Proposing a Novel Approach Non-Intrusive Load Monitoring Based on Feature Extraction Matrix and KNN Machine Learning Model

Subject Areas : Communication Engineering

Behrooz Taheri ¹ , Mostafa Sedighizadeh ² , Mohammad Reza Nasiri ³ , Alireza Sheikhi Fini ⁴

1 - Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Faculty of Electrical Engineering, Shahid Beheshti University, Evin, Tehran, Iran
3 - Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
4 - Power System Operation and Planning Research Department, Niroo Research Institute, Tehran, Iran

Received: 2023-08-05 Accepted : 2023-10-20 Published : 2024-02-20

Keywords: Feature matrix, Instantaneous frequency, NILM, Hilbert transform, KNN, Extracting features,

Abstract :

In recent years, the interest in conducting research on non-intrusive load monitoring is increasing strongly due to the increase in electrical energy consumption. Numerous studies have underscored that the implementation of non-intrusive load monitoring methods, apart from various advantages such as load response, increasing the accuracy of load prediction, etc., will increase the level of cost savings for occupants of residential structures. Recently, with the adoption of techniques grounded in deep learning, the use of these methods has also increased in order to load disaggregation. However, the most important problem with these methods is the need for complex hardware in order to train and examine the techniques. For this reason, it is necessary to transfer the power signal sampled from the smart meter to data processing centers and be analyzed. In addition to the need for high-speed communication networks, this also endangers data security. Accordingly, in this article, a non-intrusive load monitoring method is presented based on extracting the feature matrix from the instantaneous frequency signal obtained from the power signal of household appliances. The most important feature of the presented method is to increase the accuracy of the classical KNN model. The presented method has been analyzed using EMBED open-access data, which includes the consumption dataset from three different apartments. The results show that the KNN model attains significantly enhanced accuracy when using the feature matrix data introduced in this article compared to other feature extraction methods.

References:

1. M. Afzalan, F. Jazizadeh, and J. Wang, "Self-configuring event detection in electricity monitoring for human-building interaction," Energy and Buildings, vol. 187, pp. 95-109, 2019, doi: 10.1016/j.enbuild.2019.01.036.


2. R. Gopinath, M. Kumar, C. P. C. Joshua, and K. Srinivas, "Energy management using non-intrusive load monitoring techniques–State-of-the-art and future research directions," Sustainable Cities and Society, vol. 62, p. 102411, 2020, doi: 10.1016/j.scs.2020.102411.


3. J. Froehlich, E. Larson, S. Gupta, G. Cohn, M. Reynolds, and S. Patel, "Disaggregated end-use energy sensing for the smart grid," IEEE pervasive computing, vol. 10, no. 1, pp. 28-39, 2010, doi: 10.1109/mprv.2010.74.


4. R. G. Pratt et al., "The smart grid: An estimation of the energy and CO2 benefits," Pacific Northwest National Lab.(PNNL), Richland, WA (United States), 2010, doi: 10.2172/971103.


5. X. Yang, L. Yang, X. Xiao, Y. Wang, and S. Zhang, "An adaptive lightweight seq2subseq model for non‐intrusive load monitoring," IET Generation, Transmission & Distribution, vol. 16, no. 18, pp. 3706-3718, 2022, doi: 10.1049/gtd2.12558.


6. N. Sadeghianpourhamami, J. Ruyssinck, D. Deschrijver, T. Dhaene, and C. Develder, "Comprehensive feature selection for appliance classification in NILM," Energy and Buildings, vol. 151, pp. 98-106, 2017, doi: 10.1016/j.enbuild.2017.06.042.


7. G. Cui, B. Liu, W. Luan, and Y. Yu, "Estimation of target appliance electricity consumption using background filtering," IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 5920-5929, 2019, doi: 10.1109/tsg.2019.2892841.


8. H. Liu, Non-intrusive Load Monitoring: Theory, Technologies and Applications. Springer Nature, 2019, doi: 10.1007/978-981-15-1860-7.


9. B. M. Mulinari et al., "A new set of steady-state and transient features for power signature analysis based on VI trajectory," in 2019 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), 2019: IEEE, pp. 1-6, doi: 10.1109/isgt-la.2019.8895360.


10. A. Faustine, N. H. Mvungi, S. Kaijage, and K. Michael, "A survey on non-intrusive load monitoring methodies and techniques for energy disaggregation problem," arXiv preprint arXiv:1703.00785, 2017, doi: 10.48550/arXiv.1703.00785.


