Interpretable extreme wind speed prediction with concept bottleneck models

Abstract

Concept-bottleneck models (CBMs) are a new paradigm to construct interpretable classifiers. The CBM architecture can be regarded as a neural network with a single hidden layer whose neurons are replaced by binary classifiers. Each of these binary classifiers implements a concept, that is, it attempts to answer a meaningful yes/no question: the presence or absence of a concept in the observation presented to the network. The output layer is usually implemented as a linear stage that combines the concepts into final decisions. This paper presents an application of the CBM paradigm to a problem of Extreme Wind Speed prediction, such that the forecasting properties of the system can be interpreted in terms of different concepts considered for this problem. A significant contribution of this paper is the proposal of an automated concept generation method, with controlled sparsity, in which the concepts are automatically extracted from the branches of decision trees fitted with informative features that capture the dynamics of the wind speed time series. The final forecasting system is able to predict extreme wind speed values in wind farms with good accuracy and interpretable results, as experiments over real wind speed data from a wind farm in Spain show.