RT Journal Article
T1 Ensemble forecaster based on the combination of time-frequency analysis and machine learning strategies for very short-term wind speed prediction
A1 Rodríguez, Fermín
A1 Alonso-Pérez, Sandra
A1 Sánchez-Guardamino, Ignacio
A1 Galarza, Ainhoa
AB Based on the predictions of fossil fuels depletion in the following years, as well as their negative impact due to generated exhaust fumes, eco-friendly generators, and more specifically wind generators, have arisen as a solution for the electric demand challenge. Wind energy consists in extracting energy from wind speed, and because of the uncertain and intermittent behaviour of this meteorological parameter, wind turbines output power cannot be optimally exploited. Although the vast majority of the research in wind speed forecasting field has consisted in the purpose of novel algorithms, these studies have not made a pre-processing step of the data in order to try to extract the maximum information from databases. Therefore, the goal of this paper consists in analysing whether the combination of time-frequency decomposition of wind speed data with different machine learning algorithms can increase the accuracy of current wind speed predictions for 10 min ahead. Obtained error metrics demonstrated that the deviation of developed wind speed forecaster was lower than 0.1% in 62% of the validation database. In addition, the root mean square error of the final forecaster was 0.34 m/s. This means an accuracy increase of 51.5% if the result is compared with benchmark model's results.
SN 0378-7796
YR 2023
FD 2023-01-01
LA eng
NO Rodríguez , F , Alonso-Pérez , S , Sánchez-Guardamino , I & Galarza , A 2023 , ' Ensemble forecaster based on the combination of time-frequency analysis and machine learning strategies for very short-term wind speed prediction ' , Electric Power Systems Research , vol. 214 , 108863 . https://doi.org/10.1016/j.epsr.2022.108863
NO Publisher Copyright: © 2022
DS TECNALIA Publications
RD 3 sept 2024