{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:06:08Z","timestamp":1758845168281,"version":"3.41.0"},"reference-count":14,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2012,7,1]],"date-time":"2012-07-01T00:00:00Z","timestamp":1341100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGBED Rev."],"published-print":{"date-parts":[[2012,7]]},"abstract":"<jats:p>Smart meters can measure the electricity consumption of a household at a fine temporal granularity. By adequately processing this aggregated data an estimation of the consumption of individual appliances can be retrieved and used to provide novel services, such as personalized recommendations on how to reduce the overall energy consumption of the household. In this paper, we build upon existing work in consumption data disaggregation and consider smart meter data along with additional information made available by networked sensors and household appliances. In particular, we investigate the use of ON\/OFF events, which signal when appliances have been turned on or off, to improve the accuracy of a state-of-the art disaggregation algorithm that uses such events along with smart meter data to estimate the consumption of single appliances. Our results, obtained by applying the algorithm to a publicly available dataset, show that the accuracy of the algorithm quickly deteriorates as the number of available ON\/OFF events decreases. We thus suggest possible countermeasures to cope with this limitation and to provide accurate electricity consumption breakdowns in private households.<\/jats:p>","DOI":"10.1145\/2367580.2367586","type":"journal-article","created":{"date-parts":[[2012,9,11]],"date-time":"2012-09-11T22:21:06Z","timestamp":1347402066000},"page":"32-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Improving device-level electricity consumption breakdowns in private households using ON\/OFF events"],"prefix":"10.1145","volume":"9","author":[{"given":"Christian","family":"Beckel","sequence":"first","affiliation":[{"name":"Institute for Pervasive Computing, Zurich, Switzerland"}]},{"given":"Wilhelm","family":"Kleiminger","sequence":"additional","affiliation":[{"name":"Institute for Pervasive Computing, Zurich, Switzerland"}]},{"given":"Thorsten","family":"Staake","sequence":"additional","affiliation":[{"name":"Information Management, Zurich, Switzerland"}]},{"given":"Silvia","family":"Santini","sequence":"additional","affiliation":[{"name":"Wireless Sensor Networks Lab, Darmstadt, Germany"}]}],"member":"320","published-online":{"date-parts":[[2012,7]]},"reference":[{"issue":"12","key":"e_1_2_1_1_1","first-page":"4454","volume":"36","author":"Burgess J.","year":"2008","journal-title":"Re-Materialising Energy Use Through Transparent Monitoring Systems. 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Z.","key":"e_1_2_1_8_1"},{"doi-asserted-by":"crossref","unstructured":"F.\n       \n      Mattern\n     and \n      \n      \n      C.\n       \n      Floerkemeier\n      \n  \n  . \n  From the Internet of Computers to the Internet of Things. In Kai Sachs Ilia Petrov Pablo Guerrero (Eds.): From Active Data Management to Event-Based Systems and More volume \n  6462\n   of \n  LNCS pages \n  242\n  --\n  259\n  . \n  Springer 2010\n  .   F. Mattern and C. Floerkemeier. From the Internet of Computers to the Internet of Things. In Kai Sachs Ilia Petrov Pablo Guerrero (Eds.): From Active Data Management to Event-Based Systems and More volume 6462 of LNCS pages 242--259. Springer 2010.","key":"e_1_2_1_9_1","DOI":"10.1007\/978-3-642-17226-7_15"},{"doi-asserted-by":"publisher","key":"e_1_2_1_10_1","DOI":"10.1145\/1791314.1791316"},{"volume-title":"Using Hidden Markov Models for Iterative Non-intrusive Appliance Monitoring. 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