{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T18:02:39Z","timestamp":1773338559621,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T00:00:00Z","timestamp":1558656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013276","name":"Interreg","doi-asserted-by":"publisher","award":["MAC\/3.5b\/065. Programa de Cooperaci\u00f3n Territorial. INTERREG V A Espa\u00f1a-Portugal. MAC 2014-2020"],"award-info":[{"award-number":["MAC\/3.5b\/065. Programa de Cooperaci\u00f3n Territorial. INTERREG V A Espa\u00f1a-Portugal. MAC 2014-2020"]}],"id":[{"id":"10.13039\/100013276","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007757","name":"Agencia Canaria de Investigaci\u00f3n, Innovaci\u00f3n y Sociedad de la Informaci\u00f3n","doi-asserted-by":"publisher","award":["Agencia Canaria de Investigaci\\'on, Innovaci\\'on y Sociedad de la Informaci\\'on (ACIISI) de la Consejer\\'ia de Econom\\'ia, Industria, Comercio y Conocimiento and by Fondo Social Europeo (FSE) Programa Operativo Integrado de Canarias 2014-2020, Eje 3 Tema"],"award-info":[{"award-number":["Agencia Canaria de Investigaci\\'on, Innovaci\\'on y Sociedad de la Informaci\\'on (ACIISI) de la Consejer\\'ia de Econom\\'ia, Industria, Comercio y Conocimiento and by Fondo Social Europeo (FSE) Programa Operativo Integrado de Canarias 2014-2020, Eje 3 Tema"]}],"id":[{"id":"10.13039\/501100007757","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, managing climate-change-associated risks. The Spanish National Meteorology Agency (AEMET) has a network of weather stations across the eight Canary Islands. Using data from those stations, we propose a novel methodology for the prediction of maximum wind speed in order to trigger an early alert for extreme weather conditions. The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. This type of machine learning system relies on two important features: models are learned incrementally and adaptively. That means the learner tunes the models gradually and endlessly as new observations are received and also modifies it when there is concept drift (statistical instability), in the modeled phenomenon. The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the methodology.<\/jats:p>","DOI":"10.3390\/s19102388","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T11:20:46Z","timestamp":1558696846000},"page":"2388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2530-3182","authenticated-orcid":false,"given":"Javier J.","family":"S\u00e1nchez-Medina","sequence":"first","affiliation":[{"name":"Centro de Innovaci\u00f3n para la Sociedad de la Informaci\u00f3n (CICEI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-9993","authenticated-orcid":false,"given":"Juan Antonio","family":"Guerra-Montenegro","sequence":"additional","affiliation":[{"name":"Centro de Innovaci\u00f3n para la Sociedad de la Informaci\u00f3n (CICEI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2700-1591","authenticated-orcid":false,"given":"David","family":"S\u00e1nchez-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Instituto Universitario para el Desarrollo Tecnol\u00f3gico y la Innovaci\u00f3n en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8487-2559","authenticated-orcid":false,"given":"Itziar G.","family":"Alonso-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Instituto Universitario para el Desarrollo Tecnol\u00f3gico y la Innovaci\u00f3n en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3860-3424","authenticated-orcid":false,"given":"Juan L.","family":"Navarro-Mesa","sequence":"additional","affiliation":[{"name":"Instituto Universitario para el Desarrollo Tecnol\u00f3gico y la Innovaci\u00f3n en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,24]]},"reference":[{"key":"ref_1","unstructured":"Beven, J. (2006). Tropical Cyclone Report: Tropical Storm Delta, 22\u201328 November 2005, Tropical Prediction Center, National Hurricane Center. NOAA Technical Notes."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1007\/s00024-009-0502-5","article-title":"GPS monitoring of the tropical storm delta along the Canary Islands track, 28\u201329 November 2005","volume":"166","author":"Seco","year":"2009","journal-title":"Pure Appl. Geophys."},{"key":"ref_3","first-page":"60","article-title":"La inusual y an\u00f3mala tormenta tropical \u201cDelta\u201d","volume":"52","year":"2006","journal-title":"Ambienta La revista del Ministerio de Medio Ambiente"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1038\/s41586-018-0673-2","article-title":"Anthropogenic influences on major tropical cyclone events","volume":"563","author":"Patricola","year":"2018","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1002\/joc.870","article-title":"Precipitation trends in the Canary Islands","volume":"23","author":"Gallego","year":"2003","journal-title":"Int. J. Climatol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1002\/joc.3710","article-title":"An analysis of the climate of Macaronesia, 1865\u20132012","volume":"34","author":"Cropper","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TPWRS.2017.2690297","article-title":"Probabilistic forecast for multiple wind farms based on regular vine copulas","volume":"33","author":"Wang","year":"2018","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.renene.2017.03.064","article-title":"Hour-ahead wind power forecast based on random forests","volume":"109","author":"Lahouar","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1109\/TSTE.2017.2774195","article-title":"Direct interval forecast of uncertain wind power based on recurrent neural networks","volume":"9","author":"Shi","year":"2018","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","article-title":"Ensemble learning for data stream analysis: A survey","volume":"37","author":"Krawczyk","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"278","DOI":"10.2307\/2981683","article-title":"Present position and potential developments: Some personal views statistical theory the prequential approach","volume":"147","author":"Dawid","year":"1984","journal-title":"J. R. Stat. Soc. Ser. A Gen."