{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:30:11Z","timestamp":1775266211853,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030034955","type":"print"},{"value":"9783030034962","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","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":[[2018]]},"DOI":"10.1007\/978-3-030-03496-2_18","type":"book-chapter","created":{"date-parts":[[2018,11,8]],"date-time":"2018-11-08T07:58:05Z","timestamp":1541663885000},"page":"155-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Studying the Effect of Measured Solar Power on Evolutionary Multi-objective Prediction Intervals"],"prefix":"10.1007","author":[{"given":"R.","family":"Mart\u00edn-V\u00e1zquez","sequence":"first","affiliation":[]},{"given":"J.","family":"Huertas-Tato","sequence":"additional","affiliation":[]},{"given":"R.","family":"Aler","sequence":"additional","affiliation":[]},{"given":"I. M.","family":"Galv\u00e1n","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,9]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.solener.2016.06.073","volume":"136","author":"MQ Raza","year":"2016","unstructured":"Raza, M.Q., Nadarajah, M., Ekanayake, C.: On recent advances in pv output power forecast. Sol. Energy 136, 125\u2013144 (2016)","journal-title":"Sol. Energy"},{"issue":"6","key":"18_CR2","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1002\/we.230","volume":"10","author":"P Pinson","year":"2007","unstructured":"Pinson, P., Nielsen, H.A., M\u00f8ller, J.K., Madsen, H., Kariniotakis, G.N.: Non-parametric probabilistic forecasts of wind power: required properties and evaluation. Wind. Energy 10(6), 497\u2013516 (2007)","journal-title":"Wind. Energy"},{"issue":"3","key":"18_CR3","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/TNN.2010.2096824","volume":"22","author":"A Khosravi","year":"2011","unstructured":"Khosravi, A., Nahavandi, S., Creighton, D., Atiya, A.F.: Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans. Neural Netw. 22(3), 337\u2013346 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"4","key":"18_CR4","doi-asserted-by":"publisher","first-page":"4877","DOI":"10.1109\/TPWRS.2013.2258824","volume":"28","author":"C Wan","year":"2013","unstructured":"Wan, C., Xu, Z., Pinson, P.: Direct interval forecasting of wind power. IEEE Trans. Power Syst. 28(4), 4877\u20134878 (2013)","journal-title":"IEEE Trans. Power Syst."},{"issue":"4","key":"18_CR5","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1109\/TSTE.2013.2253140","volume":"4","author":"A Khosravi","year":"2013","unstructured":"Khosravi, A., Nahavandi, S.: Combined nonparametric prediction intervals for wind power generation. IEEE Trans. Sustain. Energy 4(4), 849\u2013856 (2013)","journal-title":"IEEE Trans. Sustain. Energy"},{"issue":"4598","key":"18_CR6","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1126\/science.220.4598.671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671\u2013680 (1983)","journal-title":"Science"},{"key":"18_CR7","volume-title":"Swarm Intelligence","author":"RC Eberhart","year":"2001","unstructured":"Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence. Elsevier, Amsterdam (2001)"},{"key":"18_CR8","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.ins.2017.08.039","volume":"418","author":"IM Galv\u00e1n","year":"2017","unstructured":"Galv\u00e1n, I.M., Valls, J.M., Cervantes, A., Aler, R.: Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks. Inf. Sci. 418, 363\u2013382 (2017)","journal-title":"Inf. Sci."},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Coello Coello, C.A., Lechuga, M.S.: MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Proceedings of the Evolutionary Computation on CEC 2002, vol. 2, pp. 1051\u20131056. IEEE Computer Society, Washington (2002)","DOI":"10.1109\/CEC.2002.1004388"},{"key":"18_CR10","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1016\/j.renene.2016.06.018","volume":"97","author":"LM Aguiar","year":"2016","unstructured":"Aguiar, L.M., Pereira, B., Lauret, P., D\u00edaz, F., David, M.: Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting. Renew. Energy 97, 599\u2013610 (2016)","journal-title":"Renew. Energy"},{"key":"18_CR11","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. Sol. Energy 135, 197\u2013208 (2016)","journal-title":"Sol. Energy"},{"key":"18_CR12","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-92007-8_21","volume-title":"Artificial Intelligence Applications and Innovations","author":"R Mart\u00edn-V\u00e1zquez","year":"2018","unstructured":"Mart\u00edn-V\u00e1zquez, R., Aler, R., Galv\u00e1n, I.M.: Wind energy forecasting at different time horizons with individual and global models. In: Iliadis, L., Maglogiannis, I., Plagianakos, V. (eds.) AIAI 2018. IAICT, vol. 519, pp. 240\u2013248. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-92007-8_21"},{"key":"18_CR13","series-title":"Econometric Society Monographs","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511754098","volume-title":"Quantile Regression","author":"R Koenker","year":"2005","unstructured":"Koenker, R.: Quantile Regression. Econometric Society Monographs, vol. 38. Cambridge University Press, Cambridge (2005)"},{"key":"18_CR14","unstructured":"Koenker, R.: quantreg: Quantile Regression. R package version 5.36 (2018)"},{"issue":"3","key":"18_CR15","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1016\/j.ijforecast.2016.02.001","volume":"32","author":"T Hong","year":"2016","unstructured":"Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., Hyndman, R.J.: Probabilistic energy forecasting: global energy forecasting competition 2014 and beyond. Int. J. Forecast. 32(3), 896\u2013913 (2016)","journal-title":"Int. J. Forecast."}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-03496-2_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:26:14Z","timestamp":1775262374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-03496-2_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030034955","9783030034962"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-03496-2_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"9 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aida.ii.uam.es\/ideal2018\/#!\/main","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}