Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing

In this paper we present an evolutionary approach for the problem of discovering pressure patterns under a quality measure related to wind speed and direction. This clustering problem is specially interesting for companies involving in the management of wind farms, since it can be useful for analysis of results of the wind farm in a given period and also for long-term wind speed prediction. The proposed evolutionary algorithm is based on a specific encoding of the problem, which uses a dimensional reduction of the problem. With this special encoding, the required centroids are evolved together with some other parameters of the algorithm. We define a specific crossover operator and two different mutations in order to improve the evolutionary search of the proposed approach. In the experimental part of the paper, we test the performance of our approach in a real problem of pressure pattern extraction in the Iberian Peninsula, using a wind speed and direction series in a wind farm in the center of Spain. We compare the performance of the proposed evolutionary algorithm with that of an existing weather types (WT) purely meteorological approach, and we show that the proposed evolutionary approach is able to obtain better results than the WT approach.

► A novel evolutionary algorithm to detect atmospheric pressure patterns is proposed.
► A measure of wind speed and direction is used as objective function.
► The algorithm is used for long-term wind speed prediction, or in wind speed reconstruction.
► A test of the algorithm on wind module reconstruction in a wind farm is shown.