{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T13:24:09Z","timestamp":1767965049626,"version":"3.49.0"},"reference-count":15,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T00:00:00Z","timestamp":1554854400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-15-IDEX-02"],"award-info":[{"award-number":["ANR-15-IDEX-02"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2019,4,10]]},"abstract":"<jats:p>A general approach is proposed to determine occupant behavior (occupancy and activity) in offices and residential buildings in order to use these estimates for improved energy management. Occupant behavior is modelled with a Bayesian network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO<jats:sub>2<\/jats:sub> concentration. Different case studies have been investigated with diversity according to their context (available sensors, occupancy or activity feedback, complexity of the environment, etc.). Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort.<\/jats:p>","DOI":"10.1155\/2019\/7129872","type":"journal-article","created":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T20:27:51Z","timestamp":1554928071000},"page":"1-12","source":"Crossref","is-referenced-by-count":19,"title":["Estimating Occupancy from Measurements and Knowledge Using the Bayesian Network for Energy Management"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5610-8833","authenticated-orcid":true,"given":"Manar","family":"Amayri","sequence":"first","affiliation":[{"name":"G-SCOP Laboratory\/Grenoble Institute of Technology, 1, 46 Avenue Felix Viallet, 38031 Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephane","family":"Ploix","sequence":"additional","affiliation":[{"name":"G-SCOP Laboratory\/Grenoble Institute of Technology, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hussain","family":"Kazmi","sequence":"additional","affiliation":[{"name":"KU Leuven, Department of Electrical Engineering (ESAT), Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quoc-Dung","family":"Ngo","sequence":"additional","affiliation":[{"name":"People\u2019s Security Academy of Hanoi, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. 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