Deep Learning Based Active Power Disaggregation of Appliances for Personalized Energy Feedback System

Imgyu Kim; Hyuncheol Kim; Sangyong Shin

doi:10.7836/kses.2022.42.1.103

All Issue

2022 Vol.42, Issue 1 Preview Page Next Page

Research Article

Deep Learning Based Active Power Disaggregation of Appliances for Personalized Energy Feedback System 개인 맞춤형 에너지 피드백 시스템을 위한 딥러닝 기반의 다수 가전기기 유효전력 분해

28 February 2022. pp. 103-114

PDF XML

Abstract

Energy consumption feedback with an appliance-level feedback system can reduce consumption by a maximum of 12%. In this study, we proposed a data acquisition and training framework for configuring deep learning based on Non-Intrusive Load Monitoring (NILM) for a personalized and appliance-level energy consumption feedback system. To construct a training dataset, an aggregation of active power data from four types of home appliances (refrigerator, induction, TV, washing machine) was performed for approximately three weeks. LSTNet was applied to extract and recognize the features of active power data and the state of each home appliance. With an accuracy metric of more than 90% of the disaggregation result, the applicability of the appliance-level active power feedback system was verified.

Keywords

Non-Intrusive Load Monitoring

Energy disaggregation

LSTNet

Appliances level feedback system

Pattern Analysis

Neural Network

키워드

비침입 부하 모니터링

에너지 분해

LSTNet

가전기기 단위 피드백 시스템

패턴 분석

인공신경망

References

Elma, O. and Selamoğullar, U. S., A Survey of a Residential Load Profile for Demand Side Management Systems, 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE), pp. 85-89, 2017, doi: 10.1109/sege.2017.8052781. 10.1109/SEGE.2017.8052781

Mostafavi, S. and Cox, R. W., An Unsupervised Approach in Learning Load Patterns for Non-intrusive Load Monitoring, 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), pp. 631-636, 2017, doi: 10.1109/icnsc.2017.8000164. 10.1109/ICNSC.2017.8000164

Pérez-Lombard, L., Ortiz, J., and Pout, C., A Review on Buildings Energy Consumption Information, Energy and Buildings, Vol. 40, No. 3, pp. 394-398, 2008, doi: 10.1016/j.enbuild.2007.03.007. 10.1016/j.enbuild.2007.03.007

Armel, K. C., Gupta, A., Shrimali, G., and Albert, A. Is Disaggregation the Holy Grail of Energy Efficiency?, The Case of Electricity, Energy Policy, Vol. 52, pp. 213-234, 2013. 10.1016/j.enpol.2012.08.062

Davis, A. L., Krishnamurti, T., Fischhoff, B., and Bruine de Bruin, W., Setting a Standard for Electricity Pilot Studies, Energy Policy, Vol. 62, pp. 401-409, 2013, doi: 10.1016/j.enpol.2013.07.093.

Hazas, M., Friday, A., and Scott, J., Look Back before Leaping Forward: Four Decades of Domestic Energy Inquiry, IEEE Pervasive Computing, Vol. 10, No. 1, pp. 13-19, 2011, doi: 10.1109/MPRV.2010.89. 10.1109/MPRV.2010.89

Hart, G. W., Nonintrusive Appliance Load Monitoring, Proceedings of the IEEE, Vol. 80, No. 12, pp. 1870-1891, 1992, doi: 10.1109/5.192069. 10.1109/5.192069

Schirmer, P. A., Mporas, I., and Sheikh-Akbari, A., Energy Disaggregation using Two-stage Fusion of Binary Device Detectors, Energies, Vol. 13, No. 9, p. 2148, 2020. 10.3390/en13092148

Gopinath, R., Kumar, M., Joshua, C. P. C., and Srinivas, K., Energy Management using Non-intrusive Load Monitoring Techniques-State-of-the-art and Future Research Directions, Sustainable Cities and Society, Vol. 62, 102411, 2020. 10.1016/j.scs.2020.102411

Hassan, T., Javed, F., and Arshad, N., An Empirical Investigation of VI Trajectory based Load Signatures for Non-intrusive Load Monitoring, IEEE Transactions on Smart Grid, Vol. 5, No. 2, pp. 870-878, 2013, doi: 10.1109/pesgm.2014.6938824. 10.1109/PESGM.2014.6938824PMC3921076

