Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty-Reference-Cited by-同舟云学术

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Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty

Published:2023-03 Issue: Volume:205 Page:598-607
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Liu Lei^ORCID,Liu Jicheng,Ye Yu,Liu Hui,Chen Kun,Li Dong,Dong Xue,Sun Mingzhai^ORCID

Funder

Key Technologies Research and Development Program

National Natural Science Foundation of China

Natural Science Foundation of Anhui Province

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference56 articles.

1. A review on the forecasting of wind speed and generated power;Lei;Renew. Sustain. Energy Rev.,2009

2. A review of combined approaches for prediction of short-term wind speed and power, Renew. Sustain. Energy Rev. 34 (jun.) (2014) 243–254.

3. A review on the selected applications of forecasting models in renewable power systems, Renew. Sustain. Energy Rev. 100 (2019) 9–21.

4. A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning;Wang;Renew. Energy,2020

5. Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods;Zhang;Energy Convers. Manage.,2016

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