Short‐term wind power forecasting based on two‐stage attention mechanism-Reference-Cited by-同舟云学术

Short‐term wind power forecasting based on two‐stage attention mechanism

Published:2020-01-22 Issue:2 Volume:14 Page:297-304
ISSN:1752-1416
Container-title:IET Renewable Power Generation
language:en
Short-container-title:IET Renewable Power Generation

Author:

Li Pengbo¹,Wang Xiangwen¹,Yang Junjie¹²

Affiliation:

1. College of Electronics and Information EngineeringShanghai University of Electric PowerNo. 2103, Pingliang Road, Yangpu DistrictShanghaiPeople's Republic of China

2. Shanghai Dianji UniversityNo. 300, Shuihua Road, Pudong New Area DistrictShanghaiPeople's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2019.0614

Reference26 articles.

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2. Prediction methods of short‐term wind power in wind power plant;Chen D.‐D.;J. Shanghai Univ. Electr. Power,2011

3. A short‐term wind power forecasting approach with adjustment of numerical weather prediction input by data mining;Xu Q.;IEEE Trans. Sustain. Energy,2015

4. A sensitivity study of the WRF model in wind simulation for an area of high wind energy

5. Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals

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