},{"key":"ref_12","unstructured":"European Commission (EC) (2010). Europe 2020: A Strategy for Smart, Sustainable and Inclusive Growth, European Commission. Working Paper {COM (2010) 2020}."},{"key":"ref_13","first-page":"1","article-title":"Short-term wind prediction using Kalman filters","volume":"9","author":"Bossanyi","year":"1985","journal-title":"Wind Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.solener.2004.09.013","article-title":"Forecast of hourly average wind speed with ARMA models in Navarre (Spain)","volume":"79","author":"Torres","year":"2005","journal-title":"Sol. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.epsr.2014.12.025","article-title":"Markov chain modeling for very-short-term wind power forecasting","volume":"122","author":"Carpinone","year":"2015","journal-title":"Electr. Power Syst. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1016\/j.renene.2008.10.017","article-title":"Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction","volume":"34","author":"Prieto","year":"2009","journal-title":"Renew. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1016\/j.apenergy.2009.12.013","article-title":"On comparing three artificial neural networks for wind speed forecasting","volume":"87","author":"Li","year":"2010","journal-title":"Appl. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dalto, M., Matu\u0161ko, J., and Va\u0161ak, M. (2015, January 17\u201319). Deep neural networks for ultra-short-term wind forecasting. Proceedings of the 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Spain.","DOI":"10.1109\/ICIT.2015.7125335"},{"key":"ref_19","unstructured":"Huang, C.J., and Kuo, P.H. (2018). A deep cnn-lstm model for particulate matter (PM2. 5) forecasting in smart cities. Sensors, 18."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.renene.2003.11.009","article-title":"Support vector machines for wind speed prediction","volume":"29","author":"Mohandes","year":"2004","journal-title":"Renew. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.apenergy.2012.03.054","article-title":"Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output","volume":"99","author":"Cassola","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.renene.2012.07.041","article-title":"Very short-term wind speed forecasting with Bayesian structural break model","volume":"50","author":"Jiang","year":"2013","journal-title":"Renew. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cai, L., Gu, J., Ma, J., and Jin, Z. (2019). Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees. Energies, 12.","DOI":"10.3390\/en12010159"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.renene.2014.09.027","article-title":"A coral reefs optimization algorithm with harmony search operators for accurate wind speed prediction","volume":"75","author":"Prieto","year":"2015","journal-title":"Renew. Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"151","DOI":"10.5194\/asr-2-151-2008","article-title":"Sensitivity study of surface wind flow of a limited area model simulating the extratropical storm Delta affecting the Canary Islands","volume":"2","author":"Marrero","year":"2008","journal-title":"Adv. Sci. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1016\/j.epsr.2011.08.009","article-title":"Estimating wind speed probability distribution using kernel density method","volume":"81","author":"Qin","year":"2011","journal-title":"Electr. Power Syst. Res."},{"key":"ref_27","unstructured":"Bradley, J., Barbier, J., and Handler, D. (2013). Embracing the Internet of Everything To Capture Your Share of $14.4 Trillion: More Relevant Valuable Connections Will Improve Innovation Productivity Efficiency & Customer Experience, Cisco Systems Inc.. White Paper."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Demchenko, Y., Grosso, P., De Laat, C., and Membrey, P. (2013, January 20\u201324). Addressing big data issues in scientific data infrastructure. Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA.","DOI":"10.1109\/CTS.2013.6567203"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.procs.2015.04.188","article-title":"A brief introduction on Big Data 5Vs characteristics and Hadoop technology","volume":"48","author":"Ishwarappa","year":"2015","journal-title":"Proc. Comput. Sci."},{"key":"ref_30","unstructured":"Bifet, A., and Kirkby, R.B. (2019, May 24). Data Stream Mining a Practical Approach. Available online: https:\/\/www.cs.waikato.ac.nz\/~abifet\/MOA\/StreamMining.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gama, J., and Gaber, M.M. (2007). Learning from Data Streams: Processing Techniques in Sensor Networks, Springer.","DOI":"10.1007\/3-540-73679-4"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/BF00116895","article-title":"Incremental learning from noisy data","volume":"1","author":"Schlimmer","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/BF00116900","article-title":"Learning in the presence of concept drift and hidden contexts","volume":"23","author":"Widmer","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_34","unstructured":"Hastie, T., and Tibshirani, R. (1990). Generalized Additive Models, CRC Press."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis, Springer.","DOI":"10.1007\/978-3-319-24277-4_9"},{"key":"ref_36","first-page":"243","article-title":"The climate of the Canary Islands by annual cycle parameters","volume":"XLI-B8","author":"Bechtel","year":"2016","journal-title":"ISPRS"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.2136\/sssaj2009.0436","article-title":"Soil temperature regimes from different latitudes on a subtropical island (Tenerife, Spain)","volume":"74","author":"Neris","year":"2010","journal-title":"Soil Sci. Soc. Am. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2388\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:57Z","timestamp":1760187297000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2388"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,24]]},"references-count":37,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19102388"],"URL":"https:\/\/doi.org\/10.3390\/s19102388","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,24]]}}}