{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T09:42:27Z","timestamp":1778319747434,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2018,7,28]],"date-time":"2018-07-28T00:00:00Z","timestamp":1532736000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,7,28]],"date-time":"2018-07-28T00:00:00Z","timestamp":1532736000000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s00521-018-3628-5","type":"journal-article","created":{"date-parts":[[2018,7,28]],"date-time":"2018-07-28T14:08:22Z","timestamp":1532786902000},"page":"15825-15833","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Comparing different solutions for forecasting the energy production of a wind farm"],"prefix":"10.1007","volume":"32","author":[{"given":"Dar\u00edo","family":"Baptista","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo\u00e3o Paulo","family":"Carvalho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernando","family":"Morgado-Dias","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,7,28]]},"reference":[{"issue":"1","key":"3628_CR1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.ijforecast.2005.05.003","volume":"22","author":"I S\u00e1nchez","year":"2006","unstructured":"S\u00e1nchez I (2006) Short-term prediction of wind energy production. Int J Forecast 22(1):43\u201356","journal-title":"Int J Forecast"},{"issue":"1","key":"3628_CR2","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1109\/TEC.2005.847954","volume":"21","author":"TG Barbounis","year":"2006","unstructured":"Barbounis TG, Theocharis JB, Alexiadis MC, Dokopoulos PS (2006) Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans Energy Convers 21(1):273\u2013284","journal-title":"IEEE Trans Energy Convers"},{"issue":"4","key":"3628_CR3","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1115\/1.1413216","volume":"123","author":"S Li","year":"2001","unstructured":"Li S, Wunsch DC, O\u2019Hair E, Giesselmann MG (2001) Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation. J Solar Energy Eng Trans ASME 123(4):327\u2013332","journal-title":"J Solar Energy Eng Trans ASME"},{"issue":"1","key":"3628_CR4","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.solener.2004.09.013","volume":"79","author":"JL Torres","year":"2005","unstructured":"Torres JL, Garc\u00eda A, De Blas M, De Francisco A (2005) Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Sol Energy 79(1):65\u201377","journal-title":"Sol Energy"},{"issue":"2","key":"3628_CR5","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1109\/TEC.2003.821865","volume":"19","author":"IG Damousis","year":"2004","unstructured":"Damousis IG, Alexiadis MC, Theocharis JB, Dokopoulos PS (2004) A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Trans Energy Convers 19(2):352\u2013361","journal-title":"IEEE Trans Energy Convers"},{"key":"3628_CR6","unstructured":"Tande JO, Lanberg L (1993) A 10\u00a0s forecast of wind turbine output with neural. In: Proceedings of the 1993 ECWEC, pp 774\u2013777"},{"key":"3628_CR7","unstructured":"Fernandes F, Baptista D, Jardim B, Figueira A, Morgado-Dias D (2012) In: 10th portuguese conference on automatic control\u2014CONTROLO\u201912. LOIRAL wind park modelling using artificial neural network"},{"issue":"6","key":"3628_CR8","first-page":"287","volume":"22","author":"DA Bechrakis","year":"1998","unstructured":"Bechrakis DA, Sparis PD (1998) Wind speed prediction using artificial neural networks. Wind Eng 22(6):287\u2013295","journal-title":"Wind Eng"},{"issue":"2","key":"3628_CR9","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/S0960-1481(01)00193-8","volume":"27","author":"A Sfetsos","year":"2002","unstructured":"Sfetsos A (2002) A novel approach for the forecasting of mean hourly wind speed time series. Renew Energy 27(2):163\u2013174","journal-title":"Renew Energy"},{"issue":"2","key":"3628_CR10","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1109\/TSTE.2011.2180029","volume":"3","author":"J Zeng","year":"2012","unstructured":"Zeng J, Qiao W (2012) Short-term wind power prediction using a wavelet support vector