Abstract
Energy disaggregation, also known as Non-Intrusive Load Monitoring (NILM), aims to analyze the aggregate energy consumption of a user, such as a household, to infer the energy usage of individual appliances. Deep learning methods have become popular for this task, typically relying on supervised learning with labeled data. To reduce the need for extensive data collection, researchers have investigated various data augmentation techniques. However, it remains unclear to what extent labeled training data can be extended to facilitate energy disaggregation. To address this question, this study explores how training data diversity can be augmented from both intra-user and inter-user perspectives. Specifically, we synthesize new training samples using appliance activation data extracted from the same user (intra-user) as well as from other users (inter-user). Based on the public dataset REFIT, we show that by using the proposed intra-user and inter-user data augmentation methods, the disaggregation errors of the energy disaggregation models based on WaveNet and BERT are decreased by 6.6% and 26.8% respectively, in terms of MAE and SAE.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alcalá, J., Parson, O., Rogers, A.: Detecting anomalies in activities of daily living of elderly residents via energy disaggregation and Cox processes. In: Proceedings of the 2nd ACM International conference on Embedded Systems for Energy-Efficient Built Environments, pp. 225–234 (2015)
Bejarano, G., DeFazio, D., Ramesh, A.: Deep latent generative models for energy disaggregation. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 850–857 (2019)
Chakravarty, P., Gupta, A.: Impact of energy disaggregation on consumer behavior. In: Proceedings of UC Berkeley: Behavior, Energy and Climate Change Conference (2013)
Chen, K., Wang, Q., He, Z., Chen, K., Hu, J., He, J.: Convolutional sequence to sequence non-intrusive load monitoring. J. Eng. 2018(17), 1860–1864 (2018)
Delfosse, A., Hebrail, G., Zerroug, A.: Deep learning applied to NILM: is data augmentation worth for energy disaggregation? In: Proceedings of the 9th International Conference on Prestigious Applications of Intelligent Systems, pp. 2972–2977 (2020)
Faruqui, A., Sergici, S., Sharif, A.: The impact of informational feedback on energy consumption-a survey of the experimental evidence. Energy 35(4), 1598–1608 (2010)
Ghahramani, Z., Jordan, M.: Factorial hidden Markov models. Mach. Learn. 29, 245–273 (1997)
Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)
Jiang, J., Kong, Q., Plumbley, M.D., Gilbert, N., Hoogendoorn, M., Roijers, D.M.: Deep learning-based energy disaggregation and on/off detection of household appliances. ACM Trans. Knowl. Discov. Data 15(3), 1–21 (2021)
Kaselimi, M., Doulamis, N., Voulodimos, A., Protopapadakis, E., Doulamis, A.: Context aware energy disaggregation using adaptive bidirectional LSTM models. IEEE Trans. Smart Grid 11(4), 3054–3067 (2020)
Kelly, J., Knottenbelt, W.: 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, pp. 55–64 (2015)
Kelly, J., Knottenbelt, W.: The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2(1), 1–14 (2015)
Kim, H., Marwah, M., Arlitt, M., Lyon, G., Han, J.: Unsupervised disaggregation of low frequency power measurements. In: Proceedings of the SIAM International Conference on Data Mining, pp. 747–758 (2011)
Kolter, J.Z., Johnson, M.J.: Redd: a public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability at SIGKDD, pp. 59–62 (2011)
Murray, D., Stankovic, L., Stankovic, V.: An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study. Sci. Data 4(1), 1–12 (2017)
van den Oord, A., et al.: WaveNet: a generative model for raw audio. arXiv:1609.03499 (2016)
Oublal, K., Ladjal, S., Benhaiem, D., Roueff, F., et al.: Temporal attention bottleneck is informative? interpretability through disentangled generative representations for energy time series disaggregation. In: Workshop on Challenges in Deployable Generative AI at ICML (2023)
Quy, T.L., Zerr, S., Ntoutsi, E., Nejdl, W.: Data augmentation for dealing with low sampling rates in NILM. arXiv:2104.02055 (2021)
Rafiq, H., Shi, X., Zhang, H., Li, H., Ochani, M.K., Shah, A.A.: Generalizability improvement of deep learning-based non-intrusive load monitoring system using data augmentation. IEEE Trans. Smart Grid 12(4), 3265–3277 (2021)
Song, H., Kalogridis, G., Fan, Z.: Short paper: time-dependent power load disaggregation with applications to daily activity monitoring. In: Proceedings of IEEE World Forum on Internet of Things, pp. 183–184 (2014)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000–6010 (2017)
Yue, Z., Witzig, C.R., Jorde, D., Jacobsen, H.A.: BERT4NILM: A bidirectional transformer model for non-intrusive load monitoring. In: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, pp. 89–93 (2020)
Acknowledgements
This research is supported in part by the National Natural Science Foundation of China (Grant No. 62306336), and in part by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515110685) and in part by Science Foundation of China University of Petroleum, Beijing (No. 2462023BJRC014).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this paper.
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, S., Jiang, J., Kong, Q. (2025). Intra and Inter-User Data Augmentation Methods for Energy Disaggregation. In: Sheng, Q.Z., et al. Advanced Data Mining and Applications. ADMA 2024. Lecture Notes in Computer Science(), vol 15387. Springer, Singapore. https://doi.org/10.1007/978-981-96-0811-9_6
Download citation
DOI: https://doi.org/10.1007/978-981-96-0811-9_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-96-0810-2
Online ISBN: 978-981-96-0811-9
eBook Packages: Computer ScienceComputer Science (R0)