{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:16:59Z","timestamp":1768342619043,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2066207"],"award-info":[{"award-number":["U2066207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52107120"],"award-info":[{"award-number":["52107120"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2066207"],"award-info":[{"award-number":["U2066207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52107120"],"award-info":[{"award-number":["52107120"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors. NILM is defined as disaggregating loads only from aggregate power measurements through analytical tools. Although low-rate NILM tasks have been conducted by unsupervised approaches based on graph signal processing (GSP) concepts, enhancing feature selection can still contribute to performance improvement. Therefore, a novel unsupervised GSP-based NILM approach with power sequence feature (STS-UGSP) is proposed in this paper. First, state transition sequences (STS) are extracted from power readings and featured in clustering and matching, instead of power changes and steady-state power sequences featured in other GSP-based NILM works. When generating graph in clustering, dynamic time warping distances between STSs are calculated for similarity quantification. After clustering, a forward-backward power STS matching algorithm is proposed for searching each STS pair of an operational cycle, utilizing both power and time information. Finally, load disaggregation results are obtained based on STS clustering and matching results. STS-UGSP is validated on three publicly accessible datasets from various regions, generally outperforming four benchmarks in two evaluation metrics. Besides, STS-UGSP estimates closer energy consumption of appliances to the ground truth than benchmarks.<\/jats:p>","DOI":"10.3390\/s23083939","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T02:09:21Z","timestamp":1681351761000},"page":"3939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9546-3101","authenticated-orcid":false,"given":"Bochao","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuhao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenpeng","family":"Luan","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8855-5160","authenticated-orcid":false,"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Corbett, J., Wardle, K., and Chen, C. (2018). Toward a Sustainable Modern Electricity Grid: The Effects of Smart Metering and Program Investments on Demand-Side Management Performance in the US Electricity Sector 2009-2012. IEEE Trans. Eng. Manag., 252\u2013263.","DOI":"10.1109\/TEM.2017.2785315"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1109\/5.192069","article-title":"Nonintrusive appliance load monitoring","volume":"80","author":"Hart","year":"1992","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101601","DOI":"10.1016\/j.jobe.2020.101601","article-title":"Trainingless multi-objective evolutionary computing-based nonintrusive load monitoring: Part of smart-home energy management for demand-side management","volume":"33","author":"Lin","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/TCE.2011.5735484","article-title":"Nonintrusive appliance load monitoring: Review and outlook","volume":"57","author":"Zeifman","year":"2011","journal-title":"IEEE Trans. Consum. Electr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.1109\/TSG.2015.2494592","article-title":"Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring","volume":"7","author":"Makonin","year":"2015","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103202","DOI":"10.1016\/j.scs.2021.103202","article-title":"Cooling load disaggregation using a NILM method based on random forest for smart buildings","volume":"74","author":"Xiao","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liao, J., Elafoudi, G., Stankovic, L., and Stankovic, V. (2014, January 3\u20136). Non-intrusive appliance load monitoring using low-resolution smart meter data. Proceedings of the 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), Venice, Italy.","DOI":"10.1109\/SmartGridComm.2014.7007702"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kelly, J., and Knottenbelt, W. (2015, January 4\u20135). Neural nilm: Deep neural networks applied to energy disaggregation. Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, Seoul, Republic of Korea.","DOI":"10.1145\/2821650.2821672"},{"key":"ref_9","first-page":"1","article-title":"Non-intrusive Load Monitoring based on Self-supervised Learning","volume":"72","author":"Chen","year":"2023","journal-title":"IEEE Trans. Instrument. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhong, M., Wang, Z., Goddard, N., and Sutton, C. (2018, January 2\u20137). Sequence-to-point learning with neural networks for non-intrusive load monitoring. Proceedings of the Thirty-second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11873"},{"key":"ref_11","first-page":"1419","article-title":"Transfer learning for non-intrusive load monitoring","volume":"11","author":"Squartini","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"884","DOI":"10.3934\/energy.2016.1.1","article-title":"Low-complexity energy disaggregation using appliance load modelling","volume":"4","author":"Altrabalsi","year":"2016","journal-title":"Aims Energy"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lin, Y.H., Tsai, M.S., and Chen, C.S. (2011, January 27\u201330). Applications of fuzzy classification with fuzzy c-means clustering and optimization strategies for load identification in NILM systems. Proceedings of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), Taipei, Taiwan.","DOI":"10.1109\/FUZZY.2011.6007393"},{"key":"ref_14","first-page":"792","article-title":"Load Disaggregation Based on Aided Linear Integer Programming","volume":"64","author":"Bhotto","year":"2017","journal-title":"IEEE Trans. Circ. Syst. II Express Briefs"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1109\/TII.2020.2975810","article-title":"An Appliance Load Disaggregation Scheme Using Automatic State Detection Enabled Enhanced Integer Programming","volume":"17","author":"Dash","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3301","DOI":"10.1109\/TSG.2022.3152147","article-title":"Mixed-Integer Nonlinear Programming for State-Based Non-Intrusive Load Monitoring","volume":"13","author":"Balletti","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1109\/ACCESS.2016.2557460","article-title":"On a training-less solution for non-intrusive appliance load monitoring using graph signal