Ultra‐short‐term irradiance forecasting model based on ground‐based cloud image and deep learning algorithm

Author:

Zhen Zhao123ORCID,Zhang Xuemin1ORCID,Mei Shengwei1ORCID,Chang Xiqiang4,Chai Hua2,Yin Rui5,Wang Fei23

Affiliation:

1. State Key Lab of Power System, Department of Electrical Engineering Tsinghua University Beijing China

2. Department of Electrical Engineering North China Electric Power University Baoding China

3. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) Beijing China

4. State Grid Xinjiang Electric Power Co., Ltd Urumqi China

5. State Grid Hebei Electric Power Co., Ltd Shijiazhuang China

Funder

National Natural Science Foundation of China

Science and Technology Project of State Grid

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

Reference50 articles.

1. Solar energy: Potential and future prospects;Kabir E.;Renewable Sustain Energy Rev.,2018

2. irena.org: Solar Energy Data.https://www.irena.org/solar(2021). Accessed 17 April 2021

3. iea.org: Global Energy Review 2020.https://www.iea.org/reports/global‐energy‐review‐2020/renewables#abstract(2020). Accessed 17 June 2020

4. Optimal power peak shaving using hydropower to complement wind and solar power uncertainty;Liu B.;Energy Convers. Manag,2020

5. Capacity and output power estimation approach of individual behind‐the‐meter distributed photovoltaic system for demand response baseline estimation;Kangping L.;Appl. Energy,2019

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