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ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series (2401.05381v1)

Published 17 Dec 2023 in eess.SP and cs.LG

Abstract: Over the past decade, millions of smart meters have been installed by electricity suppliers worldwide, allowing them to collect a large amount of electricity consumption data, albeit sampled at a low frequency (one point every 30min). One of the important challenges these suppliers face is how to utilize these data to detect the presence/absence of different appliances in the customers' households. This valuable information can help them provide personalized offers and recommendations to help customers towards the energy transition. Appliance detection can be cast as a time series classification problem. However, the large amount of data combined with the long and variable length of the consumption series pose challenges when training a classifier. In this paper, we propose ADF, a framework that uses subsequences of a client consumption series to detect the presence/absence of appliances. We also introduce TransApp, a Transformer-based time series classifier that is first pretrained in a self-supervised way to enhance its performance on appliance detection tasks. We test our approach on two real datasets, including a publicly available one. The experimental results with two large real datasets show that the proposed approach outperforms current solutions, including state-of-the-art time series classifiers applied to appliance detection. This paper appeared in VLDB 2024.

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References (60)
  1. 2012. CER Smart Metering Project - Electricity Customer Behaviour Trial, 2009-2010. https://www.ucd.ie/issda/data/commissionforenergyregulationcer/
  2. Adrian Albert and Ram Rajagopal. 2013. Smart Meter Driven Segmentation: What Your Consumption Says About You. IEEE Transactions on Power Systems 28, 4 (2013), 4019–4030. https://doi.org/10.1109/TPWRS.2013.2266122
  3. Hunt Allcott. 2011. Social norms and energy conservation. Journal of Public Economics 95, 9 (2011), 1082–1095. https://doi.org/10.1016/j.jpubeco.2011.03.003 Special Issue: The Role of Firms in Tax Systems.
  4. Muzaffer Aslan and Ebra Nur Zurel. 2022. An efficient hybrid model for appliances classification based on time series features. Energy and Buildings 266 (2022), 112087. https://doi.org/10.1016/j.enbuild.2022.112087
  5. The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version. https://doi.org/10.48550/ARXIV.1602.01711
  6. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. https://doi.org/10.48550/ARXIV.1803.01271
  7. Smart metering and functionalities of smart meters in smart grid - a review. In 2015 IEEE Electrical Power and Energy Conference (EPEC). 138–145. https://doi.org/10.1109/EPEC.2015.7379940
  8. DCAM: Dimension-Wise Class Activation Map for Explaining Multivariate Data Series Classification. In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD ’22). Association for Computing Machinery, New York, NY, USA, 1175–1189. https://doi.org/10.1145/3514221.3526183
  9. Language Models are Few-Shot Learners. https://doi.org/10.48550/ARXIV.2005.14165
  10. Smart grids deployments within EU projects: The role of smart meters. In 2016 Smart Cities Symposium Prague (SCSP). 1–5. https://doi.org/10.1109/SCSP.2016.7501033
  11. T. Cover and P. Hart. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 1 (1967), 21–27. https://doi.org/10.1109/TIT.1967.1053964
  12. CoAtNet: Marrying Convolution and Attention for All Data Sizes. https://doi.org/10.48550/ARXIV.2106.04803
  13. The UCR Time Series Archive. https://doi.org/10.48550/ARXIV.1810.07758
  14. ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. CoRR abs/1910.13051 (2019). arXiv:1910.13051 http://arxiv.org/abs/1910.13051
  15. Residential Appliance Detection Using Attention-based Deep Convolutional Neural Network. CSEE Journal of Power and Energy Systems 8, 2 (2022), 621–633. https://doi.org/10.17775/CSEEJPES.2020.03450
  16. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://doi.org/10.48550/ARXIV.1810.04805
  17. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. https://doi.org/10.48550/ARXIV.2010.11929
  18. SAITS: Self-attention-based imputation for time series. Expert Systems with Applications 219 (jun 2023), 119619. https://doi.org/10.1016/j.eswa.2023.119619
  19. InceptionTime: Finding AlexNet for time series classification. Data Mining and Knowledge Discovery 34, 6 (sep 2020), 1936–1962. https://doi.org/10.1007/s10618-020-00710-y
  20. Masked Autoencoders Are Scalable Vision Learners. arXiv:2111.06377 [cs.CV]
  21. Deep Residual Learning for Image Recognition. https://doi.org/10.48550/ARXIV.1512.03385
  22. Dan Hendrycks and Kevin Gimpel. 2016. Gaussian Error Linear Units (GELUs). https://doi.org/10.48550/ARXIV.1606.08415
  23. Classification of time series by shapelet transformation. Data Min Knowl Disc 28 (2014), 851–881. https://doi.org/10.1007/s10618-013-0322-1
  24. Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https://doi.org/10.48550/ARXIV.1502.03167
  25. Deep Learning for Time Series Classification: A Review. Data Min. Knowl. Discov. 33, 4 (jul 2019), 917–963. https://doi.org/10.1007/s10618-019-00619-1
