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Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation

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成果类型:
期刊论文
作者:
Wu, Weixiong;Gao, Rui;Wu, Peng;Yuan, Chen;Xia, Xiaoling;...
通讯作者:
Yuan, C
作者机构:
[Gao, Rui; Wu, Weixiong; Wu, Peng] Guizhou Beipanjiang Elect Power Co Ltd, Mamaya Photovolta Branch, Guiyang 550081, Peoples R China.
[Yuan, Chen; Xia, Xiaoling] Guizhou New Meteorol Technol Co Ltd, Guiyang 550081, Peoples R China.
[Wang, Yifei; Liu, Renfeng] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
通讯机构:
[Yuan, C ] G
Guizhou New Meteorol Technol Co Ltd, Guiyang 550081, Peoples R China.
语种:
英文
关键词:
ultra-short-term PV power forecasting;sky imagers;MCR;cloud cover estimation
期刊:
Energies
ISSN:
1996-1073
年:
2025
卷:
18
期:
1
页码:
28-
基金类别:
Conceptualization, C.Y.; data curation, R.G. and X.X.; formal analysis, R.L. and C.Y.; funding acquisition, W.W.; investigation, P.W., R.G. and X.X.; methodology, R.L., W.W. and C.Y.; project administration, W.W.; software, P.W., Y.W. and X.X.; supervision, C.Y. and W.W.; writing—original draft, W.W. and R.L.; writing—review and editing, R.L. All authors have read and agreed to the published version of the manuscript. This research was funded by China Huadian Corporation’s 2023 Science and Technology Project (CHDKJ23-02-40): “Research and Application of Meteorological Empowerment for Photovoltaic Power Plant Safety and Efficient Warning and Prediction Technology”, and Guizhou Meteorological Science and Technology Cooperation Project TD [2024]04: “Research on Wind and Solar Power Forecasting Technology”.
机构署名:
本校为其他机构
院系归属:
数学与计算机学院
摘要:
Accurate photovoltaic (PV) power forecasting is crucial for stable grid integration, particularly under rapidly changing weather conditions. This study presents an ultra-short-term forecasting model that integrates sky imager data and meteorological radar data, achieving significant improvements in forecasting accuracy. By dynamically tracking cloud movement and estimating cloud coverage, the model enhances performance under both clear and cloudy conditions. Over an 8-day evaluation period, the average forecasting accuracy improved from 67.26% ...

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