{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:23:12Z","timestamp":1770751392301,"version":"3.50.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319663784","type":"print"},{"value":"9783319663791","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,10,5]],"date-time":"2017-10-05T00:00:00Z","timestamp":1507161600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-66379-1_16","type":"book-chapter","created":{"date-parts":[[2017,10,4]],"date-time":"2017-10-04T08:08:21Z","timestamp":1507104501000},"page":"175-184","source":"Crossref","is-referenced-by-count":5,"title":["Wind Power Production Forecasting Using Ant Colony Optimization and Extreme Learning Machines"],"prefix":"10.1007","author":[{"given":"Maria","family":"Carrillo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Del Ser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miren","family":"Nekane Bilbao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristina","family":"Perfecto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Camacho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,10,5]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Farhangi, H.: The path of the smart grid. IEEE Power Energy Mag. 8(1) (2010)","DOI":"10.1109\/MPE.2009.934876"},{"issue":"7","key":"16_CR2","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1016\/j.apenergy.2011.01.042","volume":"88","author":"M Wissner","year":"2011","unstructured":"Wissner, M.: The smart grid-a saucerful of secrets? Appl. Energy 88(7), 2509\u20132518 (2011)","journal-title":"Appl. Energy"},{"key":"16_CR3","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1016\/j.rser.2016.10.038","volume":"72","author":"KSR Murthy","year":"2017","unstructured":"Murthy, K.S.R., Rahi, O.P.: A comprehensive review of wind resource assessment. Renew. Sustain. Energy Rev. 72, 1320\u20131342 (2017)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Kaur, T., Kumar, S., Segal, R.: Application of artificial neural network for short term wind speed forecasting. In: International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE), pp. 1\u20135 (2016)","DOI":"10.1109\/PESTSE.2016.7516458"},{"key":"16_CR5","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.rser.2015.04.166","volume":"49","author":"R Ata","year":"2015","unstructured":"Ata, R.: Artificial neural networks applications in wind energy systems: a review. Renew. Sustain. Energy Rev. 49, 534\u2013562 (2015)","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"1","key":"16_CR6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0038-092X(98)00032-2","volume":"63","author":"MC Alexiadis","year":"1998","unstructured":"Alexiadis, M.C., Dokopoulos, P.S., Sahsamanoglou, H.S., Manousaridis, I.M.: Short-term forecasting of wind speed and related electrical power. Solar Energy 63(1), 61\u201368 (1998)","journal-title":"Solar Energy"},{"issue":"6","key":"16_CR7","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.renene.2003.11.009","volume":"29","author":"MA Mohandes","year":"2004","unstructured":"Mohandes, M.A., Halawani, T.O., Rehman, S., Hussain, A.A.: Support vector machines for wind speed prediction. Renew. Energy 29(6), 939\u2013947 (2004)","journal-title":"Renew. Energy"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Zhao, P., Xia, J., Dai, Y., He, J.: Wind speed prediction using support vector regression. In: 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 882\u2013886 (2010)","DOI":"10.1109\/ICIEA.2010.5515626"},{"key":"16_CR9","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.renene.2015.03.071","volume":"81","author":"A Troncoso","year":"2015","unstructured":"Troncoso, A., Salcedo-Sanz, S., Casanova-Mateo, C., Riquelme, J.C., Prieto, L.: Local models-based regression trees for very short-term wind speed prediction. Renew. Energy 81, 589\u2013598 (2015)","journal-title":"Renew. Energy"},{"key":"16_CR10","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.renene.2015.11.073","volume":"89","author":"J Heinermann","year":"2016","unstructured":"Heinermann, J., Kramer, O.: Machine learning ensembles for wind power prediction. Renew. Energy 89, 671\u2013679 (2016)","journal-title":"Renew. Energy"},{"key":"16_CR11","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.renene.2015.06.034","volume":"85","author":"Q Hu","year":"2016","unstructured":"Hu, Q., Zhang, R., Zhou, Y.: Transfer learning for short-term wind speed prediction with deep neural networks. Renew. Energy 85, 83\u201395 (2016)","journal-title":"Renew. Energy"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Dalto, M., Matu\u0161ko, J., Va\u0161ak, M.: Deep neural networks for ultra-short-term wind forecasting. In: IEEE International Conference on Industrial Technology (ICIT), pp. 1657\u20131663 (2015)","DOI":"10.1109\/ICIT.2015.7125335"},{"issue":"4","key":"16_CR13","doi-asserted-by":"crossref","first-page":"4052","DOI":"10.1016\/j.eswa.2010.09.067","volume":"38","author":"S Salcedo-Sanz","year":"2011","unstructured":"Salcedo-Sanz, S., Ortiz-Garcia, E.G., Perez-Bellido, A.M., Portilla-Figueras, A., Prieto, L.: Short term wind speed prediction based on evolutionary support vector regression algorithms. Expert Syst. Appl. 38(4), 4052\u20134057 (2011)","journal-title":"Expert Syst. Appl."