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Data Compressive Dictionary Matching Based Optimization Approach for Non-Intrusive Load Monitoring


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DOI: https://doi.org/10.15866/iree.v17i2.21668

Abstract


Non-Intrusive Load Monitoring (NILM) supports energy management by characterizing the energy usage patterns of individual appliances from aggregate demand measurements. Machine learning algorithms for NILM require bulk training data while optimization techniques involve high computation time. Addressing these issues, this paper presents an intelligent dictionary matching-based optimization approach for NILM of finite-state residential appliances. The operating states of the target appliances are determined by K-means clustering using limited training data. Employing these power states, a consumer-specific dictionary containing all the possible combinations of aggregate demand is constructed in sorted form. To reduce the computational burden, the number of aggregate demand samples is reduced by event-driven data compression. These compressed samples are then matched in the sorted dictionary to estimate the share of individual appliances in aggregate demand. Furthermore, a post-processing algorithm is proposed to eliminate the effect of sudden spikes/dips during state transitions of appliances. The proposed algorithm is evaluated using the low-frequency Reference Energy Disaggregation Dataset and its results are compared with recent approaches. The comparison results show that the proposed approach gives better results as compared to recent techniques with low computational effort. The proposed approach can support energy managers in managing demand side resources of residential microgrids.
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Keywords


Load Monitoring; Energy Disaggregation; Smart Meter Data; Clustering; Data Compression; NILM

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References


P. Capros, N. Tasios, A. De Vita, L. Mantzos, and L. Paroussos, Transformations of the energy system in the context of the decarbonisation of the EU economy in the time horizon to 2050, Energy Strategy Reviews, vol. 1, no. 2, pp. 85-96, 2012/09/01/ 2012.
https://doi.org/10.1016/j.esr.2012.06.001

Y. Chen, P. Xu, J. Gu, F. Schmidt, and W. Li, Measures to improve energy demand flexibility in buildings for demand response (DR): A review, (in English), Energy and Buildings, Review vol. 177, pp. 125-139, Oct 2018.
https://doi.org/10.1016/j.enbuild.2018.08.003

J. Kelly and W. Knottenbelt, Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature, arXiv preprint arXiv:1605.00962, 2016.

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, Applied Sciences, vol. 8, no. 12, p. 2337, 2018.
https://doi.org/10.3390/app8122337

S. Welikala, C. Dinesh, M. P. B. Ekanayake, R. I. Godaliyadda, and J. Ekanayake, Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting, (in English), IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 448-461, Jan 2019.
https://doi.org/10.1109/TSG.2017.2743760

A. F. Ebrahim and O. A. Mohammed, Energy Disaggregation Based Deep Learning Techniques: A pre-processing Stage to Enhance The Household Load Forecasting, in 2018 IEEE Industry Applications Society Annual Meeting (IAS), 2018, pp. 1-8.
https://doi.org/10.1109/IAS.2018.8544664

F. Luo, G. Ranzi, W. Kong, Z. Y. Dong, S. Wang, and J. Zhao, Non-intrusive energy saving appliance recommender system for smart grid residential users, IET Generation, Transmission & Distribution, vol. 11, no. 7, pp. 1786-1793, 2017.
https://doi.org/10.1049/iet-gtd.2016.1615

Z. Guo, Z. J. Wang, and A. Kashani, Home Appliance Load Modeling From Aggregated Smart Meter Data, IEEE Transactions on Power Systems, vol. 30, no. 1, pp. 254-262, 2015.
https://doi.org/10.1109/TPWRS.2014.2327041

H. Rashid, P. Singh, V. Stankovic, and L. Stankovic, Can non-intrusive load monitoring be used for identifying an appliance's anomalous behaviour?, Applied Energy, vol. 238, pp. 796-805, 2019/03/15/ 2019.
https://doi.org/10.1016/j.apenergy.2019.01.061

W. Chen, Q. Gong, G. Geng, and Q. Jiang, Cloud-Based Non-Intrusive Leakage Current Detection for Residential Appliances, IEEE Transactions on Power Delivery, vol. 35, no. 4, pp. 1977-1986, 2020.
https://doi.org/10.1109/TPWRD.2019.2958521

