Abstract
Non-intrusive load monitoring (NILM) is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit. NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights. This enables informed decision-making, energy optimization, and cost reduction. However, NILM encounters substantial challenges like signal noise, data availability, and data privacy concerns, necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios. Deep learning techniques have recently shown some promising results in NILM research, but training these neural networks requires significant labeled data. Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’ appliances is laborious and expensive and exposes users to severe privacy risks. It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states (On/Off) from their respective energy consumption value. This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network (TCN) and long short-term memory (LSTM) for classifying appliance operation states from labeled and unlabeled data. The two thresholding techniques, namely Middle-Point Thresholding and Variance-Sensitive Thresholding, which are needed to derive the threshold values for determining appliance operation states, are also compared thoroughly. The superiority of the proposed model, along with finding the appliance states through the Middle-Point Thresholding method, is demonstrated through 15% improved overall improved F1micro score and almost 26% improved Hamming loss, F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
To ensure reproducibility of our results and further improvements by other researchers, the complete source code of this research work is available in https://github.com/Mohammad-Kaosain-Akbar/Semi-Supervised-NILM.
References
Abbas MZ, Ali Sajjad I, Hussain B, et al. (2022). An adaptive-neuro fuzzy inference system based-hybrid technique for performing load disaggregation for residential customers. Scientific Reports, 12: 2384.
Akbar MK, Amayri M, Bouguila N (2023). Deep learning based solution for appliance operational state detection and power estimation in non-intrusive load monitoring. In: Proceedings of International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems.
Alami M, Decock J, Kaddah R, et al. (2022). Conv-NILM-Net, a causal and multi-appliance model for energy source separation. In: Proceedings of European Conference on Machine Learning (ECML), MLBEM Workshop.
Barsim KS, Yang B (2015). Toward a semi-supervised non-intrusive load monitoring system for event-based energy disaggregation. In: Proceedings of 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, USA.
Çavdar İH, Faryad V (2019). New design of a supervised energy disaggregation model based on the deep neural network for a smart grid. Energies, 12: 1217.
Chen HY, Lai CL, Chen H, et al. (2013). LocalSense: An infrastructure-mediated sensing method for locating appliance usage events in homes. In: Proceedings of 2013 International Conference on Parallel and Distributed Systems, Seoul, R.O. Korea.
Correa-Delval M, Sun H, Matthews PC, et al. (2021). Appliance classification using BiLSTM neural networks and feature extraction. In: Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Espoo, Finland.
de Diego-Otón L, Fuentes-Jimenez D, Hernández Á, et al. (2021). Recurrent LSTM architecture for appliance identification in non-intrusive load monitoring. In: Proceedings of 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Glasgow, UK.
Desai S, Alhadad R, Mahmood A, et al. (2019). Multi-state energy classifier to evaluate the performance of the NILM algorithm. Sensors, 19: 5236.
EIA (2012). Electricity Explained: Use of Electricity. Available at http://www.eia.gov/energyexplained/index.cfm?page=electricity_use
Eurostat (2018). Energy statistics—An overview. Available at https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Energy_statistics_-_an_overview#Final_energy_consumption
Fatouh AM, Nasr OA, Eissa MM (2018). New semi-supervised and active learning combination technique for non-intrusive load monitoring. In: Proceedings of 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, USA.
Faustine A, Pereira L, Bousbiat H, et al. (2020). UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM. In: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring.
Fischer C (2008). Feedback on household electricity consumption: A tool for saving energy? Energy Efficiency, 1: 79–104.
Gillis JM, Morsi WG (2017). Non-intrusive load monitoring using semi-supervised machine learning and wavelet design. IEEE Transactions on Smart Grid, 8: 2648–2655.
Goodfellow I, Bengio Y, Courville A (2016). Deep Learning. Cambridge, MA, USA: MIT Press.
Harbecke D, Chen Y, Hennig L, et al. (2022). Why only micro-F1? Class weighting of measures for relation classification. arXiv: 2205.09460.
Hart GW (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80: 1870–1891.
Hernandez AS, Ballado AH, Heredia APD (2021). Development of a non-intrusive load monitoring (NILM) with unknown loads using support vector machine. In: Proceedings of 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS), Shah Alam, Malaysia.
