Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/281588 
Year of Publication: 
2021
Citation: 
[Journal:] Amfiteatru Economic Journal [ISSN:] 2247-9104 [Volume:] 23 [Issue:] 57 [Year:] 2021 [Pages:] 412-428
Publisher: 
The Bucharest University of Economic Studies, Bucharest
Abstract: 
The adoption of wind energy has grown significantly in recent years. New, cost-effective technologies have been developed, led by customer awareness of green technologies and a legal framework proposed at the European Union level. The stochastic nature of wind speed is transferred to wind turbine output, making wind energy difficult to predict. The main scope of predicting wind energy production is to be proactive in balancing and reserving energy to meet demand. When the prediction identifies a potential gap between supply and demand, additional energy from other sources must be generated and supplied. Creating a synergy of physical devices through advanced sensing capabilities, software, storage and analytics capabilities, the Industrial Internet of Things is enabling the effective transition to wind energy through automation by removing many of the disadvantages in a way that has recently become accessible. This research focuses on the data analytics, proposing a fast univariate network-based approach for wind energy prediction, using Feed Forward Neural Networks, Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Unit, and Convolutional Neural Networks. Moreover, by introducing the theoretical fundamentals, the implementation method and the hyperparameters of the final models, this article becomes unique in the context of wind energy. At the time of this study, no prior research studies have presented a direct comparison between feedforward, recurrent, and convolutional neural networks - these being the most important in the field of supervised learning.
Subjects: 
machine learning
artificial neural networks
wind energy
internet of things
industrial internet of things
JEL: 
O13
O14
O31
Q47
C45
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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