A fuzzy-based cascade ensemble model for improving extreme wind speeds prediction

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.