{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T18:46:38Z","timestamp":1769280398060,"version":"3.49.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031486487","type":"print"},{"value":"9783031486494","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-48649-4_8","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T06:03:13Z","timestamp":1701410593000},"page":"135-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Real-Time Non-Invasive Anomaly Detection Technique for\u00a0Cooling Systems"],"prefix":"10.1007","author":[{"given":"Keshav","family":"Kaushik","sequence":"first","affiliation":[]},{"given":"Vinayak","family":"Naik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGboost: a scalable tree boosting system. In: KDD (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"8_CR2","unstructured":"Dorogush, A.V., Ershov, V., Gulin, A.: CatBoost: gradient boosting with categorical features support. CoRR abs\/1810.11363 http:\/\/arxiv.org\/abs\/1810.11363 (2018)"},{"key":"8_CR3","unstructured":"EnergyPlus (2021). https:\/\/energyplus.net"},{"key":"8_CR4","doi-asserted-by":"publisher","unstructured":"Frank, S.M., Kim, J., Cai, J., Braun, J.E.: Common faults and their prioritization in small commercial buildings: February 2017 - December 2017 (2018). https:\/\/doi.org\/10.2172\/1457127, https:\/\/www.osti.gov\/biblio\/1457127","DOI":"10.2172\/1457127"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Kaushik, K., Agrawal, P., Naik, V.: A dynamic scheduling technique to optimize energy consumption by ductless-split ACs. In: ICOIN (2023)","DOI":"10.1109\/ICOIN56518.2023.10048941"},{"key":"8_CR6","unstructured":"Ke, G., et al.: LightGbm: a highly efficient gradient boosting decision tree. In: NIPS (2017)"},{"issue":"1","key":"8_CR7","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1080\/10789669.2009.10390828","volume":"15","author":"H Li","year":"2009","unstructured":"Li, H., Braun, J.E.: Development, evaluation, and demonstration of a virtual refrigerant charge sensor. HVAC &R Res. 15(1), 117\u2013136 (2009)","journal-title":"HVAC &R Res."},{"key":"8_CR8","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.enbuild.2019.07.011","volume":"199","author":"Y Li","year":"2019","unstructured":"Li, Y., O\u2019Neill, Z.: An innovative fault impact analysis framework for enhancing building operations. Energ. Build. 199, 311\u2013331 (2019)","journal-title":"Energ. Build."},{"issue":"11","key":"8_CR9","doi-asserted-by":"publisher","first-page":"8973","DOI":"10.1016\/j.aej.2022.02.038","volume":"61","author":"A Malki","year":"2022","unstructured":"Malki, A., Atlam, E.S., Gad, I.: Machine learning approach of detecting anomalies and forecasting time-series of IoT devices. Alex. Eng. J. 61(11), 8973\u20138986 (2022)","journal-title":"Alex. Eng. J."},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Narayanaswamy, B., Balaji, B., Gupta, R., Agarwal, Y.: Data driven investigation of faults in HVAC systems with model, cluster and compare (MCC). In: Buildsys (2014)","DOI":"10.1145\/2674061.2674067"},{"key":"8_CR11","first-page":"7","volume":"100","author":"HS Ramadan","year":"2022","unstructured":"Ramadan, H.S., Maghawry, H.A., El-Eleamy, M., El-Bahnasy, K.: A heuristic novel approach for determination of optimal epsilon for DBSCAN clustering algorithm. J. Theor. Appl. Inf. Technol. 100, 7 (2022)","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Rashid, H., Singh, P.: Monitor: An abnormality detection approach in buildings energy consumption. In: IEEE CIC (2018)","DOI":"10.1109\/CIC.2018.00-44"},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.apenergy.2019.01.061","volume":"238","author":"H Rashid","year":"2019","unstructured":"Rashid, H., Singh, P., Stankovic, V., Stankovic, L.: Can non-intrusive load monitoring be used for identifying an appliance\u2019s anomalous behaviour? Appl. Energ. 238, 796\u2013805 (2019)","journal-title":"Appl. Energ."},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Sathe, S., Aggarwal, C.: Lodes: local density meets spectral outlier detection. In: SDM 2016","DOI":"10.1137\/1.9781611974348.20"},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3068335","volume":"42","author":"E Schubert","year":"2017","unstructured":"Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. 42, 1\u201321 (2017)","journal-title":"ACM Trans. Database Syst."},{"key":"8_CR16","unstructured":"Sefidian, A.M.: How to determine epsilon and minpts parameters of dbscan clustering (2021). http:\/\/www.sefidian.com\/2020\/12\/18\/how-to-determine-epsilon-and-minpts-parameters-of-dbscan-clustering\/"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Vishwanath, A., Chandan, V., Mendoza, C., Blake, C.: A data driven pre-cooling framework for energy cost optimization in commercial buildings. In: e-Energy (2017)","DOI":"10.1145\/3077839.3077847"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Zhao, X., Liu, H., Fan, W., Liu, H., Tang, J., Wang, C.: AutoLoss: automated loss function search in recommendations. In: KDD (2021)","DOI":"10.1145\/3447548.3467208"},{"key":"8_CR19","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1016\/j.applthermaleng.2015.09.121","volume":"111","author":"Y Zhao","year":"2017","unstructured":"Zhao, Y., Wen, J., Xiao, F., Yang, X., Wang, S.: Diagnostic Bayesian networks for diagnosing air handling units faults - part i: faults in dampers, fans, filters and sensors. Appl. Therm. Eng. 111, 1272\u20131286 (2017)","journal-title":"Appl. Therm. Eng."}],"container-title":["Lecture Notes in Computer Science","Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-48649-4_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T06:05:16Z","timestamp":1701410716000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-48649-4_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"ISBN":["9783031486487","9783031486494"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-48649-4_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]},"assertion":[{"value":"2 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EI.A","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Energy Informatics Academy Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Campinas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.energyinformatics.academy\/eia-2023-conference","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3 other papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}