A hybrid deep learning-based neural network for 24-h ahead wind power forecasting

Ying-Yi Hong and Christian Lian Paulo P. Rioflorido

Applied Energy, 2019, vol. 250, issue C, 530-539

Abstract: Wind power generation is always associated with uncertainties as a result of fluctuations of wind speed. Accurate predictions of wind power generation are important for the efficient operation of power systems. This paper presents a hybrid deep learning neural network for 24 h-ahead wind power generation forecasting. This novel method is based on a Convolutional Neural Network (CNN) that is cascaded with a Radial Basis Function Neural Network (RBFNN) with a double Gaussian function (DGF) as its activation function. The CNN is utilized to extract wind power characteristics by convolution, kernel and pooling operations. The supervised RBFNN, incorporating a DGF, deals with uncertain characteristics. Realistic wind power generations, measured on a wind farm, were used in simulations. The proposed method is implemented using TensorFlow and Keras Library. Comparative studies of different approaches are shown. Simulation results reveal that the proposed method is more accurate than traditional methods for 24 h-ahead wind power forecasting.

Keywords: Deep learning; Double Gaussian function; Feature extraction; Wind power forecasting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (61)

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DOI: 10.1016/j.apenergy.2019.05.044

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