{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T02:17:53Z","timestamp":1766197073654},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319245850"},{"type":"electronic","value":"9783319245867"}],"license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"DOI":"10.1007\/978-3-319-24586-7_21","type":"book-chapter","created":{"date-parts":[[2015,9,30]],"date-time":"2015-09-30T04:05:49Z","timestamp":1443585949000},"page":"306-319","source":"Crossref","is-referenced-by-count":2,"title":["CBR Model for Predicting a Building\u2019s Electricity Use: On-Line Implementation in the Absence of Historical Data"],"prefix":"10.1007","author":[{"given":"Radu","family":"Platon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacques","family":"Martel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiser","family":"Zoghlami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,11,26]]},"reference":[{"key":"21_CR1","unstructured":"Transition to Sustainable Buildings. International Energy Agency, Paris (2013)"},{"key":"21_CR2","unstructured":"North American Intelligent Buildings Roadmap. Continental Automated Buildings Association, Ottawa (2011)"},{"key":"21_CR3","unstructured":"Kreider, J.F., Haberl, J.S: Predicting hourly building energy use: the great energy predictor shootout \u2013 overview and discussion of results. In: Proceedings of the ASHRAE Annual Meeting, June 25\u201329 1994, pp. 1104\u20131118, Florida (1994)"},{"key":"21_CR4","first-page":"49","volume":"40","author":"JS Haberl","year":"1998","unstructured":"Haberl, J.S., Thamilseran, S.: Great energy predictor shootout II measuring retrofit savings. ASHRAE J. 40, 49\u201356 (1998)","journal-title":"ASHRAE J."},{"key":"21_CR5","doi-asserted-by":"publisher","first-page":"3586","DOI":"10.1016\/j.rser.2012.02.049","volume":"16","author":"H-X Zhao","year":"2012","unstructured":"Zhao, H.-X., Magoules, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16, 3586\u20133592 (2012)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.advengsoft.2008.05.003","volume":"40","author":"BB Ekici","year":"2009","unstructured":"Ekici, B.B., Aksoy, U.T.: Prediction of building energy consumption by using artificial neural networks. Adv. Eng. Softw. 40, 356\u2013362 (2009)","journal-title":"Adv. Eng. Softw."},{"key":"21_CR7","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/j.enbuild.2004.09.006","volume":"37","author":"PA Gonzalez","year":"2005","unstructured":"Gonzalez, P.A., Zamarreno, J.M.: Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy Build. 37, 595\u2013601 (2005)","journal-title":"Energy Build."},{"key":"21_CR8","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1016\/j.enbuild.2005.11.005","volume":"38","author":"S Karatasou","year":"2006","unstructured":"Karatasou, S., Santamouris, M., Geros, V.: Modeling and predicting building\u2019s energy use with artificial neural networks: methods and results. Energy Build. 38, 949\u2013958 (2006)","journal-title":"Energy Build."},{"key":"21_CR9","unstructured":"Ucenic, C., Atsalakis, G.: A neuro-fuzzy approach to forecast the electricity demand. In: Proceedings of the 2006 IASME\/WSEAS International Conference on Energy & Environmental Systems, pp. 299\u2013304, Chalkida, Greece (2006)"},{"key":"21_CR10","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.enbuild.2014.07.024","volume":"82","author":"G Escriv\u00e1-Escriv\u00e1","year":"2014","unstructured":"Escriv\u00e1-Escriv\u00e1, G., Rold\u00e1n-Blay, C., \u00c1lvarez-Bel, C.: Electrical consumption forecast using actual data of building end-use decomposition. Energy Build. 82, 73\u201381 (2014)","journal-title":"Energy Build."},{"key":"21_CR11","doi-asserted-by":"publisher","first-page":"3112","DOI":"10.1016\/j.enbuild.2011.08.008","volume":"43","author":"G Escriv\u00e1-Escriv\u00e1","year":"2011","unstructured":"Escriv\u00e1-Escriv\u00e1, G., \u00c1lvarez-Bel, C., Rold\u00e1n-Blay, C., Alc\u00e1zar-Ortega, M.: New artificial neural network prediction method for electrical consumption forecasting based on building end-uses. Energy Build. 