{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T13:48:51Z","timestamp":1726408131255},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030298586"},{"type":"electronic","value":"9783030298593"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-29859-3_16","type":"book-chapter","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T16:03:53Z","timestamp":1566835433000},"page":"181-192","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parsimonious Modeling for Estimating Hospital Cooling Demand to Reduce Maintenance Costs and Power Consumption"],"prefix":"10.1007","author":[{"given":"Eduardo","family":"Dulce","sequence":"first","affiliation":[]},{"given":"Francisco Javier","family":"Martinez-de-Pison","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,26]]},"reference":[{"key":"16_CR1","unstructured":"OECD\/IEA: International energy agency (2014)"},{"key":"16_CR2","unstructured":"IDAE, Fenercom: Gu\u00eda de ahorro y eficiencia energ\u00e9tica en hospitales. Fenercom (2010)"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.scs.2018.08.008","volume":"42","author":"SH Yoon","year":"2018","unstructured":"Yoon, S.H., Kim, S.Y., Park, G.H., Kim, Y.K., Cho, C.H., Park, B.H.: Multiple power-based building energy management system for efficient management of building energy. Sustain. Cities Soc. 42, 462\u2013470 (2018)","journal-title":"Sustain. Cities Soc."},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.enbuild.2013.12.018","volume":"71","author":"R Missaoui","year":"2014","unstructured":"Missaoui, R., Joumaa, H., Ploix, S., Bacha, S.: Managing energy smart homes according to energy prices: analysis of a building energy management system. Energy Build. 71, 155\u2013167 (2014)","journal-title":"Energy Build."},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/978-3-319-30746-6_30","volume-title":"Mediterranean Green Buildings & Renewable Energy","author":"M Palme","year":"2017","unstructured":"Palme, M.: The possible shift between heating and cooling demand of buildings under climate change conditions: are some mitigation policies wrongly understood? In: Sayigh, A. (ed.) Mediterranean Green Buildings & Renewable Energy, pp. 417\u2013422. Springer International Publishing, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-30746-6_30"},{"key":"16_CR6","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1016\/j.applthermaleng.2018.11.122","volume":"148","author":"M Saeedi","year":"2019","unstructured":"Saeedi, M., Moradi, M., Hosseini, M., Emamifar, A., Ghadimi, N.: Robust optimization based optimal chiller loading under cooling demand uncertainty. Appl. Therm. Eng. 148, 1081\u20131091 (2019)","journal-title":"Appl. Therm. Eng."},{"key":"16_CR7","doi-asserted-by":"publisher","first-page":"1740","DOI":"10.1016\/j.apenergy.2018.07.085","volume":"228","author":"L Wang","year":"2018","unstructured":"Wang, L., Lee, E.W., Yuen, R.K.: Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach. Appl. Energy 228, 1740\u20131753 (2018)","journal-title":"Appl. Energy"},{"issue":"2","key":"16_CR8","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.ijepes.2005.11.006","volume":"28","author":"R Abdel-Aal","year":"2006","unstructured":"Abdel-Aal, R.: Modeling and forecasting electric daily peak loads using abductive networks. Int. J. Electr. Power Energy Syst. 28(2), 133\u2013141 (2006)","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"16_CR9","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.enbuild.2015.04.011","volume":"99","author":"H Chitsaz","year":"2015","unstructured":"Chitsaz, H., Shaker, H., Zareipour, H., Wood, D., Amjady, N.: Short-term electricity load forecasting of buildings in microgrids. Energy Build. 99, 50\u201360 (2015)","journal-title":"Energy Build."