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Online ISSN 1752-1424
Print ISSN 1752-1416

IET Renewable Power Generation (RPG) brings together the topics of renewable energy technology, power generation and systems integration, with techno-economic issues. All renewable energy generation technologies are within the scope of the journal.


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Impact Factor: 3.605
5-year Impact Factor: 3.649
CiteScore: 4.6
SNIP: 1.452
SJR: 1.041

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2020 Research Highlights

Xiangwen Wang

Xiangwen Wang, Shanghai University of Electric Power, China

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

This paper presents a new wind power forecasting method farms based on a deep-learning network called long short-term memory network two-stage attention. Unlike traditional prediction methods, the proposed technique uses learning to autonomously select the most important features of the weather conditions affecting wind power generation. The study has been validated using experimental data from the SCADA system of a real wind farm and shows a better prediction accuracy than traditional methods. The main impact of the study is the improvement of power dispatching as a more accurate knowledge of the available power generation will improve the economy, security and reliability of power grid operations. - Pietro Tricoli, Deputy Editor in Chief

Free to access until 3rd March 2020

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