An Aggregated Approach Towards NILM on ACS-F2 Using Machine Learning

Arsalan Ali Mujtaba; Sarmad Rafique; Gul Muhammad Khan

Authors

Arsalan Ali Mujtaba Department of Electrical Engineering, University of Engineering and Technology Peshawar, Pakistan
Sarmad Rafique Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Pakistan
Gul Muhammad Khan Department of Electrical Engineering, University of Engineering and Technology Peshawar, Pakistan

Keywords:

Non-Intrusive Load Monitoring (NILM), Intrusive Load Monitoring (ILM), Appliance Identification, Load Patterns, AEON toolkit, Energy Disaggregation.

Abstract

The Energy Sector across the globe is experiencing rapid growth, driven by Internet of Things (IoT) integration technologies and advanced algorithms. This evolution is particularly evident in the ongoing competition among tech companies in the development of smart metering solutions. Despite these advancements, a critical challenge persists— the lack of definitive technical protocols for monitoring the total usage or power signatures of individual appliances, referred to as non-intrusive load monitoring (NILM) in aggregate. While intrusive load monitoring (ILM) provides very accurate and thorough insights, non-intrusive methods are essential to address losses specially in residential areas. In this research a groundbreaking approach is proposed towards handling NILM problems by analyzing and aggregating the load patterns of four key appliances of daily use, namely the Coffee Machine, Fridge, Kettle, and Laptop from the ACS-F2 dataset. The generated aggregated dataset, is systematically combined using electrical formulations to yield the desired data which reflects the simultaneous operation of multiple appliances, this has been explored for the first time in the known literature. The proposed dataset contains around 6750 aggregated appliance load patterns for both training and testing. Furthermore, multiple Time Series Classifiers (TSC) were gauged using a suite of evaluation metrics, on the proposed dataset and an accuracy of 92.1% was achieved by the CATCH22 classifier.

References

“Smart metering and demand response continue to increase in US.” Accessed: May 07, 2024. [Online]. Available: https://www.smart-energy.com/industry-sectors/smart-meters/smart-metering-and-demand-response-continue-to-increase-in-us/

Y. H. Lin, “Design and Implementation of an IoT-Oriented Energy Management System Based on Non-Intrusive and Self-Organizing Neuro-Fuzzy Classification as an Electrical Energy Audit in Smart Homes,” Appl. Sci. 2018, Vol. 8, Page 2337, vol. 8, no. 12, p. 2337, Nov. 2018, doi: 10.3390/APP8122337.

D. Guinard, M. Weiss, and V. Trifa, “Are you Energy-Efficient: Sense it on the Web!”, Accessed: May 07, 2024. [Online]. Available: http://code.google.com/webtoolkit/

& S. Hart, G. W., Kern Jr, E. C., “F. C. (1989). U.S. Patent No. 4,858,141. Washington, DC: U.S. Patent and Trademark Office.”.

G. W. Hart, “Nonintrusive Appliance Load Monitoring,” Proc. IEEE, vol. 80, no. 12, pp. 1870–1891, 1992, doi: 10.1109/5.192069.

J. Liang, S. K. K. Ng, G. Kendall, and J. W. M. Cheng, “Load signature studypart II: Disaggregation framework, simulation, and applications,” IEEE Trans. Power Deliv., vol. 25, no. 2, pp. 561–569, Apr. 2010, doi: 10.1109/TPWRD.2009.2033800.

D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67–82, 1997, doi: 10.1109/4235.585893.

A. Ridi, C. Gisler, and J. Hennebert, “ACS-F2 — A new database of appliance consumption signatures,” Int. Conf. Soft Comput. Pattern Recognit., pp. 145–150, Jan. 2014, doi: 10.1109/SOCPAR.2014.7007996.

C. Gisler, A. Ridi, D. Zujferey, O. A. Khaled, and J. Hennebert, “Appliance consumption signature database and recognition test protocols,” 2013 8th Int. Work. Syst. Signal Process. Their Appl. WoSSPA 2013, pp. 336–341, 2013, doi: 10.1109/WOSSPA.2013.6602387.

E. Salihagić, J. Kevric, and N. Doǧru, “Classification of ON-OFF states of appliance consumption signatures,” 2016 11th Int. Symp. Telecommun. BIHTEL 2016, Dec. 2016, doi: 10.1109/BIHTEL.2016.7775722.

