Abstract Accurate minutely solar irradiance forecasting is the basis of minute-level photovoltaic (PV) power forecasting. In this paper, a minutely solar irradiance forecasting method based on real-time surface irradiance mapping model is proposed, which is beneficial to achieve higher accuracy in solar power forecasting. First, we extract the red–green–blue (RGB) values and position information of pixels in sky images after background elimination and distortion rectification, to explore the mapping relationship between sky image and solar irradiance. Then a real-time sky image-irradiance mapping model is built, trained, and updated according to real-time sky images and solar irradiance. Finally, the future solar irradiance within the time horizons varying from 1 min to 10 min ahead are capable to be forecasted by using the latest updated surface irradiance mapping model with extracted input from the current sky image. The average measures of proposed method by using MAPE, RMSE, MBE are 22.66%, 92.72, −1.26% for blocky clouds; 20.44%, 132.15, −1.06% for thin clouds and 18.82%, 120.78, −0.98% for thick clouds, thus deliver much higher forecasting accuracy than other benchmarks.

中文翻译：

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一种基于实时天空图像-辐照度映射模型的太阳辐照度精细预报方法

摘要 精确的分钟级太阳辐照度预测是分钟级光伏（PV）功率预测的基础。本文提出了一种基于实时地表辐照度映射模型的精细太阳辐照度预测方法，有利于实现更高的太阳能功率预测精度。首先，我们在背景消除和失真校正后提取天空图像中像素的红-绿-蓝（RGB）值和位置信息，以探索天空图像与太阳辐照度之间的映射关系。然后根据实时天空图像和太阳辐照度建立、训练和更新实时天空图像-辐照度映射模型。最后，可以通过使用最新更新的表面辐照度映射模型以及从当前天空图像中提取的输入来预测未来 1 分钟到 10 分钟的时间范围内的未来太阳辐照度。对于块状云，使用 MAPE、RMSE、MBE 的建议方法的平均度量为 22.66%、92.72、-1.26%；薄云为 20.44%、132.15、-1.06%，厚云为 18.82%、120.78、-0.98%，因此比其他基准提供更高的预测精度。