Abstract
As a renewable energy source, wind power is considered to be a significant alternate source of energy in the times of energy crisis. 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 prediction model is presented using a neural network optimized by a genetic algorithm (GA). Using wind data collected from a wind farm in Inner Mongolia of China, a power forecasting map is illustrated, and a comparative study between a Back-Propagation (BP) neural network model and a GA-BP neural network model is undertaken.
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© 2011 Springer-Verlag Berlin Heidelberg
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Xu, RL., Xu, X., Zhu, B., Chen, My. (2011). The Application of Genetic-Neural Network on Wind Power Prediction. In: Liu, C., Chang, J., Yang, A. (eds) Information Computing and Applications. ICICA 2011. Communications in Computer and Information Science, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27452-7_52
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DOI: https://doi.org/10.1007/978-3-642-27452-7_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27451-0
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