11. W. Lee, G. Fung, H. Lam, F. Chan, and M. Lucente, "Exploration on load signatures," in International conference on electrical Engineering (ICEE), 2004, vol. 152.


12. T. Hassan, F. Javed, and N. Arshad, "An empirical investigation of VI trajectory based load signatures for non-intrusive load monitoring," IEEE Transactions on Smart Grid, vol. 5, no. 2, pp. 870-878, 2013, doi: 10.1109/pesgm.2014.6938824.


13. C. Laughman et al., "Power signature analysis," IEEE power and energy magazine, vol. 1, no. 2, pp. 56-63, 2003, doi: 10.1109/mpae.2003.1192027.


14. A. M. Ahmed, Y. Zhang, and F. Eliassen, "Generative adversarial networks and transfer learning for non-intrusive load monitoring in smart grids," in 2020 IEEE international conference on communications, control, and computing technologies for smart grids (SmartGridComm), 2020: IEEE, pp. 1-7, doi: 10.1109/smartgridcomm47815.2020.9302933.


15. J. Kelly and W. Knottenbelt, "Neural nilm: Deep neural networks applied to energy disaggregation," in Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments, 2015, pp. 55-64, doi: 10.1145/2821650.2821672.


16. H. Wu and H. Liu, "Non-intrusive load transient identification based on multivariate LSTM neural network and time series data augmentation," Sustainable Energy, Grids and Networks, vol. 27, p. 100490, 2021, doi: 10.1016/j.segan.2021.100490.


17. Y. Zhang et al., "A novel non-intrusive load monitoring method based on ResNet-seq2seq networks for energy disaggregation of distributed energy resources integrated with residential houses," Applied Energy, vol. 349, p. 121703, 2023, doi: 10.1016/j.apenergy.2023.121703.


18. M. M. R. Khan, M. A. B. Siddique, and S. Sakib, "Non-intrusive electrical appliances monitoring and classification using K-nearest neighbors," in 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), 2019: IEEE, pp. 1-5, doi: 10.1109/iciet48527.2019.9290671.


19. M. Hu, S. Tao, H. Fan, X. Li, Y. Sun, and J. Sun, "Non-intrusive load monitoring for residential appliances with ultra-sparse sample and real-time computation," Sensors, vol. 21, no. 16, p. 5366, 2021, doi: 10.3390/s21165366.


20. H. Liu, C. Yu, H. Wu, C. Chen, and Z. Wang, "An improved non-intrusive load disaggregation algorithm and its application," Sustainable cities and society, vol. 53, p. 101918, 2020. , doi: 10.1016/j.scs.2019.101918.


21. A. Harell, S. Makonin, and I. V. Bajić, "Wavenilm: A causal neural network for power disaggregation from the complex power signal," in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019: IEEE, pp. 8335-8339, doi: 10.1109/icassp.2019.8682543.


22. S. Salehimehr, B. Taheri, and M. Sedighizadeh, "Short‐term load forecasting in smart grids using artificial intelligence methods: A survey," The Journal of Engineering, vol. 2022, no. 12, pp. 1133-1142, 2022, doi: 10.1049/tje2.12183.


23. H. Abniki, M. Hajati Samsi, B. Taheri, and S. A. Hosseini, "High Impedance Fault Detection in Power Transmission Lines Using Hilbert Transform and Instantaneous Frequency," International Journal of Industrial Electronics Control and Optimization, vol. 6, no. 1, pp. 1-11, 2023, doi: 10.22111/ieco.2022.42814.1439.


24. A. A. Nazari, F. Razavi, and A. Fakharian, "A novel method to differentiate internal faults and inrush current in power transformers using adaptive sampling and Hilbert transform," Iranian Electric Industry Journal of Quality and Productivity, vol. 11, no. 1, pp. 97-110, 2022.


25. M. Feldman, "Hilbert transform in vibration analysis," Mechanical systems and signal processing, vol. 25, no. 3, pp. 735-802, 2011, doi: 10.1002/9781119991656.


26. B. Taheri, S. Salehimehr, F. Razavi, and M. Parpaei, "Detection of power swing and fault occurring simultaneously with power swing using instantaneous frequency," Energy Systems, vol. 11, no. 2, pp. 491-514, 2020, doi: 10.1007/s12667-018-00320-0.


