A fuzzy-based cascade ensemble model for improving extreme wind speeds prediction
Article
Pelaez-Rodriguez, C., Perez-Aracil, J., Prieto-Godino, L., Ghimire, S., Deo, R. and Salcedo-sanz, S.. 2023. "A fuzzy-based cascade ensemble model for improving extreme wind speeds prediction." Journal of Wind Engineering and Industrial Aerodynamics. 240. https://doi.org/10.1016/j.jweia.2023.105507
Article Title | A fuzzy-based cascade ensemble model for improving extreme wind speeds prediction |
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ERA Journal ID | 3792 |
Article Category | Article |
Authors | Pelaez-Rodriguez, C., Perez-Aracil, J., Prieto-Godino, L., Ghimire, S., Deo, R. and Salcedo-sanz, S. |
Journal Title | Journal of Wind Engineering and Industrial Aerodynamics |
Journal Citation | 240 |
Article Number | 105507 |
Number of Pages | 25 |
Year | 2023 |
Place of Publication | Netherlands |
ISSN | 0167-6105 |
1872-8197 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.jweia.2023.105507 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0167610523002106 |
Abstract | A novel fuzzy-based cascade ensemble of regression models is proposed to address a problem of extreme wind speed events forecasting, using data from atmospheric reanalysis models. Although this problem has been mostly explored in the context of classification tasks, the innovation of this paper arises from tackling a continuous predictive domain, aiming at providing an accurate estimation of the extreme wind speed values. The proposed layered framework involves a successive partition of the training data into fuzzy-soft clusters according to the target variable value, and further training a specific regression model within each designated cluster, so that each model can analyze a particular part of the target domain. Finally, predictions made by individual models are integrated into a fuzzy-based ensemble, where a pertinence value is designated to each model based on the previous layer's prediction, and on the defined membership functions for each cluster. A Differential Evolution (DE) optimization algorithm is adopted to find the optimal way to perform data partitioning. Fast training randomized neural networks methods are used as final regression schemes. The performance of the proposed methodology has been assessed by comparison against state-of-the-art methods in real data from three wind farms in Spain. |
Keywords | Wind energy; Wind speed extremes; Wind extremes prediction; Extreme Learning Machine; Fuzzy ensemble |
ANZSRC Field of Research 2020 | 370101. Adverse weather events |
401199. Environmental engineering not elsewhere classified | |
Byline Affiliations | University of Alcala, Spain |
Polytechnic University of Madrid, Spain | |
Iberdrola, Spain | |
School of Mathematics, Physics and Computing |
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