Wind parameter forecasting for wind turbines

Simple item page
dc.contributor.advisorSantos Peñas, Matilde
dc.contributor.authorGarcía Puente, Belén
dc.contributor.authorRodríguez Hurtado, Antonio
dc.date.accessioned2023-06-16T13:24:40Z
dc.date.available2023-06-16T13:24:40Z
dc.date.issued2022-09
dc.degree.titleGrado en Ingeniería Informática
dc.descriptionTrabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Arquitectura de Computadores y Automática, Curso 2021/2022.
dc.description.abstractIn recent years, the importance of clean and renewable energy has increased due to the rise of pollution and environmental degradation. In that context, alternatives such as wind, solar, wave, green hydrogen, or biomass stand out. Specifically, wind energy has been considered as one of the most promising alternatives. Nevertheless, wind energy is a quite unstable source, due to the continuous variation and random nature of wind and wind speed. The uncertainty generated by this energy production clearly affects its stability, and increases the cost of its productions. That is why accurate forecasting of wind positively affects wind energy development and its trade in certain markets. In this context, we have analyzed if gradient boosting (XGBoost), a very recent and powerful intelligent technique, can be used to obtain great results in wind prediction. Therefore, in this project we aim to model some wind features using XGBoost, and then compare them with the performance of other algorithms, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR) and neural networks (NN) to see if it is a promising algorithm to consider. In this project we show that the results obtained confirmed our theory: XGBoost is in fact a good option to consider in wind forecasting. However, we have to keep in mind that the further we want to predict, the worse the accuracy of each model gets. Due to this, these algorithms are better for short-term forecasts.
dc.description.departmentDepto. de Arquitectura de Computadores y Automática
dc.description.facultyFac. de Informática
dc.description.refereedTRUE
dc.description.statusunpub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/75018
dc.identifier.urihttps://hdl.handle.net/20.500.14352/3310
dc.language.isoeng
dc.page.total84
dc.rightsAtribución-NoComercial 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/es/
dc.subject.cdu004(043.3)
dc.subject.keywordWind forecasting
dc.subject.keywordSVR
dc.subject.keywordGPR
dc.subject.keywordXGBoost
dc.subject.keywordNeural Networks
dc.subject.keywordRegression
dc.subject.keywordWind models
dc.subject.keywordGradient boosting
dc.subject.keywordPrediction
dc.subject.keywordRMSE.
dc.subject.ucmInformática (Informática)
dc.subject.unesco1203.17 Informática
dc.titleWind parameter forecasting for wind turbines
dc.title.alternativePredicción de parámetros del viento aplicado a turbinas eólicas.
dc.typebachelor thesis
dspace.entity.typePublication
relation.isAdvisorOfPublication99cac82a-8d31-45a5-bb8d-8248a4d6fe7f
relation.isAdvisorOfPublication.latestForDiscovery99cac82a-8d31-45a5-bb8d-8248a4d6fe7f
Download
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1901348434 - ANTONIO RODRÍGUEZ HURTADO 83118_ANTONIO_RODRIGUEZ_HURTADO_Memoria_TFG_1398833_1252709322.pdf
Size:
5.63 MB
Format:
Adobe Portable Document Format
downoload Download
Collections
Trabajos Fin de Grado (TFG) y Diplomas de Estudios Avanzados (DEA)