卫星
光伏系统
气象学
环境科学
滞后
计算机科学
间歇性
云计算
遥感
航空航天工程
工程类
地理
电气工程
湍流
计算机网络
操作系统
作者
Hou Jiang,Ning Lu,Ning Liu,Ling Yao,Chenghu Zhou
标识
DOI:10.1016/j.rser.2022.112680
摘要
Accurate output forecasts are essential for photovoltaic projects to achieve stable power supply. Traditional forecasts based on ground observation time series are widely troubled by the phase lag issue due to the incomplete consideration of the impacts of cloud motion. With the consensus that this issue can be addressed by introducing satellite-derived cloud information, we propose an innovative framework that integrates ground and satellite observations through deep learning to enhance PV output forecasts. Cloud motion patterns are captured from satellite observations using convolutional neural networks, and the long-range spatio-temporal cloud impacts on subsequent PV outputs are established by long short-term memory network. The forecast accuracy of real-time PV output is significantly improved, with a minimum (maximum) relative root mean square error of 16% (29%). The ratio of phase lag is reduced to 15% on average. This work provides a potential for alleviating the power intermittency of solar PV system and making advance planning in solar energy utilization.
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