machine. IEEE Trans Sustain Energy 3(2):255\u2013264","journal-title":"IEEE Trans Sustain Energy"},{"issue":"1","key":"3628_CR11","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1109\/TEC.2008.2006552","volume":"24","author":"A Kusiak","year":"2009","unstructured":"Kusiak A, Zheng H, Song Z (2009) Short-term prediction of wind farm power: a data mining approach. IEEE Trans Energy Convers 24(1):125\u2013136","journal-title":"IEEE Trans Energy Convers"},{"key":"3628_CR12","doi-asserted-by":"crossref","unstructured":"Wu L, Park J, Choi J, Cha J, Lee KY (2009) A study on wind speed prediction using artificial neural network at Jeju Island in Korea. In: 2009 transmission and distribution conference and exposition: Asia and Pacific, pp 1\u20134","DOI":"10.1109\/TD-ASIA.2009.5356873"},{"key":"3628_CR13","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.asoc.2017.01.033","volume":"54","author":"X Ma","year":"2017","unstructured":"Ma X, Jin Y, Dong Q (2017) A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting. Appl Soft Comput J 54:296\u2013312","journal-title":"Appl Soft Comput J"},{"key":"3628_CR14","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1016\/j.renene.2015.07.004","volume":"85","author":"G Santamar\u00eda-Bonfil","year":"2016","unstructured":"Santamar\u00eda-Bonfil G, Reyes-Ballesteros A, Gershenson C (2016) Wind speed forecasting for wind farms: a method based on support vector regression. Renew Energy 85:790\u2013809","journal-title":"Renew Energy"},{"issue":"9","key":"3628_CR15","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.3390\/en10091422","volume":"10","author":"H Yang","year":"2017","unstructured":"Yang H, Jiang Z, Lu H (2017) A hybrid wind speed forecasting system based on a \u2018decomposition and ensemble\u2019 strategy and fuzzy time series. Energies 10(9):1422","journal-title":"Energies"},{"key":"3628_CR16","unstructured":"Engasoft \u201csotaventogalicia\u201d (2005) http:\/\/www.sotaventogalicia.com\/ . Accessed 10 Jun 2016"},{"key":"3628_CR17","doi-asserted-by":"publisher","DOI":"10.1115\/1.802601","volume-title":"Wind turbine technology: fundamental concepts in wind turbine engineering","author":"DA Spera","year":"2009","unstructured":"Spera DA (2009) Wind turbine technology: fundamental concepts in wind turbine engineering, 2nd edn. American Society of Mechanical Engineers (ASME), New York"},{"key":"3628_CR18","volume-title":"Research methods and statistics in psychology","author":"H Coolican","year":"1999","unstructured":"Coolican H (1999) Research methods and statistics in psychology. Hodder & Stoughton, London"},{"issue":"318","key":"3628_CR19","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1080\/01621459.1967.10482916","volume":"62","author":"HW Lilliefors","year":"1967","unstructured":"Lilliefors HW (1967) On the Kolmogorov\u2013Smirnov test for normality with mean and variance unkown. J Am Stat Assoc 62(318):399\u2013402","journal-title":"J Am Stat Assoc"},{"issue":"4","key":"3628_CR20","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1080\/00031305.1986.10475419","volume":"40","author":"GE Dallal","year":"1986","unstructured":"Dallal GE, Wilkinson L (1986) An analytic approximation to the distribution of Lilliefors\u2019s test statistic for normality. Am Stat 40(4):294\u2013296","journal-title":"Am Stat"},{"issue":"7","key":"3628_CR21","first-page":"459","volume":"111","author":"C Nangolo","year":"2011","unstructured":"Nangolo C, Musingwini C (2011) Empirical correlation of mineral commodity prices with exchange-traded mining stock prices. J S Afr Inst Min Metall 111(7):459\u2013468","journal-title":"J S Afr Inst Min Metall"},{"issue":"3","key":"3628_CR22","doi-asserted-by":"publisher","first-page":"377","DOI":"10.2307\/2347111","volume":"24","author":"DJ Best","year":"1975","unstructured":"Best DJ, Roberts DE (1975) Algorithm AS 89: the upper tail probabilities of Spearman\u2019s rho. Appl Stat 24(3):377","journal-title":"Appl Stat"},{"key":"3628_CR23","first-page":"2520","volume":"1993","author":"X He","year":"1993","unstructured":"He