临近预报
地球静止轨道
光伏系统
光辉
云计算
气象学
环境科学
遥感
太阳辐照度
卫星
计算机科学
航空航天工程
地理
工程类
电气工程
操作系统
作者
Pan Xia,Lu Zhang,Min Min,Jun Li,Yun Wang,Yu Yu,Shengjie Jia
标识
DOI:10.1038/s41467-023-44666-1
摘要
Abstract Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction at the leading time of 0–4 h at photovoltaic plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at five photovoltaic plants and several stations with cloud fraction observations in China. The results demonstrate that the cloud fraction nowcasting is efficient, high quality and adaptable. Particularly, it shows an excellent forecast performance within the first 2-hour leading time, with an average correlation coefficient close to 0.8 between the predicted clear sky ratio and actual power generation at photovoltaic plants. Our findings highlight the benefits and potential of this technique to improve the competitiveness of solar photovoltaic energy in electricity market.
科研通智能强力驱动
Strongly Powered by AbleSci AI