  26. ISSDA. [n.d.]. Irish Social Science Data Archive. https://www.ucd.ie/issda/
  27. Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning. https://doi.org/10.48550/ARXIV.2209.03759
  28. A Comprehensive Feature Study for Appliance Recognition on High Frequency Energy Data. In Proceedings of the Eighth International Conference on Future Energy Systems (Shatin, Hong Kong) (e-Energy ’17). Association for Computing Machinery, New York, NY, USA, 121–131. https://doi.org/10.1145/3077839.3077845
  29. Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring. Sensors 22 (08 2022), 5872. https://doi.org/10.3390/s22155872
  30. Electricity Demand Activation Extraction: From Known to Unknown Signatures, Using Similarity Search. In Proceedings of the Twelfth ACM International Conference on Future Energy Systems (Virtual Event, Italy) (e-Energy ’21). Association for Computing Machinery, New York, NY, USA, 148–159. https://doi.org/10.1145/3447555.3464865
  31. Improving Vision Transformers to Learn Small-Size Dataset From Scratch. IEEE Access PP (01 2022), 1–1. https://doi.org/10.1109/ACCESS.2022.3224044
  32. Network In Network. https://doi.org/10.48550/ARXIV.1312.4400
  33. Network In Network. arXiv:1312.4400 [cs.NE]
  34. Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming. Energy Reports 7 (2021), 283–291. https://doi.org/10.1016/j.egyr.2021.08.045 2021 The 4th International Conference on Electrical Engineering and Green Energy.
  35. HIVE-COTE 2.0: a new meta ensemble for time series classification. CoRR abs/2104.07551 (2021). arXiv:2104.07551 https://arxiv.org/abs/2104.07551
  36. Megan Milam and G. Kumar Venayagamoorthy. 2014. Smart meter deployment: US initiatives. In ISGT 2014. 1–5. https://doi.org/10.1109/ISGT.2014.6816507
  37. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In International Conference on Learning Representations.
  38. Keiron O’Shea and Ryan Nash. 2015. An Introduction to Convolutional Neural Networks. CoRR abs/1511.08458 (2015). arXiv:1511.08458 http://arxiv.org/abs/1511.08458
  39. ANN-based appliance recognition from low-frequency energy monitoring data. In 2013 IEEE 14th International Symposium on ”A World of Wireless, Mobile and Multimedia Networks” (WoWMoM). 1–6. https://doi.org/10.1109/WoWMoM.2013.6583496
  40. PyTorch: An Imperative Style, High-Performance Deep Learning Library. https://doi.org/10.48550/ARXIV.1912.01703
  41. Adrien Petralia. [n.d.]. Source code of TransApp experiments. https://github.com/adrienpetralia/TransApp
  42. Appliance Detection Using Very Low-Frequency Smart Meter Time Series. In ACM International Conference on Future Energy Systems (e-Energy).
  43. A residual convolutional neural network with multi-block for appliance recognition in non-intrusive load identification. Energy and Buildings 281 (2023), 112749. https://doi.org/10.1016/j.enbuild.2022.112749
  44. Near Real-Time Appliance Recognition Using Low Frequency Monitoring and Active Learning Methods. Energy Procedia 122 (2017), 691–696. https://doi.org/10.1016/j.egypro.2017.07.371 CISBAT 2017 International ConferenceFuture Buildings & Districts – Energy Efficiency from Nano to Urban Scale.
  45. Robert E Schapire. 2013. Explaining adaboost. In Empirical inference. Springer, 37–52.
  46. K Simonyan and A Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations (ICLR 2015), 1–14.
  47. ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring. Sensors 22, 8 (2022). https://doi.org/10.3390/s22082926
  48. Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series. Proc. VLDB Endow. 16, 11 (aug 2023), 3418–3432. https://doi.org/10.14778/3611479.3611536
  49. Going Deeper with Convolutions. https://doi.org/10.48550/ARXIV.1409.4842
  50. Toward Non-Intrusive Load Monitoring via Multi-Label Classification. IEEE Transactions on Smart Grid 8, 1 (2017), 26–40. https://doi.org/10.1109/TSG.2016.2584581
  51. Attention Is All You Need. CoRR abs/1706.03762 (2017). arXiv:1706.03762 http://arxiv.org/abs/1706.03762
  52. IEDeaL: A Deep Learning Framework for Detecting Highly Imbalanced Interictal Epileptiform Discharges. Proc. VLDB Endow. 16, 3 (nov 2022), 480–490. https://doi.org/10.14778/3570690.3570698
  53. Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline. https://doi.org/10.48550/ARXIV.1611.06455
  54. Transformers in Time Series: A Survey. https://doi.org/10.48550/ARXIV.2202.07125
  55. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. https://doi.org/10.48550/ARXIV.2106.13008
  56. BERT4NILM: A Bidirectional Transformer Model for Non-Intrusive Load Monitoring. In Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring (Virtual Event, Japan) (NILM’20). Association for Computing Machinery, New York, NY, USA, 89–93. https://doi.org/10.1145/3427771.3429390
  57. A Transformer-based Framework for Multivariate Time Series Representation Learning. https://doi.org/10.48550/ARXIV.2010.02803
  58. Non-intrusive load disaggregation solutions for very low-rate smart meter data. Applied Energy 268 (2020), 114949. https://doi.org/10.1016/j.apenergy.2020.114949
  59. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. https://doi.org/10.48550/ARXIV.2012.07436
  60. FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. https://doi.org/10.48550/ARXIV.2201.12740
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