“aeon 0.8.1 documentation.” Accessed: May 19, 2024. [Online]. Available: https://www.aeon-toolkit.org/en/stable/

M. Middlehurst, P. Schäfer, and A. Bagnall, “Bake off redux: a review and experimental evaluation of recent time series classification algorithms,” Data Min. Knowl. Discov. 2024, pp. 1–74, Apr. 2023, doi: 10.1007/S10618-024-01022-1/FIGURES/27.

A. Ruano, A. Hernandez, J. Ureña, M. Ruano, and J. Garcia, “NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review,” Energies 2019, Vol. 12, Page 2203, vol. 12, no. 11, p. 2203, Jun. 2019, doi: 10.3390/EN12112203.

L. Yu, H. Li, X. Feng, and J. Duan, “Nonintrusive appliance load monitoring for smart homes: Recent advances and future issues,” IEEE Instrum. Meas. Mag., vol. 19, no. 3, pp. 56–62, Jun. 2016, doi: 10.1109/MIM.2016.7477956.

D. Kelly, “Disaggregation of domestic smart meter energy data,” 2016, doi: 10.25560/49452.

C. Puente, R. Palacios, Y. González-Arechavala, and E. F. Sánchez-Úbeda, “Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques,” Energies 2020, Vol. 13, Page 3117, vol. 13, no. 12, p. 3117, Jun. 2020, doi: 10.3390/EN13123117.

J. Kelly and W. Knottenbelt, “The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes,” Sci. Data 2015 21, vol. 2, no. 1, pp. 1–14, Mar. 2015, doi: 10.1038/sdata.2015.7.

“Individual Household Electric Power Consumption.” Accessed: May 19, 2024. [Online]. Available: https://archive.ics.uci.edu/dataset/235/individual+household+electric+power+consumption

G. F. Angelis, C. Timplalexis, S. Krinidis, D. Ioannidis, and D. Tzovaras, “NILM applications: Literature review of learning approaches, recent developments and challenges,” Energy Build., vol. 261, p. 111951, Apr. 2022, doi: 10.1016/J.ENBUILD.2022.111951.

J. Kelly and W. Knottenbelt, “Neural NILM: Deep neural networks applied to energy disaggregation,” BuildSys 2015 - Proc. 2nd ACM Int. Conf. Embed. Syst. Energy-Efficient Built, pp. 55–64, Nov. 2015, doi: 10.1145/2821650.2821672.

V. M. Salerno and G. Rabbeni, “An Extreme Learning Machine Approach to Effective Energy Disaggregation,” Electron. 2018, Vol. 7, Page 235, vol. 7, no. 10, p. 235, Oct. 2018, doi: 10.3390/ELECTRONICS7100235.

C. H. Lubba et al., “catch22: CAnonical Time-series CHaracteristics,” Data Min. Knowl. Discov., vol. 33, no. 6, pp. 1821–1852, Jan. 2019, doi: 10.1007/s10618-019-00647-x.

A. Dempster, F. Petitjean, and G. I. Webb, “ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels,” Data Min. Knowl. Discov., vol. 34, no. 5, pp. 1454–1495, Sep. 2020, doi: 10.1007/S10618-020-00701-Z/TABLES/2.

M. Middlehurst, J. Large, and A. Bagnall, “The Canonical Interval Forest (CIF) Classifier for Time Series Classification,” Proc. - 2020 IEEE Int. Conf. Big Data, Big Data 2020, pp. 188–195, Dec. 2020, doi: 10.1109/BIGDATA50022.2020.9378424.

P. Cunningham and S. J. Delany, “k-Nearest Neighbour Classifiers - A Tutorial,” ACM Comput. Surv., vol. 54, no. 6, Jul. 2021, doi: 10.1145/3459665.

A. Guillaume, C. Vrain, and W. Elloumi, “Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13363 LNCS, pp. 653–664, 2022, doi: 10.1007/978-3-031-09037-0_53.

B. D. Fulcher and N. S. Jones, “hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction,” Cell Syst., vol. 5, no. 5, pp. 527-531.e3, Nov. 2017, doi: 10.1016/j.cels.2017.10.001.