X, Asada H (1993) A new method for identifying orders from input\u2013output models for nonlinear dynamic systems. Am Control Conf 1993:2520\u20132523","journal-title":"Am Control Conf"},{"key":"3628_CR24","doi-asserted-by":"publisher","first-page":"161","DOI":"10.4236\/jpee.2014.24023","volume":"2","author":"W Chang","year":"2014","unstructured":"Chang W (2014) A literature review of wind forecasting methods. J Power Energy Eng 2:161\u2013168","journal-title":"J Power Energy Eng"},{"key":"3628_CR25","unstructured":"Carvalho JP, Camelo FV (2015) One day ahead stream flow forecasting. In: 16th world congress of the International Fuzzy Systems Association (IFSA) and the 9th conference of the European Society for fuzzy logic and technology (EUSFLAT), IFSA- EUSFLAT2015, vol 89, pp 1168\u20131175"},{"issue":"3","key":"3628_CR26","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola AJ, Sch\u00f6lkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199\u2013222","journal-title":"Stat Comput"},{"key":"3628_CR27","first-page":"1889","volume":"6","author":"R-E Fan","year":"2005","unstructured":"Fan R-E, Chen P-H, Lin C-J (2005) Working set selection using second order information for training support vector machines. J Mach Learn Res 6:1889\u20131918","journal-title":"J Mach Learn Res"},{"issue":"3","key":"3628_CR28","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3390\/w8030069","volume":"8","author":"F Granata","year":"2016","unstructured":"Granata F, Gargano R, de Marinis G (2016) Support vector regression for rainfall-runoff modeling in urban drainage: a comparison with the EPA\u2019s storm water management model. Water 8(3):69","journal-title":"Water"},{"issue":"1","key":"3628_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.engappai.2005.03.005","volume":"19","author":"FM Dias","year":"2006","unstructured":"Dias FM, Antunes A, Vieira J, Mota A (2006) A sliding window solution for the on-line implementation of the Levenberg\u2013Marquardt algorithm. Eng Appl Artif Intell 19(1):1\u20137","journal-title":"Eng Appl Artif Intell"},{"issue":"6","key":"3628_CR30","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1080\/00207179508921605","volume":"62","author":"J Sjoberg","year":"1995","unstructured":"Sjoberg J, Ljung L (1995) Overtraining, regularization and searching for a minimum, with application to neural networks. Int J Control 62(6):1391\u20131407","journal-title":"Int J Control"},{"key":"3628_CR31","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.micpro.2016.11.003","volume":"49","author":"D Baptista","year":"2017","unstructured":"Baptista D, Abreu S, Travieso-Gonz\u00e1lez C, Morgado-Dias F (2017) Hardware implementation of an artificial neural network model to predict the energy production of a photovoltaic system. Microprocess Microsyst 49:77\u201386","journal-title":"Microprocess Microsyst"},{"issue":"3","key":"3628_CR32","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"JSR Jang","year":"1993","unstructured":"Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665\u2013685","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"3628_CR33","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/11339366_3","volume":"83","author":"A Abraham","year":"2005","unstructured":"Abraham A (2005) Adaptation of fuzzy inference system using neural learning. Fuzzy Syst Eng 83:53\u201383","journal-title":"Fuzzy Syst Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3628-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3628-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3628-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T19:11:58Z","timestamp":1720465918000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3628-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,28]]},"references-count":33,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["3628"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3628-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,28]]},"assertion":[{"value":"5 January 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}