27. B. Taheri, S. A. Hosseini, M. Sedighizadeh, and M. Khatibi, "A moving window numerical distance protection based on flat-top signal windowing," Arabian Journal for Science and Engineering, vol. 47, no. 11, pp. 14249-14266, 2022, doi: 10.1007/s13369-022-06709-5.


28. G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, "KNN model-based approach in classification," in On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, November 3-7, 2003. Proceedings, 2003: Springer, pp. 986-996, doi: 10.1007/978-3-540-39964-3_62.


29. L. Huang, T. Song, and T. Jiang, "Linear regression combined KNN algorithm to identify latent defects for imbalance data of ICs," Microelectronics Journal, vol. 131, p. 105641, 2023, doi: 10.1016/j.mejo.2022.105641.


30. F. Jazizadeh, M. Afzalan, B. Becerik-Gerber, and L. Soibelman, "EMBED: A dataset for energy monitoring through building electricity disaggregation," in Proceedings of the Ninth International Conference on Future Energy Systems, 2018, pp. 230-235, doi: 10.1145/3208903.3208939.


31. M. Zeifman and K. Roth, "Nonintrusive appliance load monitoring: Review and outlook," IEEE transactions on Consumer Electronics, vol. 57, no. 1, pp. 76-84, 2011, doi: 10.1109/icce.2011.5722560.


32. S. Drenker and A. Kader, "Nonintrusive monitoring of electric loads," IEEE Computer Applications in Power, vol. 12, no. 4, pp. 47-51, 1999, doi: 10.1109/67.795138.


33. A. I. Cole and A. Albicki, "Data extraction for effective non-intrusive identification of residential power loads," in IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No. 98CH36222), 1998, vol. 2: IEEE, pp. 812-815, doi: 10.1109.imtc.1998.676838.


34. F. Sultanem, "Using appliance signatures for monitoring residential loads at meter panel level," IEEE Transactions on Power Delivery, vol. 6, no. 4, pp. 1380-1385, 1991, doi: 10.1109/61.97667.


35. L. K. Norford and S. B. Leeb, "Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms," Energy and Buildings, vol. 24, no. 1, pp. 51-64, 1996, doi: 10.1016/0378-7788(95)00958-2.


36. D. Srinivasan, W. Ng, and A. Liew, "Neural-network-based signature recognition for harmonic source identification," IEEE transactions on power delivery, vol. 21, no. 1, pp. 398-405, 2005, doi: 10.1109/tpwrd.2005.852370.


37. M. E. Berges, E. Goldman, H. S. Matthews, and L. Soibelman, "Enhancing electricity audits in residential buildings with nonintrusive load monitoring," Journal of industrial ecology, vol. 14, no. 5, pp. 844-858, 2010, doi: 10.1111/j.1530-9290.2010.00280.x.


38. M. Dong, P. C. Meira, W. Xu, and C. Chung, "Non-intrusive signature extraction for major residential loads," IEEE Transactions on Smart Grid, vol. 4, no. 3, pp. 1421-1430, 2013, doi: 10.1109/tsg.2013.2245926.


39. A. G. Ruzzelli, C. Nicolas, A. Schoofs, and G. M. 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: IEEE, pp. 1-9, doi: 10.1109/secon.2010.5508244.


40. M. B. Figueiredo, A. De Almeida, and B. Ribeiro, "An experimental study on electrical signature identification of non-intrusive load monitoring (nilm) systems," in Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II 10, 2011: Springer, pp. 31-40, doi: 10.1007/978-3-642-20267-4_4.


41. J. M. Gillis, S. M. Alshareef, and W. G. Morsi, "Nonintrusive load monitoring using wavelet design and machine learning," IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 320-328, 2015, doi: 10.1109/tsg.2015.2428706.


42. J. M. Gillis and W. G. Morsi, "Non-intrusive load monitoring using semi-supervised machine learning and wavelet design," IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2648-2655, 2016, doi: 10.1109/tsg.2016.2532885.


43. S. M. Tabatabaei, S. Dick, and W. Xu, "Toward non-intrusive load monitoring via multi-label classification," IEEE Transactions on Smart Grid, vol. 8, no. 1, pp. 26-40, 2016, doi: 10.1109/tsg.2016.2584581.


44. H.-H. Chang, "Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses," Energies, vol. 5, no. 11, pp. 4569-4589, 2012, doi: 10.3390/en5114569.