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Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction

  • Published:2023-04 Issue: Volume:336 Page:120815
  • ISSN:0306-2619
  • Container-title:Applied Energy
  • language:en
  • Short-container-title:Applied Energy

Author:

Liu ChenyuORCID,Zhang Xuemin,Mei Shengwei,Zhou Qingyu,Fan HangORCID

Funder

National Key Research and Development Program of China

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference47 articles.

1. Review of the use of Numerical Weather Prediction (NWP) models for wind energy assessment;Al-Yahyai;Renew Sustain Energy Rev,2010

2. Ensemble machine learning-based wind forecasting to combine NWP output with data from weather station;Pengwei;IEEE Trans Sustain Energy,2019

3. Wind power forecasts using Gaussian processes and Numerical Weather Prediction;Chen;IEEE Trans Power Syst,2014

4. Sub-hourly forecasting of wind speed and wind energy;Dupré;Renew Energy,2020

5. M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions;Wang;Appl Energy,2022
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