NDLI: Short-term wind power forecasting based on two-stage attention mechanism

Content Provider	IET Digital Library
Author	Li, Pengbo Wang, Xiangwen Yang, Junjie
Abstract	Wind power is usually closely related to the meteorological information around the wind farm, which leads to the fluctuation of wind power and makes it difficult to predict precisely. In this study, a wind power forecasting model based on long–short-term memory network two-stage attention mechanism is established. The attention mechanism is extensively employed to weigh the input feature and strengthen the trend characteristic of wind power. The intermittency and volatility feature of the wind are efficiently mitigated, and the prediction accuracy is improved significantly. Besides, quantile regression and kernel density estimation are combined with the proposed model to predict the wind power interval and the probability density. These two parameters are important information for ensuring security and stability while accessing to the electricity grid. The simulation results on the actual wind power dataset verify the higher prediction accuracy of the proposed model compared with other machine learning methods.
Starting Page	297
Ending Page	304
Page Count	8
ISSN	17521416
Volume Number	14
e-ISSN	17521424
Issue Number	Issue 2, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/14/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.0614
Journal	IET Renewable Power Generation
Publisher Date	2019-11-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Actual Wind Power Dataset Knowledge Engineering Technique Learning in AI Load Forecasting Long–short-term Memory Network Two-stage Attention Mechanism Neural Nets Power Engineering Computing Power System Planning And Layout Probability Probability Theory Regression Analysis Statistics Stochastic Linearised SCUC Term Wind Power Forecasting Wind Farm Wind Power Wind Power Forecasting Model Wind Power Interval Wind Power Plant
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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