{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:36:27Z","timestamp":1742931387836,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030601164"},{"type":"electronic","value":"9783030601171"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60117-1_31","type":"book-chapter","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T12:02:43Z","timestamp":1602849763000},"page":"417-433","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SANDFOX Project Optimizing the Relationship Between the User Interface and Artificial Intelligence to Improve Energy Management in Smart Buildings"],"prefix":"10.1007","author":[{"given":"Christophe","family":"Bortolaso","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"St\u00e9phanie","family":"Combettes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marie-Pierre","family":"Gleizes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Berangere","family":"Lartigue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathieu","family":"Raynal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"St\u00e9phanie","family":"Rey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,17]]},"reference":[{"key":"31_CR1","unstructured":"Beaudouin-Lafon, M., Mackay, W.E.: Prototyping tools and techniques. In: Human-Computer Interaction, pp. 137\u2013160. CRC Press (2009)"},{"key":"31_CR2","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.enbuild.2017.05.051","volume":"149","author":"L Brady","year":"2017","unstructured":"Brady, L., Abdellatif, M.: Assessment of energy consumption in existing buildings. Energy Build. 149, 142\u2013150 (2017)","journal-title":"Energy Build."},{"issue":"9","key":"31_CR3","doi-asserted-by":"publisher","first-page":"4324","DOI":"10.1016\/j.eswa.2015.01.010","volume":"42","author":"A Capozzoli","year":"2015","unstructured":"Capozzoli, A., Lauro, F., Khan, I.: Fault detection analysis using data mining techniques for a cluster of smart office buildings. Expert Syst. Appl. 42(9), 4324\u20134338 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"31_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1\u201358 (2009)","journal-title":"ACM Comput. Surv."},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Chen, B., Sinn, M., Ploennigs, J., Schumann, A.: Statistical anomaly detection in mean and variation of energy consumption. In: 2014 22nd International Conference on Pattern Recognition, pp. 3570\u20133575 (2014)","DOI":"10.1109\/ICPR.2014.614"},{"key":"31_CR6","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.rser.2014.01.088","volume":"33","author":"JS Chou","year":"2014","unstructured":"Chou, J.S., Telaga, A.S.: Real-time detection of anomalous power consumption. Renew. Sustain. Energy Rev. 33, 400\u2013411 (2014)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"31_CR7","unstructured":"https:\/\/ec.europa.eu\/energy\/en\/topics\/energy-efficiency\/energy-performance-of-buildings. Accessed 17 Mar 2020"},{"key":"31_CR8","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.enbuild.2015.09.060","volume":"109","author":"C Fan","year":"2015","unstructured":"Fan, C., Xiao, F., Madsen, H., Wang, D.: Temporal knowledge discovery in big BAS data for building energy management. Energy Build. 109, 75\u201389 (2015)","journal-title":"Energy Build."},{"key":"31_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.ins.2016.09.007","volume":"373","author":"A Forestiero","year":"2016","unstructured":"Forestiero, A.: Self-organizing anomaly detection in data streams. Inf. Sci. 373, 321\u2013336 (2016)","journal-title":"Inf. Sci."},{"key":"31_CR10","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1016\/j.rser.2015.05.081","volume":"50","author":"T Labeodan","year":"2015","unstructured":"Labeodan, T., Aduda, K., Boxem, G., Zeiler, W.: On the application of multi-agent systems in buildings for improved building operations, performance and smart grid interaction - a survey. Renew. Sustain. Energy Rev. 50, 1405\u20131414 (2015)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"31_CR11","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.enbuild.2013.08.044","volume":"68","author":"S Li","year":"2014","unstructured":"Li, S., Wen, J.: A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. Energy Build. 68, 63\u201371 (2014)","journal-title":"Energy Build."},{"key":"31_CR12","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.jclepro.2018.04.270","volume":"192","author":"M Lin","year":"2018","unstructured":"Lin, M., Afshari, A., Azar, E.: A data-driven analysis of building energy use with emphasis on operation and maintenance: a case study from the uae. J. Clean. Prod. 192, 169\u2013178 (2018)","journal-title":"J. Clean. Prod."