{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:13:31Z","timestamp":1771665211290,"version":"3.50.1"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031490071","type":"print"},{"value":"9783031490088","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-49008-8_25","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T13:04:15Z","timestamp":1702559055000},"page":"311-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Comparison of\u00a0Automated Machine Learning Tools for\u00a0Predicting Energy Building Consumption in\u00a0Smart Cities"],"prefix":"10.1007","author":[{"given":"Daniela","family":"Soares","sequence":"first","affiliation":[]},{"given":"Pedro Jos\u00e9","family":"Pereira","sequence":"additional","affiliation":[]},{"given":"Paulo","family":"Cortez","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"25_CR1","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 Build. 103, 261\u2013270 (2015)","journal-title":"Energy Build."},{"key":"25_CR2","unstructured":"Bi, J., Bennett, K.P.: Regression error characteristic curves. In: Fawcett, T., Mishra, N. (eds.) Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21\u201324, 2003, Washington, DC, USA, pp. 43\u201350. AAAI Press (2003)"},{"key":"25_CR3","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.enbuild.2015.10.019","volume":"109","author":"EM Burger","year":"2015","unstructured":"Burger, E.M., Moura, S.J.: Gated ensemble learning method for demand-side electricity load forecasting. Energy Build. 109, 23\u201334 (2015). https:\/\/doi.org\/10.1016\/j.enbuild.2015.10.019","journal-title":"Energy Build."},{"key":"25_CR4","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/j.energy.2018.09.144","volume":"165","author":"JS Chou","year":"2018","unstructured":"Chou, J.S., Tran, D.S.: Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy 165, 709\u2013726 (2018). https:\/\/doi.org\/10.1016\/j.energy.2018.09.144","journal-title":"Energy"},{"key":"25_CR5","doi-asserted-by":"publisher","unstructured":"Cortez, P., Matos, L.M., Pereira, P.J., Santos, N., Duque, D.: Forecasting store foot traffic using facial recognition, time series and support vector machines. In: Gra\u00f1a, M., L\u00f3pez-Guede, J.M., Etxaniz, O., Herrero, \u00c1., Quinti\u00e1n, H., Corchado, E. (eds.) International Joint Conference SOCO\u201916-CISIS\u201916-ICEUTE\u201916 - San Sebasti\u00e1n, Spain, October 19th-21st, 2016, Proceedings. Advances in Intelligent Systems and Computing, vol. 527, pp. 267\u2013276 (2016). https:\/\/doi.org\/10.1007\/978-3-319-47364-2_26","DOI":"10.1007\/978-3-319-47364-2_26"},{"key":"25_CR6","unstructured":"Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., Smola, A.J.: Autogluon-tabular: Robust and accurate automl for structured data. CoRR abs\/2003.06505 (2020)"},{"key":"25_CR7","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.aej.2022.12.015","volume":"67","author":"M Faiq","year":"2023","unstructured":"Faiq, M., Tan, K.G., Liew, C.P., Hossain, F., Tso, C.P., Lim, L.L., Wong, A.Y.K., Shah, Z.M.: Prediction of energy consumption in campus buildings using long short-term memory. Alex. Eng. J. 67, 65\u201376 (2023)","journal-title":"Alex. Eng. J."},{"key":"25_CR8","doi-asserted-by":"publisher","unstructured":"Ferreira, L., Pilastri, A.L., Martins, C.M., Pires, P.M., Cortez, P.: A comparison of automl tools for machine learning, deep learning and xgboost. In: International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen, China, July 18\u201322, 2021, pp. 1\u20138. IEEE (2021). https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9534091","DOI":"10.1109\/IJCNN52387.2021.9534091"},{"key":"25_CR9","volume-title":"Nonparametric Statistical Methods","author":"M Hollander","year":"2013","unstructured":"Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods. Wiley, NJ, USA (2013)"},{"key":"25_CR10","unstructured":"Lundberg, S.M., Lee, S.: A unified approach to interpreting model predictions. In: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4\u20139, 2017, Long Beach, CA, USA, pp. 4765\u20134774 (2017)"},{"key":"25_CR11","doi-asserted-by":"publisher","unstructured":"Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirband, S., Varkonyi-Koczy, A.R.: State of the art of machine learning models in energy systems, a systematic review. Energies 12(7) (2019). https:\/\/doi.org\/10.3390\/en12071301","DOI":"10.3390\/en12071301"},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"Pereira, P.J., Costa, N., Barros, M., Cortez, P., Dur\u00e3es, D., Silva, A., Machado, J.: A comparison of automated time series forecasting tools for smart cities. