{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T22:40:06Z","timestamp":1749422406346,"version":"3.41.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030779764"},{"type":"electronic","value":"9783030779771"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-77977-1_32","type":"book-chapter","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T07:07:25Z","timestamp":1623222445000},"page":"401-407","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards Data-Driven Simulation Models for Building Energy Management"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0439-3692","authenticated-orcid":false,"given":"Juan","family":"G\u00f3mez-Romero","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5688-2039","authenticated-orcid":false,"given":"Miguel","family":"Molina-Solana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-017-00839-3","volume":"8","author":"F Brockherde","year":"2017","unstructured":"Brockherde, F., Vogt, L., Li, L., Tuckerman, M.E., Burke, K., M\u00fcller, K.R.: Bypassing the kohn-sham equations with machine learning. Nat. Commun. 8, 1\u201310 (2017). https:\/\/doi.org\/10.1038\/s41467-017-00839-3","journal-title":"Nat. Commun."},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2018","unstructured":"Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8, 1\u201312 (2018). https:\/\/doi.org\/10.1038\/s41598-018-24271-9","journal-title":"Sci. Rep."},{"key":"32_CR3","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1146\/annurev-control-053018-023744","volume":"2","author":"A Chiuso","year":"2019","unstructured":"Chiuso, A., Pillonetto, G.: System identification: a machine learning perspective. Ann. Rev. Control Robot. Auton. Syst. 2, 281\u2013304 (2019). https:\/\/doi.org\/10.1146\/annurev-control-053018-023744","journal-title":"Ann. Rev. Control Robot. Auton. Syst."},{"key":"32_CR4","doi-asserted-by":"publisher","unstructured":"Ferracuti, F., et al.: Data-driven models for short-term thermal behaviour prediction in real buildings. Appl. Energy 204, 1375\u20131387 (2017). https:\/\/doi.org\/10.1016\/j.apenergy.2017.05.015","DOI":"10.1016\/j.apenergy.2017.05.015"},{"key":"32_CR5","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1109\/MCS.2006.252834","volume":"26","author":"M Gevers","year":"2006","unstructured":"Gevers, M.: A personal view of the development of system identification: a 30-year journey through an exciting field. IEEE Control. Syst. 26, 93\u2013105 (2006). https:\/\/doi.org\/10.1109\/MCS.2006.252834","journal-title":"IEEE Control. Syst."},{"key":"32_CR6","unstructured":"G\u00f3mez, J., Molina-Solana, M.: Towards self-adaptive building energy control in smart grids. In: NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning. Vancouver, Canada, December 2019. https:\/\/www.climatechange.ai\/papers\/neurips2019\/49"},{"key":"32_CR7","doi-asserted-by":"publisher","unstructured":"G\u00f3mez-Romero, J., et al.: A probabilisticalgorithm for predictive control with full-complexity models innon-residential buildings. IEEE Access 7, 38748\u201338765 (2019).https:\/\/doi.org\/10.1109\/ACCESS.2019.2906311","DOI":"10.1109\/ACCESS.2019.2906311"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Kasim, M.F., et al.: Building high accuracy emulators for scientific simulations with deep neural architecture search (2020)","DOI":"10.1088\/2632-2153\/ac3ffa"},{"key":"32_CR9","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.buildenv.2016.05.034","volume":"105","author":"M Killian","year":"2016","unstructured":"Killian, M., Kozek, M.: Ten questions concerning model predictive control for energy efficient buildings. Build. Environ. 105, 403\u2013412 (2016). https:\/\/doi.org\/10.1016\/j.buildenv.2016.05.034","journal-title":"Build. Environ."