Hochreiter S, Schmidhuber J (1997). Long short-term memory. Neural Computation, 9: 1735–1780.
Hock D, Kappes M, Ghita B (2018). Non-intrusive appliance load monitoring using genetic algorithms. IOP Conference Series: Materials Science and Engineering, 366: 012003.
Hong T, Langevin J, Sun K (2018). Building simulation: Ten challenges. Building Simulation, 11: 871–898.
Hur CH, Lee HE, Kim YJ, et al. (2022). Semi-supervised domain adaptation for multi-label classification on nonintrusive load monitoring. Sensors, 22: 5838.
Ioffe S, Szegedy C (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning.
Iwayemi A, Zhou C (2017). SARAA: Semi-supervised learning for automated residential appliance annotation. IEEE Transactions on Smart Grid, 8: 779–786.
Jia Z, Yang L, Zhang Z, et al. (2021). Sequence to point learning based on bidirectional dilated residual network for non-intrusive load monitoring. International Journal of Electrical Power & Energy Systems, 129: 106837.
Kaselimi M, Doulamis N, Doulamis A, et al. (2019). Bayesian-optimized bidirectional LSTM regression model for non-intrusive load monitoring. In: Proceedings of 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.
Kelly J, Knottenbelt W (2015). 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, Seoul, R.O. Korea.
Kelly JP, James MA (2016). Radiographic outcomes of hemiepiphyseal stapling for distal radius deformity due to multiple hereditary exostoses. Journal of Pediatric Orthopaedics, 36: 42–47.
Khazaei M, Stankovic L, Stankovic V (2020). Evaluation of low-complexity supervised and unsupervised NILM methods and pre-processing for detection of multistate white goods. In: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring.
Kim H, Marwah M, Arlitt M, et al. (2011). Unsupervised disaggregation of low frequency power measurements. In: Proceedings of 2011 SIAM International Conference on Data Mining, Philadelphia, PA, USA.
Kim JG, Lee B (2019). Appliance classification by power signal analysis based on multi-feature combination multi-layer LSTM. Energies, 12: 2804.
Kingma DP, Ba J (2014). Adam: A method for stochastic optimization. arXiv: 1412.6980.
Ko MS, Lee K, Kim JK, et al. (2020). Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting. IEEE Transactions on Sustainable Energy, 12: 1321–1335.
Kolter JZ, Johnson MJ (2011). REDD: A public data set for energy disaggregation research. In: Proceedings of Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, USA.
Kong W, Dong Z, Hill DJ, et al. (2016). Improving nonintrusive load monitoring efficiency via a hybrid programing method. IEEE Transactions on Industrial Informatics, 12: 2148–2157.
Li D, Sawyer K, Dick S (2015). Disaggregating household loads via semi-supervised multi-label classification. In: Proceedings of 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), Redmond, WA, USA.
Li D, Dick S (2017). A graph-based semi-supervised learning approach towards household energy disaggregation. In: Proceedings of 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy.
Lin J, Ma J, Zhu J, et al. (2022). Deep domain adaptation for non-intrusive load monitoring based on a knowledge transfer learning network. IEEE Transactions on Smart Grid, 13: 280–292.
Lipton ZC, Elkan C, Narayanaswamy B (2014). Thresholding classifiers to maximize F1 score. arXiv: 1402.1892.
Liu G, Guo J (2019). Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 337: 325–338.
Liu Q, Kamoto KM, Liu X, et al. (2019). Low-complexity non-intrusive load monitoring using unsupervised learning and generalized appliance models. IEEE Transactions on Consumer Electronics, 65: 28–37.
Liu S, Lee K, Lee I (2020). Document-level multi-topic sentiment classification of Email data with BiLSTM and data augmentation. Knowledge-Based Systems, 197: 105918.
Liu Y, Qiu J, Lu J, et al. (2022). A single-to-multi network for latency-free non-intrusive load monitoring. IEEE Transactions on Network Science and Engineering, 9: 755–768.
MacQueen I (1967). Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics Problems.
Mollel RS, Stankovic L, Stankovic V (2022). Using explainability tools to inform NILM algorithm performance: A decision tree approach. In: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Boston, MA, USA.
Murray D, Liao J, Stankovic L, et al. (2015). A data management platform for personalised real-time energy feedback. In: Proceedings of the 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting.
