Research on short-term wind power combined forecasting and its Gaussian cloud uncertainty to support the integration of renewables and EVs-Reference-Cited by-同舟云学术

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Research on short-term wind power combined forecasting and its Gaussian cloud uncertainty to support the integration of renewables and EVs

Published:2020-06 Issue: Volume:153 Page:884-899
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Zhang Jinhua,Meng Hang,Gu Bo,Li Pin

Funder

Key scientific research projects of Henan colleges and universities

Innovative Research Team (in Science and Technology) in University of Henan Province

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference43 articles.

1. Technical challenges associated with the integration of wind power into power systems;Georgilakis;Renew. Sustain. Energy Rev.,2008

2. A novel hybrid approach for wind speed prediction;Wang;Inf. Sci.,2014

3. The Global Wind Energy Statistics;Global Wind Energy Council,2017

4. Wind Power Capacity Statistics for 2017;China Wind Energy Professional Committee of the Renewable Energy Society,2018

5. A review of researches on wind power forecasting Technology;Jinghua;Mod. Electr. Power,2017

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1. A review of short-term wind power generation forecasting methods in recent technological trends;Energy Reports;2024-12

2. Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method;Renewable Energy;2024-05

3. Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network;Energies;2024-01-12

4. Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives;Renewable and Sustainable Energy Reviews;2023-08

5. A novel two-layer hybrid model for ultra-short-term wind speed prediction based on SSP and BO-LSTM;WIND STRUCT;2023

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