Bonfigli, R. and Squartini, S., HMM Based Approach, Machine Learning Approaches to Non-intrusive Load Monitoring, Springer, pp. 31-90, 2020. 10.1007/978-3-030-30782-0_4

Murray, D., Stankovic, L., Stankovic, V., Lulic, S., and Sladojevic, S., Transferability of Neural Network Approaches For Low-rate Energy Disaggregation, ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8330-8334. doi: 10.1109/icassp.2019.8682486. 10.1109/ICASSP.2019.8682486

Said Barsim, K. and Yang, B., On the Feasibility of Generic Deep Disaggregation for Single-load Extraction, arXiv e-prints, pp. arXiv: 1802.02139, 2018.

Wu, X., Han, X., and Liang, K. X., Event-based Non-intrusive Load Identification Algorithm for Residential Loads Combined with Underdetermined Decomposition and Characteristic Filtering, IET Generation, Transmission & Distribution, Vol. 13, No. 1, pp. 99-107, 2019, doi: 10.1049/iet-nbt.2019.0335. 10.1049/iet-nbt.2019.033532338632PMC8676569

Çavdar, İ. H. and Faryad, V., New Design of a Supervised Energy Disaggregation Model based on the Deep Neural Network for a Smart Grid, Energies, Vol. 12, No. 7, p. 1217, 2019, doi: 10.3390/en12071217. 10.3390/en12071217

He, W. and Chai, Y. An Empirical Study on Energy Disaggregation via Deep Learning, Advances in Intelligent Systems Research, Vol. 133, pp. 338-342, 2016, doi: 10.2991/aiie-16.2016.77. 10.2991/aiie-16.2016.77

Mauch, L. and Yang, B. A New Approach for Supervised Power Disaggregation by using a Deep Recurrent LSTM Network, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 63-67. doi: 10.1109/globalsip.2015.7418157. 10.1109/GlobalSIP.2015.7418157

Kim, I. K., Kim H. C., Kim S. Y., and Shin S. Y., Spectogram Analysis of Active Power of Appliances and LSTM-based Energy Disaggregation, Journal of the Korea Convergence Society, Vol. 12, No. 2, pp. 21-28, 2021.

Garcia, F. C. C., Creayla, C. M. C., and Macabebe, E. Q. B., Development of an Intelligent System for Smart Home Energy Disaggregation using Stacked Denoising Autoencoders, Procedia Computer Science, Vol. 105, pp. 248-255, 2017, doi: 10.1016/j.procs.2017.01.218. 10.1016/j.procs.2017.01.218

Lai, G., Chang, W.-C., Yang, Y., and Liu, H., Modeling Long-and Short-Term Temporal Patterns with Deep Neural Networks, The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 95-104, 2018. 10.1145/3209978.3210006

Salem, H., Sayed-Mouchaweh, M., and Tagina, M., A Review on Non-intrusive Load Monitoring Approaches based on Machine Learning, Artificial Intelligence Techniques for a Scalable Energy Transition, Springer, pp. 109-131, 2020. 10.1007/978-3-030-42726-9_532603845

Butterworth, S., On the Theory of Filter Amplifiers, Wireless Engineer, Vol. 7, No. 6, pp. 536-541, 1930.

Chung, J., Gulcehre, C., Cho, K., and Bengio, Y., Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling, arXiv preprint arXiv:1412.3555, 2014.

Hochreiter, S. and Schmidhuber, J., Long Short-term Memory, Neural Computation, Vol. 9, No. 8, pp. 1735-1780, 1997. 10.1162/neco.1997.9.8.17359377276

Kolter, J. Z. and Johnson, M. J., REDD: A Public Data Set for Energy Disaggregation Research, Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, Vol. 25, pp. 59-62, 2011.

Information

Publisher :Korean Solar Energy Society
Publisher(Ko) :한국태양에너지학회
Journal Title :Journal of the Korean Solar Energy Society
Journal Title(Ko) :한국태양에너지학회 논문집
Volume : 42
No :1
Pages :103-114
Received Date : 2021-12-05
Revised Date : 2021-12-22
Accepted Date : 2021-12-26
DOI :https://doi.org/10.7836/kses.2022.42.1.103

Journal of the Korean Solar Energy Society ISSN:1598-6411(Print) 2508-3562(Online) 한국태양에너지학회 논문집

All Issue