Ultra-short-term Photovoltaic Power Forecasting Based on Multi-level Sky Image Features and Broad Learning

被引：0

作者：

Chen, Dianhao ^{[1
]}

Zang, Haixiang ^{[1
]}

Jiang, Yunan ^{[1
]}

Liu, Jingxuan ^{[1
]}

Sun, Guoqiang ^{[1
]}

Wei, Zhinong ^{[1
]}

机构：

[1] School of Electrical and Power Engineering, Hohai University, Nanjing

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 22期

基金：

中国国家自然科学基金;

关键词：

broad learning; ground-based sky image; incremental learning; photovoltaic power forecasting; ramp event;

D O I：

10.7500/AEPS20240416009

中图分类号：

学科分类号：

摘要：

Aiming at the problems of insufficient use of sky image information and large errors in ramp power forecasting, which lead to the limited predictive performance improvement, an ultra-short-term photovoltaic power forecasting method based on multilevel sky image features and broad learning is proposed. Firstly, the multi-level features of the ground-based sky image are extracted as the image features of the power forecasting model. At the same time, the cloud coverage and cloud change rate are introduced as image features of the ramp recognition model. Secondly, combined with the historical power data, the photovoltaic power forecasting model and the ramp recognition model based on the broad learning are developed. Finally, if the ramp recognition result is a non-ramp event, the forecasting results are obtained according to the power forecasting model. However, if the ramp recognition result is a ramp event, the power forecasting model is incrementally updated using the historical data related to the ramp event, and the forecasting results are obtained based on the updated power forecasting model. The experimental results show that the proposed method can effectively improve the forecasting accuracy of ultra-short-term photovoltaic power. © 2024 Automation of Electric Power Systems Press. All rights reserved.

引用

页码：131 / 139

页数：8

共 24 条

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