{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:02:10Z","timestamp":1743022930320,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030539559"},{"type":"electronic","value":"9783030539566"}],"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\/978-3-030-53956-6_3","type":"book-chapter","created":{"date-parts":[[2020,7,12]],"date-time":"2020-07-12T11:02:42Z","timestamp":1594551762000},"page":"25-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Prediction of Photovoltaic Power Using Nature-Inspired Computing"],"prefix":"10.1007","author":[{"given":"Miroslav","family":"Sumega","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Bou Ezzeddine","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriela","family":"Grmanov\u00e1","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viera","family":"Rozinajov\u00e1","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/J.SOLENER.2016.06.069","volume":"136","author":"J Antonanzas","year":"2016","unstructured":"Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de Pison, F., Antonanzas-Torres, F.: Review of photovoltaic power forecasting. Solar Energy 136, 78\u2013111 (2016). https:\/\/doi.org\/10.1016\/J.SOLENER.2016.06.069","journal-title":"Solar Energy"},{"issue":"11","key":"3_CR2","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 network. Solar Energy 85(11), 2856\u20132870 (2011). https:\/\/doi.org\/10.1016\/J.SOLENER.2011.08.027","journal-title":"Solar Energy"},{"issue":"8","key":"3_CR3","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1007\/s00521-015-1842-y","volume":"26","author":"R De Leone","year":"2015","unstructured":"De Leone, R., Pietrini, M., Giovannelli, A.: Photovoltaic energy production forecast using support vector regression. Neural Comput. Appl. 26(8), 1955\u20131962 (2015). https:\/\/doi.org\/10.1007\/s00521-015-1842-y","journal-title":"Neural Comput. Appl."},{"key":"3_CR4","unstructured":"Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, pp. 155\u2013161 (1997)"},{"key":"3_CR5","doi-asserted-by":"publisher","unstructured":"Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep learning for solar power forecasting - an approach using AutoEncoder and LSTM neural networks. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics SMC 2016 - Conference Proceedings, pp. 2858\u20132865. IEEE, October 2017. https:\/\/doi.org\/10.1109\/SMC.2016.7844673","DOI":"10.1109\/SMC.2016.7844673"},{"issue":"01","key":"3_CR6","doi-asserted-by":"publisher","first-page":"76","DOI":"10.4236\/sgre.2013.41011","volume":"04","author":"MR Hossain","year":"2013","unstructured":"Hossain, M.R., Oo, A.M.T., Ali, A.B.M.S.: Hybrid prediction method for solar power using different computational intelligence algorithms. Smart Grid Renew. Energy 04(01), 76\u201387 (2013). https:\/\/doi.org\/10.4236\/sgre.2013.41011","journal-title":"Smart Grid Renew. Energy"},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.jclepro.2017.08.081","volume":"167","author":"M Hossain","year":"2018","unstructured":"Hossain, M., Mekhilef, S., Danesh, M., Olatomiwa, L., Shamshirband, S.: Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. J. Clean. Prod. 167, 395\u2013405 (2018). https:\/\/doi.org\/10.1016\/j.jclepro.2017.08.081","journal-title":"J. Clean. Prod."},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.apenergy.2016.07.052","volume":"180","author":"Y Li","year":"2016","unstructured":"Li, Y., He, Y., Su, Y., Shu, L.: Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines. Appl. Energy 180, 392\u2013401 (2016). https:\/\/doi.org\/10.1016\/j.apenergy.2016.07.052","journal-title":"Appl. Energy"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.apenergy.2014.03.084","volume":"126","author":"H Long","year":"2014","unstructured":"Long, H., Zhang, Z., Su, Y.: Analysis of daily solar power prediction with data-driven approaches. Appl. Energy 126, 29\u201337 (2014). https:\/\/doi.org\/10.1016\/j.apenergy.2014.03.084","journal-title":"Appl. Energy"},{"key":"3_CR10","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1016\/j.procs.2017.09.143","volume":"115","author":"R Nageem","year":"2017","unstructured":"Nageem, R., Jayabarathi, R.: Predicting the power output of a grid-connected solar panel using multi-input support vector regression. Procedia Comput. Sci. 115, 723\u2013730 (2017). https:\/\/doi.org\/10.1016\/j.procs.2017.09.143","journal-title":"Procedia Comput. Sci."},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/J.ENCONMAN.2016.05.025","volume":"121","author":"M Rana","year":"2016","unstructured":"Rana, M., Koprinska, I., Agelidis, V.G.: Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Convers. Manag. 121, 380\u2013390 (2016). https:\/\/doi.org\/10.1016\/J.ENCONMAN.2016.05.025","journal-title":"Energy Convers. Manag."},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Shi, J., Lee, W.J., Liu, Y., Yang, Y., Wang, P.: Forecasting power output of photovoltaic systems based on weather classification and support vector machines. In: IEEE Transactions on Industry Applications, vol. 48, pp. 1064\u20131069, October 2012. https:\/\/doi.org\/10.1109\/TIA.2012.2190816","DOI":"10.1109\/TIA.2012.2190816"},{"key":"3_CR13","doi-asserted-by":"publisher","unstructured":"Sun, X., Zhang, T.: Solar power prediction in smart grid based on NWP data and an improved boosting method. In: 2017 IEEE International Conference on Energy Internet (ICEI), pp. 89\u201394. IEEE, April 2017. https:\/\/doi.org\/10.1109\/ICEI.2017.23","DOI":"10.1109\/ICEI.2017.23"},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Theocharides, S., Makrides, G., Georghiou, G.E., Kyprianou, A.: Machine learning algorithms for photovoltaic system power output prediction. In: 2018 IEEE International Energy Conference (ENERGYCON), pp. 1\u20136. IEEE, June 2018. https:\/\/doi.org\/10.1109\/ENERGYCON.2018.8398737","DOI":"10.1109\/ENERGYCON.2018.8398737"},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.solener.2016.05.051","volume":"135","author":"B Wolff","year":"2016","unstructured":"Wolff, B., K\u00fchnert, J., Lorenz, E., Kramer, O., Heinemann, D.: Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data. Solar Energy 135, 197\u2013208 (2016). https:\/\/doi.org\/10.1016\/j.solener.2016.05.051","journal-title":"Solar Energy"},{"issue":"3","key":"3_CR16","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1109\/TSTE.2014.2313600","volume":"5","author":"HT Yang","year":"2014","unstructured":"Yang, H.T., Huang, C.M., Huang, Y.C., Pai, Y.S.: A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output. IEEE Trans. Sustain. Energy 5(3), 917\u2013926 (2014). https:\/\/doi.org\/10.1109\/TSTE.2014.2313600","journal-title":"IEEE Trans. Sustain. Energy"},{"issue":"2","key":"3_CR17","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","volume":"2","author":"X Yang","year":"2010","unstructured":"Yang, X.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78\u201384 (2010)","journal-title":"Int. J. Bio-Inspired Comput."}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-53956-6_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,28]],"date-time":"2020-12-28T08:31:35Z","timestamp":1609144295000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-53956-6_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030539559","9783030539566"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-53956-6_3","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":"13 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgrade","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Serbia","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":"14 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-si.org\/committees\/","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":"Confy","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"127","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":"63","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":"50% - 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","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":"2.4","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":"No","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 held virtually 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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}