},{"key":"31_CR13","unstructured":"Mackay, W.E.: Educating multi-disciplinary design teams. Proc. of Tales of the Disappearing Computer, pp. 105\u2013118 (2003)"},{"key":"31_CR14","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.eswa.2016.03.002","volume":"56","author":"M Pe\u00f1a","year":"2016","unstructured":"Pe\u00f1a, M., Biscarri, F., Guerrero, J.I., Monedero, I., Le\u00f3n, C.: Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach. Expert Syst. Appl. 56, 242\u2013255 (2016)","journal-title":"Expert Syst. Appl."},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Ploennigs, J., Chen, B., Schumann, A., Brady, N.: Exploiting generalized additive models for diagnosing abnormal energy use in buildings. In: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings - BuildSys 2013, pp. 1\u20138 (2013)","DOI":"10.1145\/2528282.2528291"},{"issue":"6","key":"31_CR16","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1016\/j.engappai.2010.01.026","volume":"23","author":"Y Seng Ng","year":"2010","unstructured":"Seng Ng, Y., Srinivasan, R.: Multi-agent based collaborative fault detection and identification in chemical processes. Eng. Appl. Artif. Intell. 23(6), 934\u2013949 (2010)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"31_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/693749","volume":"2015","author":"D Sklavounos","year":"2015","unstructured":"Sklavounos, D., Zervas, E., Tsakiridis, O., Stonham, J.: A subspace identification method for detecting abnormal behavior in HVAC systems. J. Energy 2015, 1\u201312 (2015)","journal-title":"J. Energy"},{"key":"31_CR18","unstructured":"https:\/\/www.iea.org\/reports\/the-critical-role-of-buildings. Accessed 17 Mar 2020"},{"key":"31_CR19","unstructured":"https:\/\/tsimulus.readthedocs.io\/en\/latest\/. Accessed 18 Dec 2019"},{"key":"31_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.enbuild.2017.06.008","volume":"151","author":"WJ Turner","year":"2017","unstructured":"Turner, W.J., Staino, A., Basu, B.: Residential HVAC fault detection using a system identification approach. Energy Build. 151, 1\u201317 (2017)","journal-title":"Energy Build."},{"issue":"3","key":"31_CR21","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/S0098-1354(02)00162-X","volume":"27","author":"V Venkatasubramanian","year":"2003","unstructured":"Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K.: A review of process fault detection and diagnosis: Part III: process history based methods. Comput. Chem. Eng. 27(3), 327\u2013346 (2003)","journal-title":"Comput. Chem. Eng."},{"key":"31_CR22","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.neucom.2017.08.026","volume":"273","author":"J Wu","year":"2018","unstructured":"Wu, J., Zeng, W., Yan, F.: Hierarchical temporal memory method for time-series-based anomaly detection. Neurocomputing 273, 535\u2013546 (2018)","journal-title":"Neurocomputing"},{"key":"31_CR23","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1016\/j.apenergy.2017.08.035","volume":"205","author":"P Xue","year":"2017","unstructured":"Xue, P., et al.: Fault detection and operation optimization in district heating substations based on data mining techniques. Appl. Energy 205, 926\u2013940 (2017)","journal-title":"Appl. Energy"},{"issue":"2016","key":"31_CR24","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.neucom.2016.09.076","volume":"228","author":"K Yan","year":"2017","unstructured":"Yan, K., Ji, Z., Shen, W.: Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM. Neurocomputing 228(2016), 205\u2013212 (2017)","journal-title":"Neurocomputing"},{"key":"31_CR25","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.enbuild.2011.12.018","volume":"47","author":"ZJ Yu","year":"2012","unstructured":"Yu, Z.J., Haghighat, F., Fung, B.C., Zhou, L.: A novel methodology for knowledge discovery through mining associations between building operational data. Energy Build. 47, 430\u2013440 (2012)","journal-title":"Energy Build."},{"key":"31_CR26","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.enbuild.2011.09.043","volume":"44","author":"Y Zhu","year":"2012","unstructured":"Zhu, Y., Jin, X., Du, Z.: Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal. Energy Build. 44, 7\u201316 (2012)","journal-title":"Energy Build."}],"container-title":["Lecture Notes in Computer Science","HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60117-1_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T23:07:09Z","timestamp":1729033629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60117-1_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030601164","9783030601171"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60117-1_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"17 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Copenhagen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2020.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}