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A multi-task learning model for non-intrusive load monitoring based on discrete wavelet transform

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Abstract

Non-intrusive load monitoring (NILM) is an algorithm that can help to present the power consumption of each domestic appliance by analyzing the total household consumption data. With NILM, the house does not need an additional monitoring equipment and the costs can therefore be reduced. Considering the few input features (usually aggregate power only), we proposed to use sliding windows, extracted different frequency bands through discrete wavelet transform of the power in each sliding window, and characterized the features of the middle of the sliding window, so as to perform multi-dimensional features input. The multi-dimensional inputs are put into a neural network consisting of one-dimensional convolution and Bi-directional Long Short-Term Memory for prediction. Also, in consideration of the states of the device switches, we designed a classification network that serves as the auxiliary network in multi-task learning, and designed the optimized loss function that works for the binary classification problem. The final output of the neural network is the product of the losses of the two subnets to achieve information sharing. The proposed feature extraction method and deep neural network were applied to the real-world household energy dataset UK Domestic Appliance-Level Electricity. Furthermore, we selected theF1-score, mean absolute error, and signal aggregate error as the main indicators of algorithm performance evaluation. It is found that the performance is better than what the previous researchers did with subtask gated networks, with two standard error metrics (mean absolute error and signal aggregate error), respectively, improved by 25.15% and 17.83%. In addition, the averageF1-score increased by nearly 20% and the average mean absolute error and signal aggregate error decreased by 37.86% and 25.24%, respectively.

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The data supporting the findings of this work are available from the corresponding author on reasonable request.

References

  1. Neenan B, Robinson J, Boisvert R (2009) Residential electricity use feedback: A research synthesis and economic framework. Electric Power Research Institute

    Google Scholar 

  2. Darby S (2006) The effectiveness of feedback on energy consumption. a review for DEFRA of the literature on metering, billing and direct displays

  3. Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891.https://doi.org/10.1109/5.192069

    Article  Google Scholar 

  4. Zia T, Bruckner D, Zaidi A (2011) A hidden Markov model based procedure for identifying household electric loads. In: Proceedings of IECON 37th Annual Conference in IEEE Industrial Electronic Society, pp 3218–3223.https://doi.org/10.1109/IECON.2011.6119826

  5. Kolter JZ Johnson MJ (2011) REDD: a public data set for energy disaggregation research. In: Proceedings of the SustKDD Workshop on Data Mining Applications in Sustainability

  6. Hassan T, Javed F, Arshad N (2014) An empirical investigation of V-I trajectory based load signatures for non-intrusive load monitoring. IEEE Trans Smart Grid 5(2):870–878.https://doi.org/10.1109/TSG.2013.2271282

    Article  Google Scholar 

  7. Lin Y, Tsai M (2014) Development of an improved time-frequency analysis-based nonintrusive load monitor for load demand identification. IEEE Trans Instrum Meas 63(6):1470–1483.https://doi.org/10.1109/TIM.2013.2289700

    Article  Google Scholar 

  8. Senemmar S, Zhang J (2022) Non-intrusive load monitoring in MVDC Shipboard power systems using wavelet-convolutional neural networks. In: 2022 IEEE Texas Power and Energy Conference (TPEC), pp 1–6.https://doi.org/10.1109/TPEC54980.2022.9750745

  9. Hidiyanto F, Halim A (2020) KNN methods with varied K, distance and training data to disaggregate NILM with similar load characteristic. In: Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering 2020 (APCORISE 2020). Association for Computing Machinery, New York, pp 93–99.https://doi.org/10.1145/3400934.3400953

  10. Yuan Q, Wang H, Wu B, Song Y, Wang H (2019) A fusion load disaggregation method based on clustering algorithm and support vector regression optimization for low sampling data. Future Internet 11(2):51.https://doi.org/10.3390/fi11020051

    Article  Google Scholar 

  11. Zhang ZC, Zhong M, Wang Z, Goddard N, Sutton C (2018) Sequence-to-point learning with neural networks for nonintrusive load monitoring. In: Proceedings of 32nd AAAI Conference in Artificial Intelligence (AAAI)

  12. 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 (BuildSys '15). Association for Computing Machinery, New York, pp 55–64.https://doi.org/10.1145/2821650.2821672

  13. Mauch L, Yang B (2015) A new approach for supervised power disaggregation by using a deep recurrent LSTM network. In: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp 63-67.https://doi.org/10.1109/GlobalSIP.2015.7418157

  14. Aghera R et al (2021) A Deep learning technique using low sampling rate for residential non intrusive load monitoring

  15. de Diego-Otón L, Fuentes-Jimenez D, Hernández Á, Nieto R (2021) Recurrent LSTM architecture for appliance identification in non-intrusive load monitoring. In: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp 1–6.https://doi.org/10.1109/I2MTC50364.2021.9460046