},{"key":"16_CR10","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.apenergy.2018.02.165","volume":"218","author":"M Shepero","year":"2018","unstructured":"Shepero, M., van der Meer, D., Munkhammar, J., Wid\u00e9n, J.: Residential probabilistic load forecasting: a method using Gaussian process designed for electric load data. Appl. Energy 218, 159\u2013172 (2018)","journal-title":"Appl. Energy"},{"key":"16_CR11","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.energy.2018.08.169","volume":"164","author":"Y Li","year":"2018","unstructured":"Li, Y., Che, J., Yang, Y.: Subsampled support vector regression ensemble for short term electric load forecasting. Energy 164, 160\u2013170 (2018)","journal-title":"Energy"},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1016\/j.apenergy.2019.01.127","volume":"238","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Che, J., Deng, C., Li, L.: Sequential grid approach based support vector regression for short-term electric load forecasting. Appl. Energy 238, 1010\u20131021 (2019)","journal-title":"Appl. Energy"},{"issue":"Complete","key":"16_CR13","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.enbuild.2015.05.056","volume":"103","author":"A Bagnasco","year":"2015","unstructured":"Bagnasco, A., Fresi, F., Saviozzi, M., Silvestro, F., Vinci, A.: Electrical consumption forecasting in hospital facilities: an application case. Energy Buildings 103(Complete), 261\u2013270 (2015)","journal-title":"Energy Buildings"},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.enbuild.2014.08.004","volume":"84","author":"JG Jetcheva","year":"2014","unstructured":"Jetcheva, J.G., Majidpour, M., Chen, W.P.: Neural network model ensembles for building-level electricity load forecasts. Energy Build. 84, 214\u2013223 (2014)","journal-title":"Energy Build."},{"issue":"28","key":"16_CR15","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1016\/j.ifacol.2018.11.783","volume":"51","author":"YY Hsu","year":"2018","unstructured":"Hsu, Y.Y., Tung, T.T., Yeh, H.C., Lu, C.N.: Two-stage artificial neural network model for short-term load forecasting. IFAC-PapersOnLine 51(28), 678\u2013683 (2018). 10th IFAC Symposium on Control of Power and Energy Systems CPES 2018","journal-title":"IFAC-PapersOnLine"},{"key":"16_CR16","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.energy.2019.02.141","volume":"174","author":"P Singh","year":"2019","unstructured":"Singh, P., Dwivedi, P., Kant, V.: A hybrid method based on neural network and improved environmental adaptation method using controlled gaussian mutation with real parameter for short-term load forecasting. Energy 174, 460\u2013477 (2019)","journal-title":"Energy"},{"issue":"6","key":"16_CR17","doi-asserted-by":"publisher","first-page":"2851","DOI":"10.1016\/j.csda.2006.10.007","volume":"51","author":"M Avalos","year":"2007","unstructured":"Avalos, M., Grandvalet, Y., Ambroise, C.: Parsimonious additive models. Comput. Stat. Data Anal. 51(6), 2851\u20132870 (2007)","journal-title":"Comput. Stat. Data Anal."},{"key":"16_CR18","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.csda.2017.09.004","volume":"118","author":"H Li","year":"2018","unstructured":"Li, H., Shu, D., Zhang, Y., Yi, G.Y.: Simultaneous variable selection and estimation for multivariate multilevel longitudinal data with both continuous and binary responses. Comput. Stat. Data Anal. 118, 126\u2013137 (2018)","journal-title":"Comput. Stat. Data Anal."},{"key":"16_CR19","unstructured":"Husain, H., Handel, N.: Automated machine learning. A paradigm shift that accelerates data scientist productivity, May 2017"},{"key":"16_CR20","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems vol. 28, pp. 2962\u20132970. Curran Associates, Inc. (2015)"},{"key":"16_CR21","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.asoc.2015.06.012","volume":"35","author":"A Sanz-Garcia","year":"2015","unstructured":"Sanz-Garcia, A., Fernandez-Ceniceros, J., Antonanzas-Torres, F., Pernia-Espinoza, A., Martinez-de Pison, F.J.: GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace. Appl. Soft Comput. 35, 13\u201328 (2015)","journal-title":"Appl. Soft Comput."},{"issue":"Supplement C","key":"16_CR22","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2016.08.154","volume":"271","author":"R Urraca","year":"2018","unstructured":"Urraca, R., Sodupe-Ortega, E., Antonanzas, J., Antonanzas-Torres, F., de Pison, F.M.: Evaluation of a novel GA-based methodology for model structure selection: the GA-PARSIMONY. Neurocomputing 271(Supplement C), 9\u201317 (2018)","journal-title":"Neurocomputing"},{"key":"16_CR23","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-01854-6_1","volume-title":"International Joint Conference SOCO13-CISIS13-ICEUTE13","author":"A Sanz-Garc\u00eda","year":"2014","unstructured":"Sanz-Garc\u00eda, A., Fern\u00e1ndez-Ceniceros, J., Anto\u00f1anzas-Torres, F., Mart\u00ednez-de Pis\u00f3n, F.J.: Parsimonious support vector machines modelling for set points in industrial processes based on genetic algorithm optimization. In: Herrero, \u00c1., et al. (eds.) International Joint Conference SOCO13-CISIS13-ICEUTE13. Advances in Intelligent Systems and Computing, vol. 239, pp. 1\u201310. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-01854-6_1"},{"key":"16_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1007\/978-3-319-19644-2_52","volume-title":"Hybrid Artificial Intelligent Systems","author":"R Urraca","year":"2015","unstructured":"Urraca, R., Sanz-Garcia, A., Fernandez-Ceniceros, J., Sodupe-Ortega, E., Martinez-de-Pison, F.J.: Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning. In: Onieva, E., Santos, I., Osaba, E., Quinti\u00e1n, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 632\u2013643. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-19644-2_52"},{"key":"16_CR25","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.engstruct.2014.06.047","volume":"82","author":"J Fernandez-Ceniceros","year":"2015","unstructured":"Fernandez-Ceniceros, J., Sanz-Garcia, A., Antonanzas-Torres, F., de Pison, F.M.: A numerical-informational approach for characterising the ductile behaviour of the T-stub component. Part 2: Parsimonious soft-computing-based metamodel. Eng. Struct. 82, 249\u2013260 (2015)","journal-title":"Eng. Struct."},{"key":"16_CR26","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.enconman.2015.02.086","volume":"96","author":"F Antonanzas-Torres","year":"2015","unstructured":"Antonanzas-Torres, F., Urraca, R., Antonanzas, J., Fernandez-Ceniceros, J., de Pison, F.M.: Generation of daily global solar irradiation with support vector machines for regression. Energy Convers. Manag. 96, 277\u2013286 (2015)","journal-title":"Energy Convers. Manag."},{"key":"16_CR27","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/978-3-319-47364-2_20","volume-title":"International Joint Conference SOCO\u201916-CISIS\u201916-ICEUTE\u201916","author":"FJ Martinez-de-Pison","year":"2017","unstructured":"Martinez-de-Pison, F.J., Fraile-Garcia, E., Ferreiro-Cabello, J., Gonzalez, R., Pernia, A.: Searching parsimonious solutions with GA-PARSIMONY and XGBoost in high-dimensional databases. In: Gra\u00f1a, M., L\u00f3pez-Guede, J.M., Etxaniz, O., Herrero, \u00c1., Quinti\u00e1n, H., Corchado, E. (eds.) ICEUTE\/SOCO\/CISIS -2016. AISC, vol. 527, pp. 201\u2013210. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-47364-2_20"},{"issue":"C","key":"16_CR28","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.asoc.2015.06.012","volume":"35","author":"A Sanz-Garcia","year":"2015","unstructured":"Sanz-Garcia, A., Fernandez-Ceniceros, J., Antonanzas-Torres, F., Pernia-Espinoza, A., Martinez-de Pison, F.: GA-parsimony. Appl. Soft Comput. 35(C), 13\u201328 (2015)","journal-title":"Appl. Soft Comput."},{"key":"16_CR29","unstructured":"Mart\u00ednez-De-Pis\u00f3n, F.J.: GAparsimony: GA-based optimization R package for searching accurate parsimonious models (2017). R package version 0.9-1"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29859-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:09:36Z","timestamp":1710266976000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-29859-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030298586","9783030298593"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29859-3_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"26 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Le\u00f3n","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2019.haisconference.eu\/","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":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","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":"64","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":"0","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":"48% - 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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}