processing","volume":"4","author":"Zhao","year":"2016","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Stankovic, V., and Stankovic, L. (2020). Building a Graph signal processing model using dynamic time warping for load disaggregation. Sensors, 20.","DOI":"10.3390\/s20226628"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jia, N., Wang, J., and Li, N. (2012, January 3\u20135). Application of data mining in intelligent power consumption. Proceedings of the International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), Xiamen, China.","DOI":"10.1049\/cp.2012.1035"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1016\/j.apenergy.2017.08.203","article-title":"Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models","volume":"208","author":"Bonfigli","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.apenergy.2016.10.040","article-title":"A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring","volume":"185","author":"Cominola","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108086","DOI":"10.1016\/j.epsr.2022.108086","article-title":"Industrial load disaggregation based on hidden Markov models","volume":"210","author":"Luan","year":"2022","journal-title":"Electr. Power Syst. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kumar, P., and Abhyankar, A.R. (2023). A Time Efficient Factorial Hidden Markov Model Based Approach for Non-Intrusive Load Monitoring. IEEE Trans. Smart Grid.","DOI":"10.1109\/TSG.2023.3245019"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111523","DOI":"10.1016\/j.enbuild.2021.111523","article-title":"Real-time non-intrusive load monitoring: A light-weight and scalable approach","volume":"253","author":"Athanasiadis","year":"2021","journal-title":"Energy Build."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ahmed, S., and Bons, M. (2020, January 18). Edge Computed NILM: A Phone-Based Implementation Using MobileNet Compressed by Tensorflow Lite. Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring (NILM \u201820), New York, NY, USA.","DOI":"10.1145\/3427771.3427852"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Su, S., Yan, Y., Lu, H., Kangping, L., Yujing, S., Fei, W., Liming, L., and Hui, R. (October, January 28). Non-intrusive load monitoring of air conditioning using low-resolution smart meter data. Proceedings of the 2016 IEEE International Conference on Power System Technology (POWERCON), Wollongong, NSW, Australia.","DOI":"10.1109\/POWERCON.2016.7753952"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102764","DOI":"10.1016\/j.scs.2021.102764","article-title":"Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier","volume":"67","author":"Himeur","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.comcom.2020.08.024","article-title":"A secure edge monitoring approach to unsupervised energy disaggregation using mean shift algorithm in residential buildings","volume":"162","author":"Liu","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Stankovic, V., Liao, J., and Stankovic, L. (2014, January 9\u201312). A graph-based signal processing approach for low-rate energy disaggregation. Proceedings of the 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES), Orlando, FL, USA.","DOI":"10.1109\/CIES.2014.7011835"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1109\/TSG.2016.2598872","article-title":"Non-intrusive load disaggregation using graph signal processing","volume":"9","author":"He","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"53944","DOI":"10.1109\/ACCESS.2018.2871343","article-title":"Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing","volume":"6","author":"Zhao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Batra, N., Kukunuri, R., Pandey, A., Malakar, R., Kumar, R., Krystalakos, O., Zhong, M., Meira, P., and Parson, O. (2019, January 13\u201314). Towards Reproducible State-of-the-Art Energy Disaggregation. Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, New York, NY, USA. BuildSys \u201919.","DOI":"10.1145\/3360322.3360844"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Makonin, S., Popowich, F., Bartram, L., Gill, B., and Baji\u0107, I.V. (2013, January 21\u201323). AMPds: A public dataset for load disaggregation and eco-feedback research. Proceedings of the 2013 IEEE Electrical Power & Energy Conference, Halifax, Nova Scotia, Canada.","DOI":"10.1109\/EPEC.2013.6802949"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"160122","DOI":"10.1038\/sdata.2016.122","article-title":"An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study","volume":"4","author":"Murray","year":"2017","journal-title":"Sci. Data"},{"key":"ref_35","unstructured":"Kolter, J.Z., and Johnson, M.J. (2011, January 21). REDD: A public data set for energy disaggregation research. Proceedings of the Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/TASSP.1975.1162641","article-title":"Minimum prediction residual principle applied to speech recognition","volume":"23","author":"Itakura","year":"1975","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.apenergy.2017.03.010","article-title":"Dynamic time warping based non-intrusive load transient identification","volume":"195","author":"Liu","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Qi, B., Liu, L., and Wu, X. (2018). Low-rate nonintrusive load disaggregation for resident load based on graph signal processing. IEEJ Trans. Electr. Electron. Eng., 13.","DOI":"10.1002\/tee.22746"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","article-title":"The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains","volume":"30","author":"Shuman","year":"2013","journal-title":"IEEE Signal Process. Magaz."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, X., Zhao, B., Luan, W., and Liu, B. (2022, January 9\u201310). An Unsupervised Load Disaggregation Approach Based on Graph Signal Processing Featuring Power Sequences. Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, New York, NY, USA. BuildSys \u201922.","DOI":"10.1145\/3563357.3566156"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"114949","DOI":"10.1016\/j.apenergy.2020.114949","article-title":"Non-intrusive load disaggregation solutions for very low-rate smart meter data","volume":"268","author":"Zhao","year":"2020","journal-title":"Appl. Energy"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/3939\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:15:05Z","timestamp":1760123705000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/3939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,12]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23083939"],"URL":"https:\/\/doi.org\/10.3390\/s23083939","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,12]]}}}