Kernel ridge regression with active learning for wind speed prediction

This paper introduces the active learning approach for wind speed prediction. The main objective of active learning is to opportunely collect training samples in such a way as to minimize the error of the prediction process while minimizing the number of training samples used, and thus to reduce the costs related to the training sample collection. In particular, we propose three different active learning strategies, developed for kernel ridge regression (KRR). The first strategy uses a pool of regressors in order to select the samples with the greater disagreements between the different regressors, while the second one relies on the idea to add samples that are distant from the available training samples, and the last strategy is based on the selection of samples which exhibit a high expected prediction error. A thorough experimental study is presented. It is based on ten different wind speed measurement stations distributed over the vast Algerian territory. Promising results are reported, showing that a smart collection of training samples can be of benefit for wind speed prediction problems.

► This paper introduces the active learning approach for wind speed prediction. ► Three different active learning strategies are described. ► They are specifically developed for kernel ridge regression. ► Results show that a smart collection of training samples can be of great benefit.