  16. Siami-Namini S, Tavakoli N, Namin AS (2019) The performance of LSTM and BiLSTM in forecasting time series. In: 2019 IEEE International Conference on Big Data (Big Data), pp 3285–3292.https://doi.org/10.1109/BigData47090.2019.9005997

  17. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3431–3440.https://doi.org/10.1109/CVPR.2015.7298965

  18. Devlin MA, Hayes BP (2019) Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data. IEEE Trans Consum Electron 65(3):339–348.https://doi.org/10.1109/TCE.2019.2918922

    Article  Google Scholar 

  19. An S, Jang M, Yoon D (2021) Classification of single- and multi-carrier signals using CNN based deep learning. In: 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), pp 196–199.https://doi.org/10.1109/IC-NIDC54101.2021.9660515

  20. Ciancetta F, Bucci G, Fiorucci E, Mari S, Fioravanti A (2021) A new convolutional neural network-based system for NILM applications. IEEE Trans Instrum Meas 70:1–12.https://doi.org/10.1109/TIM.2020.3035193

    Article  Google Scholar 

  21. Caruana R (1997) Multitask learning. Mach Learn 28:41–75.https://doi.org/10.1023/A:1007379606734

    Article  Google Scholar 

  22. Shin C, Joo S, Yim J, Lee H, Moon T, Rhee W (2019) Subtask gated networks for non-intrusive load monitoring. Proc AAAI Conf Artif Intell 33(01):1150–1157.https://doi.org/10.1609/aaai.v33i01.33011150

    Article  Google Scholar 

  23. Faustine A, Pereira L, Bousbiat H, Kulkarni S (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 (NILM'20). Association for Computing Machinery, New York, pp 84–88.https://doi.org/10.1145/3427771.3427859

  24. Piccialli V, Sudoso AM (2021) Improving non-intrusive load disaggregation through an attention-based deep neural network. Energies 14(4):847.https://doi.org/10.3390/en14040847

    Article  Google Scholar 

  25. Barsim KS, Felix W, Yang B (2018) On the feasibility of generic deep disaggregation for single-load extraction.https://doi.org/10.13140/RG.2.2.29815.11685

  26. Cutajar M, Gatt E, Grech I, Casha O, Micallef J (2013) Discrete wavelet transforms with multiclass SVM for phoneme recognition. Eurocon 2013:1695–1700.https://doi.org/10.1109/EUROCON.2013.6625205

    Article  Google Scholar 

  27. Akansu AN, Haddad RA (2001) Chapter 6—Wavelet transform. In: Akansu AN, Haddad RA (eds) Multiresolution signal decomposition, 2nd edn. Academic Press, pp 391–442

    Chapter MATH  Google Scholar 

  28. Kehtarnavaz N (2008) Chapter 7—Frequency domain processing. In: Kehtarnavaz N (ed) Digital signal processing system design, 2nd edn. Academic Press, pp 175–196.https://doi.org/10.1016/B978-0-12-374490-6.00007-6

    Chapter  Google Scholar 

  29. Edwards TS (1991) Discrete wavelet transforms: theory and implementation

  30. Weeks M, Bayoumi MA (2003) Discrete wavelet transform: architectures, design and performance issues. J VLSI Signal Proces Syst Signal Image Video Technol 35:155–178

    Article MATH  Google Scholar 

  31. Wikipedia contributors. “Daubechies wavelet”. Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 14 Jun. 2022. Web. 11 Oct. 2022

  32. Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC (2015) Convolutional LSTM Network: a machine learning approach for precipitation nowcasting. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, vol 1 (NIPS'15). MIT Press, Cambridge, pp 802–810

  33. Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: IEEE International Conference on Computer Vision (ICCV), pp 2999–3007.https://doi.org/10.1109/ICCV.2017.324

  34. Kelly J, Knottenbelt W (2015) The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci Data 2:150007.https://doi.org/10.1038/sdata.2015.7

    Article  Google Scholar 

  35. Makonin S, Popowich F (2015) Nonintrusive load monitoring (NILM) performance evaluation. Energ Effi 8:809–814.https://doi.org/10.1007/s12053-014-9306-2

    Article  Google Scholar 

  36. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79–82.https://doi.org/10.3354/cr030079

    Article  Google Scholar 

  37. Goutte C, Gaussier E (2005) A probabilistic interpretation of precision, recall andF-score, with implication for evaluation. Lect Notes Comput Sci 3408:345–359.https://doi.org/10.1007/978-3-540-31865-1_25

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Jie Luo, Shubo Liu, Zhaohui Cai & Chang Xiong

  2. School of National Cybersecurity, Wuhan University, Wuhan, 430072, China

    Guoqing Tu

Authors
  1. Jie Luo

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  3. Zhaohui Cai

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  4. Chang Xiong

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Contributions

JL: Conceptualization, Methodology, Writing—original draft. SL: Methodology, Formal analysis, review & editing. ZC: Investigation, Conceptualization, review & editing. CX: Data curation, Formal analysis. GT: Validation, review & editing.

Corresponding author

Correspondence toShubo Liu.

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