17, 2018.

R. Bonfigli and S. Squartini, Machine Learning Approaches to Non-Intrusive Load Monitoring, Springer Nature Switzerland AG, 2019.

K. Suzuki, S. Inagaki, T. Suzuki, H. Nakamura and K. Ito, “ Nonintrusive appliance load monitoring based on integer programming,” in Proc.SICE Annual Conference-2008, 2008.

M. Z. A. Bhotto, S. Makonin and I. V. Bajić, “Load Disaggregation Based on Aided Linear Integer Programming,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 64, no. 7, pp. 792 - 796, 2017.

F. M. Wittmann, J. C. López and M. J. Rider, “Nonintrusive Load Monitoring Algorithm Using Mixed-Integer Linear Programming,” IEEE Transactions on Consumer Electronics , vol. 64, no. 2, pp. 180-187, 2018.

M. Baranski and J. Voss, “Genetic Algorithm for Pattern Detection in NIALM Systems,” in IEEE International Conference on Systems, Man and Cybernetics, 2004.

M. Weiss, A. Helfenstein, F. Mattern and T. Staake, “Leveraging smart meter data to recognize home appliances,” in IEEE International Conference on Pervasive Computing and Communications, 2012.

A. Marchiori, D. Hakkarinen, Q. Han and L. Earle, “ Circuit-Level Load Monitoring for Household Energy Management,” IEEE Pervasive Computing , vol. 10, no. 1, pp. 40-48, 2011.

A. G. Ruzzelli, C. Nicolas, A. Schoofs and G. M. P. O'Hare, “Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor,” in 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2010.

T. Zia, D. Bruckner and A. Zaidi, “A hidden Markov model based procedure for identifying household electric loads,” in IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, 2011.

D. Srinivasan, “Neural-network-based signature recognition for harmonic source identification,” IEEE Transactions on Power Delivery, vol. 21, no. 1, pp. 398 - 405, 2006.

G.-y. Lin, S.-c. Lee, J. Y.-j. Hsu and W.-r. Jih, “Applying power meters for appliance recognition on the electric panel,” in 2010 5th IEEE Conference on Industrial Electronics and Applications, 2010.

K. Kamoto, Q. Liu and X. Liu, “Unsupervised Energy Disaggregation of Home Appliances,” in Cloud Computing and Security: Third International Conference, ICCCS 2017, 2017.

H. Goncalves, A. Ocneanu and M. Berges, “Unsupervised disaggregation of appliances using aggregated consumption data,” in 1st KDD Workshop on Data Mining Applications in Sustainability (SustKDD), 2011.

H. Shao, M. Marwah and N. Ramakrishnan, “A Temporal Motif Mining Approach to Unsupervised Energy Disaggregation,” in 27th AAAI conference on Artificial Intelligence, 2013.

H. Kim, M. Marwah, M. F. Arlitt and G. Lyon, “Unsupervised Disaggregation of Low Frequency Power Measurements,” in Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011, 2011.

J. Z. Kolter and T. S. Jaakkola, “Approximate inference in additive factorial HMMs with application to energy disaggregation,” in International Conference on Artificial Intelligence and Statistics, 2012.

B. Zhao, M. Ye, L. Stankovic and V. Stankovic, “Non-intrusive load disaggregation solutions for very low-rate smart meter data,” Elsevier Applied Energy, vol. 268, pp. 1-16, 2020.

G. Zhang, G. G. Wang, H. Farhangi and A. Palizban, “Data mining of smart meters for load category based disaggregation of residential power consumption,” Elsevier Sustainable Energy, Grids and Networks, vol. 10, pp. 92-103, 2017.

B. Zhao, L. Stankovic and V. Stankovic, “Electricity usage profile disaggregation of hourly smart meter data,” in Proceedings of NILMWorkshop 2018, 2018.

J. Holweger, M. Dorokhova, L. Bloch, C. Ballif and N. Wyrsch, “Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households,” Elsevier Sustainable Energy, Grids and Networks, vol. 19, pp. 1-23, 2019.