},{"key":"32_CR10","doi-asserted-by":"publisher","unstructured":"Kwan, J., et al.: Cosmic emulation: fast predictions for the galaxy power spectrum. The Astrophysical Journal 810 (2015). https:\/\/doi.org\/10.1088\/0004-637X\/810\/1\/35","DOI":"10.1088\/0004-637X\/810\/1\/35"},{"key":"32_CR11","doi-asserted-by":"publisher","unstructured":"Lee, J.H.: Model predictive control: review of the three decades of development. Int. J. Control Autom. Syst. 9, (2011). https:\/\/doi.org\/10.1007\/s12555-011-0300-6","DOI":"10.1007\/s12555-011-0300-6"},{"key":"32_CR12","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.applthermaleng.2018.08.041","volume":"144","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Dinh, N., Sato, Y., Niceno, B.: Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results. Appl. Thermal Eng. 144, 305\u2013320 (2018). https:\/\/doi.org\/10.1016\/j.applthermaleng.2018.08.041","journal-title":"Appl. Thermal Eng."},{"key":"32_CR13","unstructured":"Ljung, L.: System Identification: Theory for the User. Prentice Hall, Hoboken (1999)"},{"key":"32_CR14","unstructured":"Loten, A.: More manufacturers bet on simulation software. Wall Street J. (2020). https:\/\/www.wsj.com\/articles\/more-manufacturers-bet-on-simulation-software-11582240105"},{"key":"32_CR15","unstructured":"Luong, M.T., Le, Q.V., Sutskever, I., Vinyals, O., Kaiser, L.: Multi-task sequence to sequence learning. In: International Conference on Learning Representations (ICLR 2016) (2016). http:\/\/arxiv.org\/abs\/1511.06114"},{"key":"32_CR16","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.rser.2016.11.132","volume":"70","author":"M Molina-Solana","year":"2017","unstructured":"Molina-Solana, M., Ros, M., Ruiz, M.D., G\u00f3mez-Romero, J., Martin-Bautista, M.J.: Data science for building energy management: a review. Renew. Sustain. Energy Rev. 70, 598\u2013609 (2017). https:\/\/doi.org\/10.1016\/j.rser.2016.11.132","journal-title":"Renew. Sustain. Energy Rev."},{"key":"32_CR17","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019). https:\/\/doi.org\/10.1016\/j.jcp.2018.10.045","journal-title":"J. Comput. Phys."},{"key":"32_CR18","doi-asserted-by":"publisher","unstructured":"Shi, H., Xu, M., Li, R.: Deep learning for household load forecasting\u2013a novel pooling deep RNN. IEEE Trans. Smart Grid 9, 5271\u20135280 (2018). https:\/\/doi.org\/10.1109\/TSG.2017.2686012","DOI":"10.1109\/TSG.2017.2686012"},{"key":"32_CR19","doi-asserted-by":"publisher","unstructured":"Shumway, R.H.: Time Series Analysis and its Applications. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-52452-8","DOI":"10.1007\/978-3-319-52452-8"},{"key":"32_CR20","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, CA, USA, December 2017"},{"key":"32_CR21","doi-asserted-by":"publisher","unstructured":"Wagg, D.J., Worden, K., Barthorpe, R.J., Gardner, P.: Digital twins: state-of-the-art and future directions for modeling and simulation in engineering dynamics applications. ASME J. Risk Uncertainty Part B 6(3) (2020). https:\/\/doi.org\/10.1115\/1.4046739","DOI":"10.1115\/1.4046739"},{"key":"32_CR22","doi-asserted-by":"publisher","unstructured":"Zhu, J., Hu, S., Arcucci, R., Xu, C., Zhu, J., ke Guo, Y.: Model error correction in data assimilation by integrating neural networks. Big Data Mining and Analytics 2, 83\u201391 (2019). https:\/\/doi.org\/10.26599\/BDMA.2018.9020033","DOI":"10.26599\/BDMA.2018.9020033"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77977-1_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T22:03:55Z","timestamp":1749420235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77977-1_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030779764","9783030779771"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77977-1_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krakow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2021\/","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":"156","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":"48","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":"14","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":"31% - 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":"2.8","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":"3.9","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":"212 full and 43 short papers were selected from 479 submissions to the workshops\/ thematic tracks. The conference was held virtually.","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)"}}]}}