43, 3112\u20133119 (2011)","journal-title":"Energy Build."},{"key":"21_CR12","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1016\/j.enbuild.2005.02.005","volume":"37","author":"J Yang","year":"2005","unstructured":"Yang, J., Rivard, H., Zmeureanu, R.: On-line building energy prediction using adaptive artificial neural networks. Energy Build. 37, 1250\u20131259 (2005)","journal-title":"Energy Build."},{"key":"21_CR13","doi-asserted-by":"publisher","first-page":"2169","DOI":"10.1016\/j.enbuild.2008.06.013","volume":"40","author":"AH Neto","year":"2008","unstructured":"Neto, A.H., Fiorelli, F.A.S.: Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Build. 40, 2169\u20132176 (2008)","journal-title":"Energy Build."},{"key":"21_CR14","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.apenergy.2012.02.052","volume":"95","author":"T Hong","year":"2012","unstructured":"Hong, T., Koo, C., Jeong, K.: A decision support model for reducing electric energy consumption in elementary school facilities. Appl. Energy 95, 253\u2013266 (2012)","journal-title":"Appl. Energy"},{"key":"21_CR15","unstructured":"Breekweg, M.R.B., Gruber, P., Ahmed, O.: Development of a generalized neural network model to detect faults in building energy performance \u2013 Part I. In: ASHRAE Transactions, Atlanta (2000)"},{"key":"21_CR16","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1016\/S0360-1323(00)00064-0","volume":"36","author":"S Kumar","year":"2001","unstructured":"Kumar, S., Mahdavib, A.: Integrating thermal comfort field data analysis in a case-based building simulation environment. Build. Environ. 36, 711\u2013720 (2001)","journal-title":"Build. Environ."},{"key":"21_CR17","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.enbuild.2014.06.017","volume":"81","author":"D Monfet","year":"2014","unstructured":"Monfet, D., Corsi, M., Choiniere, D., Arkhipova, E.: Development of an energy prediction tool for commercial buildings using case-based reasoning. Energy Build. 81, 152\u2013160 (2014)","journal-title":"Energy Build."},{"key":"21_CR18","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.enbuild.2015.01.047","volume":"92","author":"R Platon","year":"2015","unstructured":"Platon, R., Dehkordi, V.R., Martel, J.: Hourly prediction of a building\u2019s electricity consumption using case-based reasoning, artificial neural networks and principal component analysis. Energy Build. 92, 10\u201318 (2015)","journal-title":"Energy Build."},{"key":"21_CR19","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.enbuild.2010.10.008","volume":"43","author":"D Ndiayea","year":"2011","unstructured":"Ndiayea, D., Gabriel, K.: Principal component analysis of the electricity consumption in residential dwellings. Energy Build. 43, 446\u2013453 (2011)","journal-title":"Energy Build."},{"key":"21_CR20","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1016\/j.enbuild.2007.06.001","volume":"40","author":"JC Lam","year":"2008","unstructured":"Lam, J.C., Wan, K.W., Cheung, K.L., Yang, L.: Principal component analysis of electricity use in office buildings. Energy Build. 40, 828\u2013836 (2008)","journal-title":"Energy Build."},{"key":"21_CR21","unstructured":"ASHRAE Guideline 14: Measurement of energy and demand savings. In: ASHRAE, Atlanta (2002)"}],"container-title":["Lecture Notes in Computer Science","Case-Based Reasoning Research and Development"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-24586-7_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,30]],"date-time":"2019-05-30T23:11:40Z","timestamp":1559257900000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-24586-7_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"ISBN":["9783319245850","9783319245867"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-24586-7_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2015]]}}}