C. Rottondi, M. Derboni, D. Piga and A. E. Rizzoli, “An optimisation-based energy disaggregation algorithm for low frequency smart meter data,” Springer Energy Informatics, vol. 2, no. 1, pp. 1-11, 2019.

D. Piga, A. Cominola, M. Giuliani, A. Castelletti and A. E. Rizzoli, “Sparse Optimization for Automated Energy End Use Disaggregation,” IEEE Transactions on Control Systems Technology, vol. 24, no. 3, pp. 1044 - 1051, 2016.

S. Welikala, C. Dinesh, R. I. Godaliyadda, M. P. B. Ekanayake and J. Ekanayake, “Robust Non-Intrusive Load Monitoring (NILM) with unknown loads,” in IEEE International Conference on Information and Automation for Sustainability (ICIAfS), 2016.

S. Biansoongnern and B. Plangklang, “Nonintrusive load monitoring (NILM) using an Artificial Neural Network in embedded system with low sampling rate,” in 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2016.

L. Kong, D. Yang and Y. Luo, “Non-Intrusive Load Monitoring and Identification Based on Maximum Likelihood Method,” in IEEE International Conference on Energy Internet (ICEI), 2017.

B. Huang, M. Knox, K. Bradbury, L. M. Collins and R. G. Newell, “Non-intrusive load monitoring system performance over a range of low frequency sampling rates,” in IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA), 2017.

S. Lynch, “On the relationship between sampling rate and Hidden Markov Models Accuracy in Non-Intrusive Load Monitoring,” in Irish Conference on Artificial Intelligence and Cognitive Science, 2017.

Q. Yuan, H. Wang, B. Wu and Y. Song, “A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data,” Future Internet, MDPI, vol. 11, pp. 1-23, 2019.

H. Wang, W. Yang, T. Chen and Q. Yang, “An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data,” Sustainability, MDPI, vol. 11, pp. 1-16, 2019.

A. Miyasawa, Y. Fujimoto and Y. Hayashi, “Energy disaggregation based on smart metering data via semi-binary nonnegative matrix factorization,” Elsevier, Energy and Buildings, vol. 183, pp. 547-558, 2019.

M. Liang, Y. Meng, N. Lu, D. Lubkeman and A. Kling, “ HVAC load Disaggregation using Low-resolution Smart Meter Data,” in IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2019.

S. Welikala, N. Thelasingha, M. Akram, P. B. Ekanayake, R. I. Godaliyadda and J. B. Ekanayake, “Implementation of a robust real-time non-intrusive load monitoring solution,” Elsevier Applied Energy, vol. 238, pp. 1519-1529, 2019.

P. A. Schirmer, “Improving Energy Disaggregation Performance Using Appliance-Driven Sampling Rates,” in 27th European Signal Processing Conference (EUSIPCO), 2019.

X. Shi, H. Ming, S. Shakkottai, L. Xie and J. Yao, “Nonintrusive load monitoring in residential households with low-resolution data,” Elsevier Applied Energy, vol. 252, pp. 1-10, 2019.

N. Batra, A. Singh and K. Whitehouse, “Neighbourhood NILM: A Big-data Approach to Household Energy Disaggregation,” arXiv preprint arXiv:1511.02900, 2015.

A. Zoha, Q. H. Abbasi and M. A. Imran, “A Non‐Event Based Approach for Non‐Intrusive Load Monitoring,” in Wireless Automation as an Enabler for the Next Industrial Revolution, 2020 John Wiley & Sons Ltd., 2019, pp. 173-191.




How to Cite

Divya M., “NON-INTRUSIVE LOAD DISAGGREGATION METHODS FOR LOW-RATE SMART METER DATA”, IEJRD - International Multidisciplinary Journal, vol. 6, no. ICRRTNB, p. 12, Nov. 2021.