},{"key":"16_CR14","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.renene.2013.08.011","volume":"62","author":"D Liu","year":"2014","unstructured":"Liu, D., Niu, D., Wang, H., Fan, L.: Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew. Energy 62, 592\u2013597 (2014)","journal-title":"Renew. Energy"},{"issue":"4","key":"16_CR15","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1016\/j.ijforecast.2008.08.007","volume":"24","author":"R Jursa","year":"2008","unstructured":"Jursa, R., Rohrig, K.: Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models. Int. J. Forecast. 24(4), 694\u2013709 (2008)","journal-title":"Int. J. Forecast."},{"key":"16_CR16","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.enconman.2014.06.041","volume":"87","author":"S Salcedo-Sanz","year":"2014","unstructured":"Salcedo-Sanz, S., Pastor-Sanchez, A., Prieto, L., Blanco-Aguilera, A., Garcia-Herrera, R.: Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization Extreme learning machine approach. Energy Convers. Manag. 87, 10\u201318 (2014)","journal-title":"Energy Convers. Manag."},{"key":"16_CR17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.renene.2014.09.027","volume":"75","author":"S Salcedo-Sanz","year":"2015","unstructured":"Salcedo-Sanz, S., Pastor-Sanchez, A., Del Ser, J., Prieto, L., Geem, Z.W.: A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction. Renew. Energy 75, 93\u2013101 (2015)","journal-title":"Renew. Energy"},{"issue":"1","key":"16_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2011.05.033","volume":"37","author":"AM Foley","year":"2012","unstructured":"Foley, A.M., Leahy, P.G., Marvuglia, A., McKeogh, E.J.: Current methods and advances in forecasting of wind power generation. Renew. Energy 37(1), 1\u20138 (2012)","journal-title":"Renew. Energy"},{"key":"16_CR19","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.renene.2012.02.015","volume":"46","author":"I Colak","year":"2012","unstructured":"Colak, I., Sagiroglu, S., Yesilbudak, M.: Data mining and wind power prediction: A literature review. Renew. Energy 46, 241\u2013247 (2012)","journal-title":"Renew. Energy"},{"key":"16_CR20","unstructured":"Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M., Draxl, C.: The state-of-the-art in short-term prediction of wind power: A literature overview. ANEMOS. plus (2011)"},{"issue":"4","key":"16_CR21","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.rser.2008.02.002","volume":"13","author":"M Lei","year":"2009","unstructured":"Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., Yan, Z.: A review on the forecasting of wind speed and generated power. Renew. Sustain. Energy Rev. 13(4), 915\u2013920 (2009)","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"1","key":"16_CR22","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489\u2013501 (2006)","journal-title":"Neurocomputing"},{"issue":"4","key":"16_CR23","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo, M., Birattari, M., Stutzle, T.: Ant Colony Optimization. IEEE Comput. Intell. Mag. 1(4), 28\u201339 (2006)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Gonzalez-Pardo, A., Camacho, D.: A new csp graph-based representation for ant colony optimization. In: IEEE Congress on Evolutionary Computation, pp. 689\u2013696 (2013)","DOI":"10.1109\/CEC.2013.6557635"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Gonzalez-Pardo, A., Camacho, D.: A new csp graph-based representation to resource-constrained project scheduling problem. In: IEEE Congress on Evolutionary Computation (CEC), pp. 344\u2013351 (2014)","DOI":"10.1109\/CEC.2014.6900543"},{"key":"16_CR26","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.future.2016.06.033","volume":"66","author":"A Gonzalez-Pardo","year":"2017","unstructured":"Gonzalez-Pardo, A., Jung, J.J., Camacho, D.: ACO-based clustering for Ego Network analysis. Future Gener. Comput. Syst. 66, 160\u2013170 (2017)","journal-title":"Future Gener. Comput. Syst."}],"container-title":["Studies in Computational Intelligence","Intelligent Distributed Computing XI"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-66379-1_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,10,4]],"date-time":"2017-10-04T08:13:47Z","timestamp":1507104827000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-66379-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,5]]},"ISBN":["9783319663784","9783319663791"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-66379-1_16","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"value":"1860-949X","type":"print"},{"value":"1860-9503","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,5]]}}}