{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T07:10:26Z","timestamp":1760080226665,"version":"3.37.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030043025"},{"type":"electronic","value":"9783030043032"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","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":[[2018]]},"DOI":"10.1007\/978-3-030-04303-2_7","type":"book-chapter","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T05:42:01Z","timestamp":1542346921000},"page":"94-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning as Surrogate to Building Performance Simulation: A Building Design Optimization Application"],"prefix":"10.1007","author":[{"given":"Sokratis","family":"Papadopoulos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6155-1741","authenticated-orcid":false,"given":"Wei Lee","family":"Woon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7273-4853","authenticated-orcid":false,"given":"Elie","family":"Azar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,17]]},"reference":[{"issue":"2","key":"7_CR1","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.cemconres.2007.09.008","volume":"38","author":"JS Damtoft","year":"2008","unstructured":"Damtoft, J.S., Lukasik, J., Herfort, D., Sorrentino, D., Gartner, E.M.: Sustainable development and climate change initiatives. Cem. Concr. Res. 38(2), 115\u2013127 (2008)","journal-title":"Cem. Concr. Res."},{"key":"7_CR2","unstructured":"ASHRAE: Advanced Energy Design Guide for Small and Medium Office Buildings. American Society of Heating Refrigerating and Air-Conditioning Engineers Inc., Atlanta (2011)"},{"issue":"4","key":"7_CR3","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/j.buildenv.2006.10.027","volume":"43","author":"DB Crawley","year":"2008","unstructured":"Crawley, D.B., Hand, J.W., Kummert, M., Griffith, B.T.: Contrasting the capabilities of building energy performance simulation programs. Build. Environ. 43(4), 661\u2013673 (2008)","journal-title":"Build. Environ."},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Papadopoulos, S., Azar, E.: Optimizing HVAC operation in commercial buildings: a genetic algorithm multi-objective optimization framework. In: Proceedings of the 2016 Winter Simulation Conference, Washington D.C. (2016)","DOI":"10.1109\/WSC.2016.7822220"},{"key":"7_CR5","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.autcon.2013.10.007","volume":"38","author":"S-HE Lin","year":"2014","unstructured":"Lin, S.-H.E., Gerber, D.J.: Designing-in performance: a framework for evolutionary energy performance feedback in early stage design. Autom. Constr. 38, 59\u201373 (2014)","journal-title":"Autom. Constr."},{"issue":"7","key":"7_CR6","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1016\/j.buildenv.2010.01.005","volume":"45","author":"D Tuhus-Dubrow","year":"2010","unstructured":"Tuhus-Dubrow, D., Krarti, M.: Genetic-algorithm based approach to optimize building envelope design for residential buildings. Build. Environ. 45(7), 1574\u20131581 (2010)","journal-title":"Build. Environ."},{"issue":"1","key":"7_CR7","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.aei.2007.08.012","volume":"22","author":"L Caldas","year":"2008","unstructured":"Caldas, L.: Generation of energy-efficient architecture solutions applying GENE ARCH: An evolution-based generative design system. Adv. Eng. Inform. 22(1), 59\u201370 (2008)","journal-title":"Adv. Eng. Inform."},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.enbuild.2016.06.079","volume":"128","author":"S Papadopoulos","year":"2016","unstructured":"Papadopoulos, S., Azar, E.: Integrating building performance simulation in agent-based modeling using regression surrogate models: a novel human-in-the-loop energy modeling approach. Energy Build. 128, 214\u2013223 (2016)","journal-title":"Energy Build."},{"key":"7_CR9","unstructured":"Gilan, S.S., Dilkina, B.: Sustainable building design: a challenge at the intersection of machine learning and design optimization. In: Proceedings of the Workshops at the 29th AAAI Conference on Artificial Intelligence, Austin, TX (2015)"},{"key":"7_CR10","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.enbuild.2014.06.009","volume":"81","author":"E Asadi","year":"2014","unstructured":"Asadi, E., da Silva, M.G., Antunes, C.H., Dias, L., Glicksman, L.: Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application. Energy Build. 81, 444\u2013456 (2014)","journal-title":"Energy Build."},{"issue":"3","key":"7_CR11","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1016\/j.buildenv.2009.08.016","volume":"45","author":"L Magnier","year":"2010","unstructured":"Magnier, L., Haghighat, F.: Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and artificial neural network. Build. Environ. 45(3), 739\u2013746 (2010)","journal-title":"Build. Environ."},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.compag.2015.07.017","volume":"117","author":"L Brillante","year":"2015","unstructured":"Brillante, L., Gaiotti, F., Lovat, L., Vincenzi, S., Giacosa, S., Torchio, F., Tomasi, D.: Investigating the use of gradient boosting machine, random forest and their ensemble to predict skin flavonoid content from berry physical\u2013mechanical characteristics in wine grapes. Comput. Electron. Agric. 117, 186\u2013193 (2015)","journal-title":"Comput. Electron. Agric."},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.trc.2015.02.019","volume":"58","author":"Y Zhang","year":"2015","unstructured":"Zhang, Y., Haghani, A.: A gradient boosting method to improve travel time prediction. Transp. Res. Part C Emerg. Technol. 58, 308\u2013324 (2015)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"7_CR14","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189\u20131232 (2001)","journal-title":"Ann. Stat."},{"issue":"3","key":"7_CR15","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1080\/19401493.2017.1354919","volume":"11","author":"S Papadopoulos","year":"2018","unstructured":"Papadopoulos, S., Azar, E., Woon, W.L., Kontokosta, C.E.: Evaluation of tree-based ensemble learning algorithms for building energy performance estimation. J. Build. Perform. Simul. 11(3), 322\u2013332 (2018)","journal-title":"J. Build. Perform. Simul."},{"key":"7_CR16","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.enbuild.2012.03.003","volume":"49","author":"A Tsanas","year":"2012","unstructured":"Tsanas, A., Xifara, A.: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build. 49, 560\u2013567 (2012)","journal-title":"Energy Build."},{"issue":"2","key":"7_CR17","first-page":"129","volume":"3","author":"DE Goldberg","year":"1989","unstructured":"Goldberg, D.E.: Genetic algorithms and Walsh functions: Part I, a gentle introduction. Complex Syst. 3(2), 129\u2013152 (1989)","journal-title":"Complex Syst."},{"issue":"2","key":"7_CR18","first-page":"153","volume":"3","author":"DE Goldberg","year":"1989","unstructured":"Goldberg, D.E.: Genetic algorithms and Walsh functions: Part II, deception and its analysis. Complex Syst. 3(2), 153\u2013171 (1989)","journal-title":"Complex Syst."}],"container-title":["Lecture Notes in Computer Science","Data Analytics for Renewable Energy Integration. Technologies, Systems and Society"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-04303-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T05:43:53Z","timestamp":1542347033000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-04303-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030043025","9783030043032"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-04303-2_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"DARE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Data Analytics for Renewable Energy Integration","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dare2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ecmlpkdd2018.org\/workshops\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}