{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:30:29Z","timestamp":1762867829048,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030239459"},{"type":"electronic","value":"9783030239466"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-23946-6_1","type":"book-chapter","created":{"date-parts":[[2019,6,25]],"date-time":"2019-06-25T02:03:58Z","timestamp":1561428238000},"page":"5-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Energy Consumption Forecasting Using Ensemble Learning Algorithms"],"prefix":"10.1007","author":[{"given":"Jose","family":"Silva","sequence":"first","affiliation":[]},{"given":"Isabel","family":"Pra\u00e7a","sequence":"additional","affiliation":[]},{"given":"Tiago","family":"Pinto","sequence":"additional","affiliation":[]},{"given":"Zita","family":"Vale","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"1_CR1","doi-asserted-by":"publisher","unstructured":"Zhang, X., Wang, J., Zhang, K.: Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm. Electr. Power Syst. Res. 146, 270\u2013285 (2017). https:\/\/doi.org\/10.1016\/j.epsr.2017.01.035","DOI":"10.1016\/j.epsr.2017.01.035"},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1016\/j.rser.2015.04.065","volume":"50","author":"MQ Raza","year":"2015","unstructured":"Raza, M.Q., Khosravi, A.: A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sustain. Energy Rev. 50, 1352\u20131372 (2015). https:\/\/doi.org\/10.1016\/j.rser.2015.04.065","journal-title":"Renew. Sustain. Energy Rev."},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Saber, A.Y., Alam, A.K.M.R.: Short term load forecasting using multiple linear regression for big data. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1\u20136 (2017)","DOI":"10.1109\/SSCI.2017.8285261"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Pinto, T., Sousa, T.M., Vale, Z.: Dynamic artificial neural network for electricity market prices forecast. In: 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), pp. 311\u2013316 (2012)","DOI":"10.1109\/INES.2012.6249850"},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Pinto, T., Sousa, T.M., Pra\u00e7a, I., et al.: Support Vector Machines for decision support in electricity markets\u2019 strategic bidding. Neurocomputing 172, 438\u2013445 (2016). https:\/\/doi.org\/10.1016\/j.neucom.2015.03.102","DOI":"10.1016\/j.neucom.2015.03.102"},{"key":"1_CR6","doi-asserted-by":"publisher","unstructured":"Ahmad, T., Chen, H.: Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems. Sustain Cities Soc. 45, 460\u2013473 (2019). https:\/\/doi.org\/10.1016\/j.scs.2018.12.013","DOI":"10.1016\/j.scs.2018.12.013"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Touzani, S., Granderson, J., Fernandes, S.: Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy Build 158, 1533\u20131543 (2018). https:\/\/doi.org\/10.1016\/j.enbuild.2017.11.039","DOI":"10.1016\/j.enbuild.2017.11.039"},{"key":"1_CR8","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.renene.2014.09.058","volume":"75","author":"GJ Os\u00f3rio","year":"2015","unstructured":"Os\u00f3rio, G.J., Matias, J.C.O., Catal\u00e3o, J.P.S.: Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information. Renew. Energy 75, 301\u2013307 (2015). https:\/\/doi.org\/10.1016\/j.renene.2014.09.058","journal-title":"Renew. Energy"},{"key":"1_CR9","doi-asserted-by":"publisher","unstructured":"Gou, J., Hou, F., Chen, W., et al.: Improving Wang\u2013Mendel method performance in fuzzy rules generation using the fuzzy C-means clustering algorithm. Neurocomputing 151, 1293\u20131304 (2015). https:\/\/doi.org\/10.1016\/j.neucom.2014.10.077","DOI":"10.1016\/j.neucom.2014.10.077"},{"key":"1_CR10","doi-asserted-by":"publisher","unstructured":"Du, P., Wang, J., Yang, W., Niu, T.: Multi-step ahead forecasting in electrical power system using a hybrid forecasting system. Renew Energy 122, 533\u2013550 (2018). https:\/\/doi.org\/10.1016\/j.renene.2018.01.113","DOI":"10.1016\/j.renene.2018.01.113"},{"key":"1_CR11","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367\u2013378 (2002). https:\/\/doi.org\/10.1016\/S0167-9473(01)00065-2","journal-title":"Comput. Stat. Data Anal."},{"key":"1_CR13","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."},{"key":"1_CR14","unstructured":"Drucker, H.: Improving regressors using boosting techniques. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 107\u2013115. Morgan Kaufmann Publishers Inc., San Francisco (1997)"},{"key":"1_CR15","doi-asserted-by":"publisher","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119\u2013139 (1997). https:\/\/doi.org\/10.1006\/jcss.1997.1504","DOI":"10.1006\/jcss.1997.1504"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Jozi, A., Pinto, T., Pra\u00e7a, I., Vale, Z.: Day-ahead forecasting approach for energy consumption of an office building using support vector machines. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1620\u20131625 (2018)","DOI":"10.1109\/SSCI.2018.8628734"}],"container-title":["Advances in Intelligent Systems and Computing","Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-23946-6_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T15:04:15Z","timestamp":1729177455000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-23946-6_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030239459","9783030239466"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-23946-6_1","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"25 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Distributed Computing and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"\u00c1vila","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":"26 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dcai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dcai-conference.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}