Very Short-Term Surface Solar Irradiance Forecasting Based On FengYun-4 Geostationary Satellite

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 9; p. 2606
• Main Authors: Yang, Liwei, Gao, Xiaoqing, Hua, Jiajia, Wu, Pingping, Li, Zhenchao, Jia, Dongyu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 03.05.2020; MDPI AG
• Subjects: FengYun-4A satellite; forecast; Keywords: solar energy; solar power plant; surface solar irradiance; Keywords: solar energy; solar power plant; FengYun-4A satellite; forecast; surface solar irradiance
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20092606

An algorithm to forecast very short-term (30-180 min) surface solar irradiance using visible and near infrared channels (AGRI) onboard the FengYun-4A (FY-4A) geostationary satellite was constructed and evaluated in this study. The forecasting products include global horizontal irradiance (GHI) and direct normal irradiance (DNI). The forecast results were validated using data from Chengde Meteorological Observatory for four typical months (October 2018, and January, April, and July 2019), representing the four seasons. Particle Image Velocimetry (PIV) was employed to calculate the cloud motion vector (CMV) field from the satellite images. The forecast results were compared with the smart persistence (SP) model. A seasonal study showed that July and April forecasting is more difficult than during October and January. For GHI forecasting, the algorithm outperformed the SP model for all forecasting horizons and all seasons, with the best result being produced in October; the skill score was greater than 20%. For DNI, the algorithm outperformed the SP model in July and October, with skill scores of about 12% and 11%, respectively. Annual performances were evaluated; the results show that the normalized root mean square error (nRMSE) value of GHI for 30-180 min horizon ranged from 26.78% to 36.84%, the skill score reached a maximum of 20.44% at the 30-min horizon, and the skill scores were all above 0 for all time horizons. For DNI, the maximum skill score was 6.62% at the 180-min horizon. Overall, compared with the SP model, the proposed algorithm is more accurate and reliable for GHI forecasting and slightly better for DNI forecasting.