{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T10:08:29Z","timestamp":1785838109777,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3737251","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T21:04:26Z","timestamp":1754255066000},"page":"4761-4772","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2987-9111","authenticated-orcid":false,"given":"Adrien","family":"Petralia","sequence":"first","affiliation":[{"name":"EDF R&amp;D, Palaiseau, France and LIPADE, Universit\u00e9 Paris Cit\u00e9, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3039-2485","authenticated-orcid":false,"given":"Philippe","family":"Charpentier","sequence":"additional","affiliation":[{"name":"EDF R&amp;D, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1995-994X","authenticated-orcid":false,"given":"Youssef","family":"Kadhi","sequence":"additional","affiliation":[{"name":"EDF R&amp;D, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8031-0265","authenticated-orcid":false,"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[{"name":"LIPADE, Universite Paris Cite, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2023.3237862"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2012.08.062"},{"key":"e_1_3_2_2_3_1","first-page":"1","article-title":"Should We Really Use Post-Hoc Tests Based on Mean-Ranks","volume":"17","author":"Benavoli Alessio","year":"2016","unstructured":"Alessio Benavoli, Giorgio Corani, and Francesca Mangili. 2016. Should We Really Use Post-Hoc Tests Based on Mean-Ranks? Journal of Machine Learning Research, Vol. 17, 5 (2016), 1-10. http:\/\/jmlr.org\/papers\/v17\/benavoli16a.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_4_1","unstructured":"Laurent Bozzi and Philippe Charpentier. 2018. \u00c9valuation d'Impact sur la Consommation \u00c9lectrique de la Solution Digitale e.quilibre d'EDF. In Journ\u00e9es de Statistique (JdS). Soci\u00e9t\u00e9 Fran\u00e7aise de Statistique (SFdS) France."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2022.3175941"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/HDIS56859.2022.9991439"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/SCSP.2016.7501033"},{"key":"e_1_3_2_2_9_1","volume-title":"Webb","author":"Dempster Angus","year":"2019","unstructured":"Angus Dempster, Fran\u00e7ois Petitjean, and Geoffrey I. Webb. 2019. ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. CoRR, Vol. abs\/1910.13051 (2019). arXiv:1910.13051 http:\/\/arxiv.org\/abs\/1910.13051"},{"key":"e_1_3_2_2_10_1","unstructured":"DMLC. 2024. eXtreme Gradient Boosting. https:\/\/github.com\/dmlc\/xgboost"},{"key":"e_1_3_2_2_11_1","unstructured":"EDF. 2024. Private communication with DATANUMIA Team Manager."},{"key":"e_1_3_2_2_12_1","unstructured":"EDF. 2025a. The digital revolution driving energy efficiency. https:\/\/www.edf.fr\/en\/the-edf-group\/taking-action-as-a-responsible-company\/corporate-social-responsibility\/the-digital-revolution-driving-energy-efficiency"},{"key":"e_1_3_2_2_13_1","unstructured":"EDF. 2025b. Solution suivi conso EDF. https:\/\/particulier.edf.fr\/fr\/accueil\/bilan-consommation\/solution-suivi-conso.html"},{"key":"e_1_3_2_2_14_1","volume-title":"French Patent FR1451531","author":"Laurent Bozzi Gregory Yard EDF","year":"2014","unstructured":"Gregory Yard EDF, Laurent Bozzi. French Patent FR1451531, 2014. ESTIMATION DE LA CONSOMMATION ELECTRIQUE D'UN EQUIPEMENT DONNE PARMI UN ENSEMBLE D'EQUIPEMENTS ELECTRIQUES. https:\/\/data.inpi.fr\/brevets\/FR1451531"},{"key":"e_1_3_2_2_15_1","volume-title":"French Patent FR3017975","author":"Laurent Bozzi Melanie Cazes EDF","year":"2016","unstructured":"Melanie Cazes EDF, Laurent Bozzi. French Patent FR3017975, 2016. ESTIMATION FINE DE CONSOMMATION ELECTRIQUE POUR DES BESOINS DE CHAUFFAGE\/CLIMATISATION D'UN LOCAL D'HABITATION. https:\/\/data.inpi.fr\/brevets\/FR3017975"},{"key":"e_1_3_2_2_16_1","unstructured":"EDF \u00e0 la R\u00e9union. 2024. Sarz la Kaz. https:\/\/reunion.edf.fr\/edf-a-la-reunion\/actualites-a-la-reunion\/sarz-la-kaz. Accessed: 2025-02-07."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/en13164154"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3427771.3427859"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00710-y"},{"key":"e_1_3_2_2_20_1","article-title":"Switch transformers: scaling to trillion parameter models with simple and efficient sparsity","volume":"23","author":"Fedus William","year":"2022","unstructured":"William Fedus, Barret Zoph, and Noam Shazeer. 2022. Switch transformers: scaling to trillion parameter models with simple and efficient sparsity. J. Mach. Learn. Res., Vol. 23, 1, Article 120 (Jan. 2022), 39 pages.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.17028\/rd.lboro.2070091.v1"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-023-00948-2"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3184011"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.192069"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. doi:10.48550\/ARXIV.1512.03385","DOI":"10.48550\/ARXIV.1512.03385"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. Gaussian Error Linear Units (GELUs). doi:10.48550\/ARXIV.1606.08415","DOI":"10.48550\/ARXIV.1606.08415"},{"key":"e_1_3_2_2_27_1","unstructured":"Romain Ilbert Ambroise Odonnat Vasilii Feofanov Aladin Virmaux Giuseppe Paolo Themis Palpanas and Ievgen Redko. 2024. SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention. arXiv:2402.10198 [cs.LG]"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1502.03167"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22155872"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2821650.2821672"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2015.7"},{"key":"e_1_3_2_2_32_1","unstructured":"Hyungsul Kim Manish Marwah Martin F. Arlitt Geoff Lyon and Jiawei Han. 2011. Unsupervised Disaggregation of Low Frequency Power Measurements. In SDM. https:\/\/api.semanticscholar.org\/CorpusID:18447017"},{"key":"e_1_3_2_2_33_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=cGDAkQo1C0p","author":"Kim Taesung","year":"2021","unstructured":"Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo. 2021. Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=cGDAkQo1C0p"},{"key":"e_1_3_2_2_34_1","volume-title":"REDD : A Public Data Set for Energy Disaggregation Research.","author":"Kolter J. Zico","year":"2011","unstructured":"J. Zico Kolter. 2011. REDD : A Public Data Set for Energy Disaggregation Research."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447555.3464865"},{"key":"e_1_3_2_2_36_1","volume-title":"Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting. In Neural Information Processing Systems. https:\/\/api.semanticscholar.org\/CorpusID:252968420","author":"Liu Yong","year":"2022","unstructured":"Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long. 2022. Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting. In Neural Information Processing Systems. https:\/\/api.semanticscholar.org\/CorpusID:252968420"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.3390\/en17236131"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.3390\/app10041454"},{"key":"e_1_3_2_2_39_1","volume-title":"Johnson","author":"Mayhorn Ebony","year":"2016","unstructured":"Ebony Mayhorn, Greg Sullivan, Joseph M. Petersen, Ryan Butner, and Erica M. Johnson. 2016. Load Disaggregation Technologies: Real World and Laboratory Performance. https:\/\/api.semanticscholar.org\/CorpusID:115779193"},{"key":"e_1_3_2_2_40_1","volume-title":"Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Sch\u00e4fer, and Anthony Bagnall.","author":"Middlehurst Matthew","year":"2024","unstructured":"Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Sch\u00e4fer, and Anthony Bagnall. 2024. aeon: a Python toolkit for learning from time series. arXiv:2406.14231 [cs.LG] https:\/\/arxiv.org\/abs\/2406.14231"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISGT.2014.6816507"},{"key":"e_1_3_2_2_42_1","volume-title":"International Conference on Learning Representations.","author":"Nie Yuqi","year":"2023","unstructured":"Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2023. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2010.5596746"},{"key":"e_1_3_2_2_44_1","volume-title":"An Introduction to Convolutional Neural Networks. CoRR","author":"O'Shea Keiron","year":"2015","unstructured":"Keiron O'Shea and Ryan Nash. 2015. An Introduction to Convolutional Neural Networks. CoRR, Vol. abs\/1511.08458 (2015). arXiv:1511.08458 http:\/\/arxiv.org\/abs\/1511.08458"},{"key":"e_1_3_2_2_45_1","volume-title":"PyTorch: an imperative style, high-performance deep learning library","author":"Paszke Adam","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas K\u00f6pf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: an imperative style, high-performance deep learning library. Curran Associates Inc., Red Hook, NY, USA."},{"key":"e_1_3_2_2_46_1","unstructured":"Adrien Petralia. 2025. Source code of NILMFormer experiments. https:\/\/github.com\/adrienpetralia\/NILMFormer"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE65448.2025.00350"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE65448.2025.00329"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575813.3595198"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.14778\/3632093.3632115"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05149-8"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.113890"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23073540"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22082926"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-021-00745-9"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.segan.2023.101246"},{"key":"e_1_3_2_2_58_1","volume-title":"CoRR","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. CoRR, Vol. abs\/1706.03762 (2017). arXiv:1706.03762 http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3163347"},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04414-3"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3427771.3429390"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"crossref","unstructured":"Chaoyun Zhang Mingjun Zhong Zongzuo Wang Nigel Goddard and Charles Sutton. 2018. Sequence-to-point learning with neural networks for non-intrusive load monitoring. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence(New Orleans Louisiana USA) (AAAI'18\/IAAI'18\/EAAI'18). AAAI Press Article 318 8 pages.","DOI":"10.1609\/aaai.v32i1.11873"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.114949"},{"key":"e_1_3_2_2_65_1","volume-title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In AAAI Conference on Artificial Intelligence. https:\/\/api.semanticscholar.org\/CorpusID:229156802","author":"Zhou Haoyi","year":"2020","unstructured":"Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wan Zhang. 2020. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In AAAI Conference on Artificial Intelligence. https:\/\/api.semanticscholar.org\/CorpusID:229156802"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12273-024-1175-910.1007\/s12273-024-1175-9"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3737251","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:14:49Z","timestamp":1777572889000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3737251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":66,"alternative-id":["10.1145\/3711896.3737251","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3737251","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}