M. A. Devlin and B. P. Hayes, Non-Intrusive Load Monitoring and Classification of Activities of Daily Living Using Residential Smart Meter Data, IEEE Transactions on Consumer Electronics, vol. 65, no. 3, pp. 339-348, 2019.
https://doi.org/10.1109/TCE.2019.2918922

S. Zeinal-Kheiri, A. M. Shotorbani, and B. Mohammadi-Ivatloo, Residential Load Disaggregation Considering State Transitions, IEEE Transactions on Industrial Informatics, vol. 16, no. 2, pp. 743-753, 2020.
https://doi.org/10.1109/TII.2019.2925323

G. W. Hart, Nonintrusive appliance load monitoring, Proceedings of the IEEE, vol. 80, no. 12, pp. 1870-1891, 1992.
https://doi.org/10.1109/5.192069

L. Pereira and N. Nunes, Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools-A review, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 6, p. e1265, 2018.
https://doi.org/10.1002/widm.1265

E. Azizi, A. M. Shotorbani, M.-T. Hamidi-Beheshti, B. Mohammadi-Ivatloo, and S. Bolouki, Residential Household Non-Intrusive Load Monitoring via Smart Event-based Optimization, IEEE Transactions on Consumer Electronics, vol. 66, no. 3, pp. 233-241, 2020.
https://doi.org/10.1109/TCE.2020.3008261

H. Kim, M. Marwah, M. Arlitt, G. Lyon, and J. Han, Unsupervised disaggregation of low frequency power measurements, in Proceedings of the 2011 SIAM international conference on data mining, 2011, pp. 747-758: SIAM.
https://doi.org/10.1137/1.9781611972818.64

S. Makonin, F. Popowich, I. V. Bajić, B. Gill, and L. Bartram, Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring, IEEE Transactions on smart grid, vol. 7, no. 6, pp. 2575-2585, 2015.
https://doi.org/10.1109/TSG.2015.2494592

W. Kong, Z. Y. Dong, D. J. Hill, J. Ma, J. Zhao, and F. Luo, A hierarchical hidden Markov model framework for home appliance modeling, IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 3079-3090, 2016.
https://doi.org/10.1109/TSG.2016.2626389

O. Parson, S. Ghosh, M. Weal, and A. Rogers, An unsupervised training method for non-intrusive appliance load monitoring, Artificial Intelligence, vol. 217, pp. 1-19, 2014.
https://doi.org/10.1016/j.artint.2014.07.010

J. Z. Kolter and T. Jaakkola, Approximate inference in additive factorial hmms with application to energy disaggregation, in Artificial intelligence and statistics, 2012, pp. 1472-1482: PMLR.

D. Egarter, V. P. Bhuvana, and W. Elmenreich, PALDi: Online load disaggregation via particle filtering, IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 2, pp. 467-477, 2014.
https://doi.org/10.1109/TIM.2014.2344373

W. Kong, Z. Y. Dong, D. J. Hill, F. Luo, and Y. Xu, Improving nonintrusive load monitoring efficiency via a hybrid programing method, IEEE Transactions on Industrial Informatics, vol. 12, no. 6, pp. 2148-2157, 2016.
https://doi.org/10.1109/TII.2016.2590359

Y. Liu, X. Wang, and W. You, Non-Intrusive Load Monitoring by Voltage-Current Trajectory Enabled Transfer Learning, IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 5609-5619, 2019.
https://doi.org/10.1109/TSG.2018.2888581

D. Li and S. Dick, Residential Household Non-Intrusive Load Monitoring via Graph-Based Multi-Label Semi-Supervised Learning, IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 4615-4627, 2019.
https://doi.org/10.1109/TSG.2018.2865702

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, 2017.
https://doi.org/10.1109/TSG.2016.2584581

M. Kaselimi, N. Doulamis, A. Doulamis, A. Voulodimos, and E. Protopapadakis, Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring, in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 2747-2751.
https://doi.org/10.1109/ICASSP.2019.8683110

