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

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Carro Calvo, L. and Salcedo Sanz, S. and Kirchner Bossi, N. and Portilla Figueras, A. and Prieto, L. and García Herrera, Ricardo and Hernández Martín, E. (2011) Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing. Energy, 36 (3). pp. 1571-1581. ISSN 0360-5442

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Official URL: http://dx.doi.org/10.1016/j.energy.2011.01.001


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Abstract

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.


Item Type:Article
Additional Information:

© 2011 Elsevier Ltd. All rights reserved. This work has been partially supported by Spanish Ministry of Industry, Tourism and Trading, under an Avanza 2 project, number TSI-020100-2010-663.

Uncontrolled Keywords:Means clustering-algorithm; Neural-networks; Expression data; Optimization; MOdels; Computation; Prediction; Tracking; SYstems; Energy
Subjects:Sciences > Physics > Atmospheric physics
ID Code:61757
Deposited On:02 Sep 2020 12:07
Last Modified:03 Sep 2020 06:28

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