Non-Intrusive Load Monitoring Design Using K-Means Clustering Extreme Learning Machine

Dimas Fajar Uman Putra; Ontoseno Penangsang; Adi Soeprijanto

doi:10.15866/iremos.v11i4.13969

Non-Intrusive Load Monitoring Design Using K-Means Clustering Extreme Learning Machine

Dimas Fajar Uman Putra^(1*), Ontoseno Penangsang⁽²⁾, Adi Soeprijanto⁽³⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/iremos.v11i4.13969

Abstract

This paper purposes a new algorithm to determine the on-off status for Non-Intrusive Load Monitoring (NILM) by using K-means Clustering Extreme Learning Machine (ELM). K-means Clustering is used in this research to cluster the current group from the load. By clustering this current, the input for ELM can be grouped by the current magnitude from wavelet process. This clustering process is done to prevent a singularity matrix that usually happens when the inversed H matrix in ELM process is done. The simulation result shows that the proposed method has a good result as Neural Network when identifying the loads status but it has a faster training and testing time. Singularity occurs when ELM is used without applying the clustering method before.
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Keywords

NILM; K-Means Clustering; ELM

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References

M. Energy and Mineral Resources and S. E. Company, "Electricity Supply Business Plan (RUPTL) State Electricity Company (PT. PLN) (2017-2026)," 2017.

S. M. Tabatabaei, S. Dick, and W. Xu, "Toward Non-Intrusive Load Monitoring via Multi-Label Classification," IEEE Transactions on Smart Grid, vol. 8, pp. 26-40, 2017.
http://dx.doi.org/10.1109/tsg.2016.2584581

S. S. Hosseini, K. Agbossou, S. Kelouwani, and A. Cardenas, "Non-intrusive load monitoring through home energy management systems: A comprehensive review," Renewable and Sustainable Energy Reviews, vol. 79, pp. 1266-1274, 2017.
http://dx.doi.org/10.1016/j.rser.2017.05.096

G. W. HART, "Nonintrusive appliance load monitoring," Proceedings of the IEEE, vol. 80 No. 12, pp. 1870-1891, December 1992.
http://dx.doi.org/10.1109/5.192069

S. Althaher, P. Mancarella, and J. Mutale, "Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints," IEEE Transactions on Smart Grid, vol. 6, pp. 1874-1883, 2015.
http://dx.doi.org/10.1109/tsg.2014.2388357

M. Dong, P. C. M. Meira, W. Xu, and C. Y. Chung, "Non-Intrusive Signature Extraction for Major Residential Loads.," IEEE Transactions on Smart Grid, vol. 4, NO. 3, pp. 1421-1430, September 2013.
http://dx.doi.org/10.1109/tsg.2013.2245926

T. Hassan, F. Javed, and N. Arshad, "An Empirical Investigation of V-I Trajectory Based Load Signatures for Non-Intrusive Load Monitoring," IEEE Transactions on Smart Grid, vol. 5, pp. 870-878, 2014.
http://dx.doi.org/10.1109/tsg.2013.2271282

M. Nardello, M. Rossi, and D. Brunelli, "A Low-cost Smart Sensor for Non Intrusive Load conference," IEEE, pp. 1362-1368, 2017.
http://dx.doi.org/10.1109/isie.2017.8001444

B.-G. Kim, Y. Zhang, M. van der Schaar, and J.-W. Lee, "Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning," IEEE Transactions on Smart Grid, vol. 7, pp. 2187-2198, 2016.
http://dx.doi.org/10.1109/tsg.2015.2495145

K. Basu, V. Debusschere, S. Bacha, U. Maulik, and S. Bondyopadhyay, "Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach," IEEE Transactions on Industrial Informatics, vol. 11, pp. 262-270, 2015.
http://dx.doi.org/10.1109/tii.2014.2361288

M. Zeifman and K. Roth, "Nonintrusive Appliance Load Monitoring: Review and Outlook," IEEE Transactions on Consumer Electronics, vol. 57, pp. 76-84, 2011.
http://dx.doi.org/10.1109/tce.2011.5735484

J. G. Roos, I. E. Lane, E. C. Botha, and G. P. Hancke, "Using Neural Networks for Non-Intrusive Monitoring of Industrial Electrical Loads," IEEE Instrumentation and Measurement Technology Conf., IMTC94 Conf. Proc., 10th Anniversary. Advanced Technologies in I&M vol. 3, pp. 1115–1118, May 10-12 1994
http://dx.doi.org/10.1109/imtc.1994.351862

Uman Putra, D., Penangsang, O., Soeprijanto, A., Miyauchi, H., Unbalanced Radial Distribution Systems Voltage Stability Index Using Extreme Learning Machine, (2016) International Review of Electrical Engineering (IREE), 11 (4), pp. 428-434.
http://dx.doi.org/10.15866/iree.v11i4.8916

G. B. Huang, Q. Y. Zhu, and S. C. K., "Extreme Learning Machine A New Learning Scheme of Feedforward Neural Networks," Proc. IJCNN, Budapest, Hungary, vol. 2, pp. 985–990., July 25–29 2004.
http://dx.doi.org/10.1109/ijcnn.2004.1380068

E. J. Aladesanmi and K. A. Folly, "Overview of non-intrusive load monitoring and identification techniques," IFAC-PapersOnLine, vol. 48-30, pp. 415–420, 2015.
http://dx.doi.org/10.1016/j.ifacol.2015.12.414

J. Liang, S. K. K. Ng, G. Kendall, and J. W. M. Cheng, "Load Signature Study—Part I: Basic Concept, Structure, and Methodology," IEEE Transactions on Power Delivery, vol. 25, No. 2, pp. 551-560, April 2010.
http://dx.doi.org/10.1109/tpwrd.2009.2033799

H.-H. Chang, K.-L. Lian, Y.-C. Su, and W.-J. Lee, "Power-Spectrum-Based Wavelet Transform for Nonintrusive Demand Monitoring and Load Identification," IEEE Transactions on Industry Applications, vol. 50, pp. 2081-2089, 2014.
http://dx.doi.org/10.1109/tia.2013.2283318

Koumir, M., El Bakri, A., Boumhidi, I., Cascading Control Based on Intelligent Algorithms for a Wind Turbine Equipped with a Doubly-Fed Induction Generator, (2017) International Review of Automatic Control (IREACO), 10 (4), pp. 332-340.
http://dx.doi.org/10.15866/ireaco.v10i4.11814

Bazi, S., Nait Said, M., Extreme Learning Machines and Particle Swarm Optimization for Induction Motor Faults Detection and Classification, (2015) International Review of Electrical Engineering (IREE), 10 (4), pp. 501-509.
http://dx.doi.org/10.15866/iree.v10i4.7048

Demidova, L., Sokolova, Y., Nikulchev, E., Use of Fuzzy Clustering Algorithms Ensemble for SVM Classifier Development, (2015) International Review on Modelling and Simulations (IREMOS), 8 (4), pp. 446-457.
http://dx.doi.org/10.15866/iremos.v8i4.6825

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