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds.) Progress in Artificial Intelligence - 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31 - September 2, 2022, Proceedings. Lecture Notes in Computer Science, vol. 13566, pp. 551\u2013562. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16474-3_45","DOI":"10.1007\/978-3-031-16474-3_45"},{"key":"25_CR13","doi-asserted-by":"publisher","unstructured":"Pereira, P.J., Gon\u00e7alves, C., Nunes, L.L., Cortez, P., Pilastri, A.: AI4CITY - An Automated Machine Learning Platform for Smart Cities. In: SAC \u201923: The 38th ACM\/SIGAPP Symposium on Applied Computing, Tallinn, Estonia, March 27\u201331, 2023, pp. 886\u2013889. ACM (2023). https:\/\/doi.org\/10.1145\/3555776.3578740","DOI":"10.1145\/3555776.3578740"},{"key":"25_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.121082","volume":"260","author":"AD Pham","year":"2020","unstructured":"Pham, A.D., Ngo, N.T., Truong, T.T.H., Huynh, N.T., Truong, N.S.: Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability. J. Clean. Prod. 260, 121082 (2020)","journal-title":"J. Clean. Prod."},{"key":"25_CR15","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1016\/j.apenergy.2017.09.060","volume":"208","author":"C Robinson","year":"2017","unstructured":"Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M.A., Pendyala, R.M.: Machine learning approaches for estimating commercial building energy consumption. Appl. Energy 208, 889\u2013904 (2017)","journal-title":"Appl. Energy"},{"key":"25_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40327-018-0064-7","volume":"6","author":"S Seyedzadeh","year":"2018","unstructured":"Seyedzadeh, S., Rahimian, F.P., Glesk, I., Roper, M.: Machine learning for estimation of building energy consumption and performance: a review. Vis. Eng. 6, 1\u201320 (2018)","journal-title":"Vis. Eng."},{"key":"25_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.dibe.2020.100037","volume":"5","author":"MKM Shapi","year":"2021","unstructured":"Shapi, M.K.M., Ramli, N.A., Awalin, L.J.: Energy consumption prediction by using machine learning for smart building: case study in Malaysia. Dev. Built Environ. 5, 100037 (2021)","journal-title":"Dev. Built Environ."},{"issue":"4","key":"25_CR18","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/S0169-2070(00)00065-0","volume":"16","author":"LJ Tashman","year":"2000","unstructured":"Tashman, L.J.: Out-of-sample tests of forecasting accuracy: an analysis and review. Int. J. Forecast. 16(4), 437\u2013450 (2000)","journal-title":"Int. J. Forecast."},{"key":"25_CR19","doi-asserted-by":"publisher","unstructured":"Wang, C., B\u00e4ck, T., Hoos, H.H., Baratchi, M., Limmer, S., Olhofer, M.: Automated machine learning for short-term electric load forecasting. In: IEEE Symposium Series on Computational Intelligence, SSCI 2019, Xiamen, China, December 6\u20139, 2019, pp. 314\u2013321. IEEE (2019). https:\/\/doi.org\/10.1109\/SSCI44817.2019.9002839","DOI":"10.1109\/SSCI44817.2019.9002839"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Wu, Z., Chu, W.: Sampling strategy analysis of machine learning models for energy consumption prediction. In: 2021 IEEE 9th International Conference on Smart Energy Grid Engineering (SEGE), pp. 77\u201381. IEEE (2021)","DOI":"10.1109\/SEGE52446.2021.9534987"},{"key":"25_CR21","doi-asserted-by":"publisher","unstructured":"Yakovlev, A., Moghadam, H.F., Moharrer, A., Cai, J., Chavoshi, N., Varadarajan, V., Agrawal, S.R., Karnagel, T., Idicula, S., Jinturkar, S., Agarwal, N.: Oracle automl: A fast and predictive automl pipeline. Proc. VLDB Endow. 13(12), 3166\u20133180 (2020). https:\/\/doi.org\/10.14778\/3415478.3415542","DOI":"10.14778\/3415478.3415542"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49008-8_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T13:17:25Z","timestamp":1702559845000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49008-8_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031490071","9783031490088"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49008-8_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faial Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2023.inesctec.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"163","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":"85","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":"52% - 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":"4","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":"2","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)"}}]}}