{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T10:28:23Z","timestamp":1777285703519,"version":"3.51.4"},"reference-count":11,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,2,12]],"date-time":"2018-02-12T00:00:00Z","timestamp":1518393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002790","name":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["EGP-501582-16"],"award-info":[{"award-number":["EGP-501582-16"]}],"id":[{"id":"10.13039\/501100002790","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Datasets are important for researchers to build models and test how well their machine learning algorithms perform. This paper presents the Rainforest Automation Energy (RAE) dataset to help smart grid researchers test their algorithms that make use of smart meter data. This initial release of RAE contains 1 Hz data (mains and sub-meters) from two residential houses. In addition to power data, environmental and sensor data from the house\u2019s thermostat is included. Sub-meter data from one of the houses includes heat pump and rental suite captures, which is of interest to power utilities. We also show an energy breakdown of each house and show (by example) how RAE can be used to test non-intrusive load monitoring (NILM) algorithms.<\/jats:p>","DOI":"10.3390\/data3010008","type":"journal-article","created":{"date-parts":[[2018,2,12]],"date-time":"2018-02-12T10:50:38Z","timestamp":1518432638000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis"],"prefix":"10.3390","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7818-8301","authenticated-orcid":false,"given":"Stephen","family":"Makonin","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}]},{"given":"Z.","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}]},{"given":"Chris","family":"Tumpach","sequence":"additional","affiliation":[{"name":"Rainforest Automation, Inc., Burnaby, BC V5G 4P5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hadzic, F., Tan, H., and Dillon, T.S. (2011). Mining of Data with Complex Structures, Springer.","DOI":"10.1007\/978-3-642-17557-2"},{"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":"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":"2016","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_4","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, NS, Canada.","DOI":"10.1109\/EPEC.2013.6802949"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"160037","DOI":"10.1038\/sdata.2016.37","article-title":"Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014","volume":"3","author":"Makonin","year":"2016","journal-title":"Sci. Data"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"150007","DOI":"10.1038\/sdata.2015.7","article-title":"The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes","volume":"2","author":"Kelly","year":"2015","journal-title":"Sci. Data"},{"key":"ref_8","unstructured":"Anderson, K., Ocneanu, A.F., Benitez, D., Carlson, D., Rowe, A., and Berges, M. (2011, January 21). BLUED: A fully labeled public dataset for event-based non-intrusive load monitoring research. Proceedings of the 2nd Workshop on Data Mining Applications in Sustainability (SustKDD), San Diego, CA, USA."},{"key":"ref_9","unstructured":"Picon, T., Meziane, M.N., Ravier, P., Lamarque, G., Novello, C., Bunetel, J.C.L., and Raingeaud, Y. (arXiv, 2016). COOLL: Controlled On\/Off Loads Library, a Public Dataset of High-Sampled Electrical Signals for Appliance Identification, arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s12053-014-9306-2","article-title":"Nonintrusive load monitoring (NILM) performance evaluation","volume":"8","author":"Makonin","year":"2014","journal-title":"Energy Effic."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Parson, O., Ghosh, S., Weal, M., and Rogers, A. (2012, January 22\u201326). Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI\u201912), Toronto, ON, Canada.","DOI":"10.1609\/aaai.v26i1.8162"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/3\/1\/8\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:54:46Z","timestamp":1760194486000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/3\/1\/8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,12]]},"references-count":11,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,3]]}},"alternative-id":["data3010008"],"URL":"https:\/\/doi.org\/10.3390\/data3010008","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,12]]}}}