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METHODS article

Front. Energy Res.
Sec. Wind Energy
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1365237
This article is part of the Research Topic Co-operative Progress in Distributed Wind and Hydrokinetic Energy Systems View all articles

Wind power short-term prediction based on digital twin technology

Provisionally accepted
Shu Liu Shu Liu *
  • Shenyang Institute of Engineering, Shenyang, China

The final, formatted version of the article will be published soon.

    Wind power generation has become an indispensable part of the power supply side of the power grid. Because of the intermittent nature of wind energy, short-term predictions of wind power control to the stable operation of power systems. By constructing the digital twin model, the real-time and highprecision prediction of wind energy is realized. Firstly, GA-SVM algorithm is used to build the model.Multi-dimensional sensors and meteorological stations of the virtual wind power generation system, collect the meteorological data of the environment, and update the meteorological history database at the same time; Secondly, bottomed on the collected meteorological data, the preliminary prediction results are obtained, by searching in the historical database, the predicted value and the actual output value of wind turbine or wind farm under similar conditions are obtained. Finally, the prediction results of the GA-SVM are modified to obtain the predicted value of the digital twin. The prediction method can greatly improve the short-term forecast accuracy of wind energy.

    Keywords: Digital Twin1, Wind power prediction2, Genetic Algorithm3, SVM model4, GA-SVM5.

    Received: 04 Jan 2024; Accepted: 21 Mar 2024.

    Copyright: © 2024 Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Shu Liu, Shenyang Institute of Engineering, Shenyang, China

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