Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction

Author:

Hu Shuai,Xiang Yue,Zhang Hongcai,Xie Shanyi,Li Jianhua,Gu Chenghong,Sun Wei,Liu Junyong

Funder

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

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

Reference59 articles.

1. Review of wind energy technology and associated market and economic conditions in Spain;Rosales-Asensio;Renew Sustain Energy Rev,2019

2. Very short-term spatial and temporal wind power forecasting: A deep learning approach;Hu;CSEE J Power Energy Syst,2020

3. A combined framework based on data preprocessing, neural networks and multi-tracker optimizer for wind speed prediction;Wang;Sustainable Energy Technol Assess,2020

4. Renewable generation forecast studies – Review and good practice guidance;Croonenbroeck;Renew Sustain Energy Rev,2019

5. Forecasting the high penetration of wind power on multiple scales using multi-to-multi mapping;Yan;IEEE Trans Power Syst,2017

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