A hybrid deep learning-based neural network for 24-h ahead wind power forecasting
By Yingyi Hong, C. L. Rioflorido. Applied Energy, 2019
299 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/3fb05a391068a1b80127fabdb84c16c34c70597e
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In Artificial Intelligence
In Electrical and Electronic Engineering
Citations in the corpus
- A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation (2021, methodology): “[19] developed a hybrid deep learning-based method for wind power prediction where the convolutional neural network (CNN) was used to extract features of volatile wind power time-series.”