A Dynamic Prediction Model for Wind Farm Power Generation Based on RBF Neural Network

Abstract:

As a renewable energy, wind power is considered to be an important non-exhaustible energy in the times of energy crisis. Therefore, in electric power system, wind turbine generators have become important generation alternatives. As wind power penetration increases, power forecasting is crucially important for integrating wind power into a conventional power grid. A short-term wind farm power output dynamic prediction model is presented using RBF neural network with error discriminant function. Based on the wind data from a wind farm in Inner Mongolia of China, a power forecasting map is illustrated, and the errors of the model are analyzed to present the differences between dynamic model and conventional prediction model.

• Periodical: Advanced Materials Research (Volumes 516-517)
• Pages: 1282-1287
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.516-517.1282
• Online since: May 2012
• Authors: Bo Zhu, Min You Chen, Rui Lin Xu, Xin Xu, Qian Wang
• Keywords: Dynamic Prediction, Error Discriminant Function, RBF Neural Network, Renewable Energy, Wind Farm

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