{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T19:00:22Z","timestamp":1759777222856,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030616083"},{"type":"electronic","value":"9783030616090"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-61609-0_22","type":"book-chapter","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T19:02:59Z","timestamp":1603134179000},"page":"271-283","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-learning"],"prefix":"10.1007","author":[{"given":"Yang","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irena","family":"Koprinska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mashud","family":"Rana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alicia","family":"Troncoso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.3390\/en12061083","volume":"12","author":"\u00d3 Trull","year":"2019","unstructured":"Trull, \u00d3., Garc\u00eda-D\u00edaz, J.C., Troncoso, A.: Application of discrete-interval moving seasonalities to Spanish electricity demand forecasting during Easter. Energies 12, 1083 (2019)","journal-title":"Energies"},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1016\/j.solener.2012.04.004","volume":"86","author":"HT Pedro","year":"2012","unstructured":"Pedro, H.T., Coimbra, C.F.: Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol. Energy 86, 2017\u20132028 (2012)","journal-title":"Sol. Energy"},{"key":"22_CR3","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.solener.2014.11.017","volume":"112","author":"Y Chu","year":"2015","unstructured":"Chu, Y., Urquhart, B., Gohari, S.M., Pedro, H.T., Kleissl, J., Coimbra, C.F.: Short-term reforecasting of power output from a 48 MWe solar PV plant. Sol. Energy 112, 68\u201377 (2015)","journal-title":"Sol. Energy"},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.pecs.2013.06.002","volume":"39","author":"RH Inman","year":"2013","unstructured":"Inman, R.H., Pedro, H.T., Coimbra, C.F.: Solar forecasting methods for renewable energy integration. Prog. Energy Combust. Sci. 39, 535\u2013576 (2013)","journal-title":"Prog. Energy Combust. Sci."},{"doi-asserted-by":"crossref","unstructured":"Rana, M., Koprinska, I., Agelidis, V.: Forecasting solar power generated by grid connected PV systems using ensembles of neural networks. In: International Joint Conference on Neural Networks (IJCNN) (2015)","key":"22_CR5","DOI":"10.1109\/IJCNN.2015.7280574"},{"key":"22_CR6","doi-asserted-by":"publisher","first-page":"2856","DOI":"10.1016\/j.solener.2011.08.027","volume":"85","author":"C Chen","year":"2011","unstructured":"Chen, C., Duan, S., Cai, T., Liu, B.: Online 24-h solar power forecasting based on weather type classification using artificial neural networks. Sol. Energy 85, 2856\u20132870 (2011)","journal-title":"Sol. Energy"},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.solener.2015.08.018","volume":"122","author":"M Rana","year":"2015","unstructured":"Rana, M., Koprinska, I., Agelidis, V.G.: 2D-interval forecasts for solar power production. Sol. Energy 122, 191\u2013203 (2015)","journal-title":"Sol. Energy"},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1109\/TKDE.2010.227","volume":"23","author":"F Mart\u00ednez-\u00c1lvarez","year":"2011","unstructured":"Mart\u00ednez-\u00c1lvarez, F., Troncoso, A., Riquelme, J.C., Aguilar Ruiz, J.S.: Energy time series forecasting based on pattern sequence similarity. IEEE Trans. Knowl. Data Eng. 23, 1230\u20131243 (2011)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"doi-asserted-by":"crossref","unstructured":"Wang, Z., Koprinska, I., Rana, M.: Solar power forecasting using pattern sequences. In: Proceedings of the International Conference on Artificial Neural Networks (ICANN) (2017)","key":"22_CR9","DOI":"10.1007\/978-3-319-68612-7_55"},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"e12394","DOI":"10.1111\/exsy.12394","volume":"36","author":"J Torres","year":"2019","unstructured":"Torres, J., Troncoso, A., Koprinska, I., Wang, Z., Mart\u00ednez-\u00c1lvarez, F.: Big data solar power forecasting based on deep learning and multiple data sources. Expert Syst. 36, e12394 (2019)","journal-title":"Expert Syst."},{"doi-asserted-by":"crossref","unstructured":"Lin, Y., Koprinska, I., Rana, M., Troncoso, A.: Pattern sequence neural network for solar power forecasting. In: Proceedings of the International Conference on Neural Information Processing (ICONIP) (2019)","key":"22_CR11","DOI":"10.1007\/978-3-030-36802-9_77"},{"doi-asserted-by":"crossref","unstructured":"Wang, Z., Koprinska, I.: Solar power forecasting using dynamic meta-learning ensemble of neural networks. In: Proceedings of the International Conference on Artificial Neural Networks and Machine Learning (ICANN) (2018)","key":"22_CR12","DOI":"10.1007\/978-3-030-01418-6_52"},{"doi-asserted-by":"crossref","unstructured":"Wang, Z., Koprinska, I.: Solar power prediction with data source weighted nearest neighbors. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN) (2017)","key":"22_CR13","DOI":"10.1109\/IJCNN.2017.7966018"},{"doi-asserted-by":"crossref","unstructured":"Cerqueira, V., Torgo, L., Pinto, F., Soares, C.: Arbitrated ensembles for time series forecasting. In: Proceedings of the European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECML-PKDD) (2017)","key":"22_CR14","DOI":"10.1007\/978-3-319-71246-8_29"},{"key":"22_CR15","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1016\/j.knosys.2018.10.009","volume":"163","author":"A Galicia","year":"2019","unstructured":"Galicia, A., Talavera-Llames, R., Troncoso, A., Koprinska, I., Mart\u00ednez-\u00c1lvarez, F.: Multi-step forecasting for big data time series based on ensemble learning. Knowl.-Based Syst. 163, 830\u2013841 (2019)","journal-title":"Knowl.-Based Syst."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61609-0_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T21:52:52Z","timestamp":1619301172000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61609-0_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030616083","9783030616090"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61609-0_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2020\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"249","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":"139","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":"56% - 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":"2.5","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":"*The conference was postponed to 2021 due to the COVID-19 pandemic.","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)"}}]}}