Q. Liu, K. M. Kamoto, X. Liu, M. Sun, and N. Linge, Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models, IEEE Transactions on Consumer Electronics, vol. 65, no. 1, pp. 28-37, 2019.
https://doi.org/10.1109/TCE.2019.2891160

Q. Yuan, H. Wang, B. Wu, Y. Song, and H. Wang, A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data, (in English), Future Internet, vol. 11, no. 2, Feb 2019.
https://doi.org/10.3390/fi11020051

A. Faustine, L. Pereira, and C. Klemenjak, Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring, IEEE Transactions on Smart Grid, pp. 1-1, 2020.
https://doi.org/10.1109/TSG.2020.3010621

D. Yang, X. Gao, L. Kong, Y. Pang, and B. Zhou, An Event-Driven Convolutional Neural Architecture for Non-Intrusive Load Monitoring of Residential Appliance, IEEE Transactions on Consumer Electronics, vol. 66, no. 2, pp. 173-182, 2020.
https://doi.org/10.1109/TCE.2020.2977964

E. Gomes and L. Pereira, PB-NILM: Pinball guided deep non-intrusive load monitoring, IEEE Access, vol. 8, pp. 48386-48398, 2020.
https://doi.org/10.1109/ACCESS.2020.2978513

M. Xia, W. a. Liu, K. Wang, X. Zhang, and Y. Xu, Non-intrusive load disaggregation based on deep dilated residual network, (in English), Electric Power Systems Research, vol. 170, pp. 277-285, May 2019.
https://doi.org/10.1016/j.epsr.2019.01.034

M. Kaselimi, N. Doulamis, A. Voulodimos, E. Protopapadakis, and A. Doulamis, Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models, IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3054-3067, 2020.
https://doi.org/10.1109/TSG.2020.2974347

K. Chen, Q. Wang, Z. He, K. Chen, J. Hu, and J. He, Convolutional sequence to sequence non‐intrusive load monitoring, The Journal of Engineering, vol. 2018, no. 17, pp. 1860-1864, 2018.
https://doi.org/10.1049/joe.2018.8352

J. Barber, H. Cuayáhuitl, M. Zhong, and W. Luan, Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning, in Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, 2020, pp. 11-15.
https://doi.org/10.1145/3427771.3427845

C. Zhang, M. Zhong, Z. Wang, N. Goddard, and C. Sutton, Sequence-to-point learning with neural networks for nonintrusive load monitoring, arXiv preprint arXiv:1612.09106, 2016.

S. Lin, L. Zhao, F. Li, Q. Liu, D. Li, and Y. Fu, A nonintrusive load identification method for residential applications based on quadratic programming, Electric Power Systems Research, vol. 133, pp. 241-248, 2016.
https://doi.org/10.1016/j.epsr.2015.12.014

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, 2015.
https://doi.org/10.1109/TCST.2015.2476777

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, 2016.
https://doi.org/10.1109/TCSII.2016.2603479

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.
https://doi.org/10.1109/TCE.2018.2843292

D. Egarter and W. Elmenreich, Load disaggregation with metaheuristic optimization, in Energieinformatik, 2015, pp. 1-12.

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, 2020/02/01/ 2020.
https://doi.org/10.1016/j.scs.2019.101918

M. Shahroz, M. S. Younis, and H. A. Nasir, A Scenario-Based Stochastic Optimization Approach for Non-Intrusive Appliance Load Monitoring, IEEE Access, vol. 8, pp. 142205-142217, 2020.
https://doi.org/10.1109/ACCESS.2020.3013682

B. Zhao, K. He, L. Stankovic, and V. Stankovic, Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing, IEEE Access, vol. 6, pp. 53944-53959, 2018.
https://doi.org/10.1109/ACCESS.2018.2871343

J. Z. Kolter and M. J. Johnson, REDD: A public data set for energy disaggregation research, in Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, 2011, vol. 25, no. Citeseer, pp. 59-62.

P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to data mining. Pearson Education India, 2016.

M. Simonov, G. Chicco, and G. Zanetto, Event-Driven Energy Metering: Principles and Applications, IEEE Transactions on Industry Applications, vol. 53, no. 4, pp. 3217-3227, 2017.
https://doi.org/10.1109/TIA.2017.2679680


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