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Findings on Energy Reported by Investigators at Chung Yuan Christian University (A Hybrid Deep Learning-based Neural Network for 24-h Ahead Wind Power Forecasting)

Source: 
Network Daily News

2019 SEP 17 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- A new study on Energy is now available. According to news reporting originating from Taoyuan, Taiwan, by NewsRx correspondents, research stated, “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.”

Funders for this research include Ministry of Science and Technology of the Republic of China, Taiwan, MOST.

Our news editors obtained a quote from the research from Chung Yuan Christian University, “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.”

According to the news editors, the research concluded: “Simulation results reveal that the proposed method is more accurate than traditional methods for 24 h-ahead wind power forecasting.”

For more information on this research see: A Hybrid Deep Learning-based Neural Network for 24-h Ahead Wind Power Forecasting. Applied Energy, 2019;250():530-539. Applied Energy can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Applied Energy - http://www.journals.elsevier.com/applied-energy/)

The news editors report that additional information may be obtained by contacting Y.Y. Hong, Chung Yuan Christian University, Dept. of Electronics Engineering, Taoyuan 320, Taiwan.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.apenergy.2019.05.044. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

 

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