Najafi B, Moaveninejad S, Rinaldi F (2018). Data analytics for energy disaggregation: Methods and applications. In: Arghandeh R, Zhou Y (eds), Big Data Application in Power Systems. Amsterdam: Elsevier.
National Bureau of Statistics of China (2018). Consumption of energy by sector. Available at http://www.stats.gov.cn/tjsj/ndsj/2018/indexch.htm
Papageorgiou PG, Gkaidatzis PA, Christoforidis GC, et al. (2021). Unsupervised NILM implementation using odd harmonic currents. In: Proceedings of the 56th International Universities Power Engineering Conference (UPEC), Middlesbrough, UK.
Parson O (2014). Unsupervised training methods for non-intrusive appliance load monitoring from smart meter data. PhD Thesis, University of Southampton, UK.
Precioso D, Gómez-Ullate D (2022). Thresholding methods in non-intrusive load monitoring to estimate appliance status. https://doi.org/10.21203/rs.3.rs-1923023/v1.
Qian Y, Yang Q, Li D, et al. (2021). An improved temporal convolutional network for non-intrusive load monitoring. In: Proceedings of the 33rd Chinese Control and Decision Conference (CCDC), Kunming, China.
Revuelta Herrero J, Lozano Murciego Á, López Barriuso A, et al. (2017). Non intrusive load monitoring (NILM): A state of the art. In: Proceedings of International Conference on Practical Applications of Agents and Multi-Agent Systems. Schuster M, Paliwal KK (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45: 2673–2681.
Singh S, Majumdar A (2018). Deep sparse coding for non–intrusive load monitoring. IEEE Transactions on Smart Grid, 9: 4669–4678.
Srivastava S, Lessmann S (2018). A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Solar Energy, 162: 232–247.
Statistics Canada (2009). Energy Statistics Handbook. Ottawa: Statistics Canada.
Tarvainen A, Valpola H (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA.
Vine D, Buys L, Morris P (2013). The effectiveness of energy feedback for conservation and peak demand: A literature review. Open Journal of Energy Efficiency, 2: 7–15.
Wang K, Qi X, Liu H (2019). Photovoltaic power forecasting based LSTM-Convolutional Network. Energy, 189: 116225.
Yang CC, Soh CS, Yap VV (2018). A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency. Energy Efficiency, 11: 239–259.
Yang Y, Zhong J, Li W, et al. (2020). Semisupervised multilabel deep learning based nonintrusive load monitoring in smart grids. IEEE Transactions on Industrial Informatics, 16: 6892–6902.
Yang W, Pang C, Huang J, et al. (2021). Sequence-to-point learning based on temporal convolutional networks for nonintrusive load monitoring. IEEE Transactions on Instrumentation and Measurement, 70: 2512910.
Zeifman M, Roth K (2011). Nonintrusive appliance load monitoring: Review and outlook. IEEE Transactions on Consumer Electronics, 57: 76–84.
Zhang C, Zhong M, Wang Z, et al. (2018). Sequence-to-point learning with neural networks for non-intrusive load monitoring. In: Proceedings of the AAAI Conference on Artificial Intelligence.
Zhang M, Li J, Li Y, et al. (2021). Deep learning for short-term voltage stability assessment of power systems. IEEE Access, 9: 29711–29718.
Zhang Z, Li Y, Duan J, et al. (2023). A multistate load state identification model based on time convolutional networks and conditional random fields. IEEE Transactions on Artificial Intelligence, 4: 1328–1336.
Zhou X, Li S, Liu C, et al. (2021). Non-intrusive load monitoring using a CNN-LSTM-RF model considering label correlation and class-imbalance. IEEE Access, 9: 84306–84315.
Zoha A, Gluhak A, Imran MA, et al. (2012). Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors, 12: 16838–16866
Acknowledgements
The completion of this research was made possible thanks to The Natural Sciences and Engineering Research Council of Canada (NSERC) and a start-up grant from Concordia University.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Mohammad Kaosain Akbar, Manar Amayri and Nizar Bouguila. The first draft of the manuscript was written by Mohammad Kaosain Akbar and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
About this article
Cite this article
Akbar, M.K., Amayri, M. & Bouguila, N. A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid. Build. Simul. 17, 441–457 (2024). https://doi.org/10.1007/s12273-023-1074-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12273-023-1074-5