A minutely solar irradiance forecasting method based on real-time sky image-irradiance mapping model
Wang, Fei; Xuan, Zhiming; Zhen, Zhao; Li, Yu; Li, Kangping; Zhao, Liqiang; Shafie-khah, Miadreza; Catalão, João P.S. (2020-09-15)
Tiedosto avautuu julkiseksi: : 15.09.2022
Catalão, João P.S.
Julkaisun pysyvä osoite on
©2020 Elsevier Ltd. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/
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.
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