Abstract
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.
Highlights
The rapid growth of global energy demands has posed serious challenges for the realization of sustainable economic and social development after entering the 21st century
Comparing the accuracy of the model in different seasons using root mean square error (RMSE) or mean absolute error (MAE) is difficult, so we used the normalized root mean square error (nRMSE) and nMAE to analyze the performance of the models
We presented an algorithm to forecast very short term (0–3 h) surface solar irradiance using FY-4A satellite observations
Summary
The rapid growth of global energy demands has posed serious challenges for the realization of sustainable economic and social development after entering the 21st century. As the largest clean and renewable energy source on earth, solar energy is expected to become the largest power source in the world in the future. The cost of solar panels and related devices has declined dramatically, creating the conditions for large-scale research and application of photovoltaic power generation systems in the coming decades [1,2,3]. Photovoltaic arrays mainly use global solar irradiance to generate output power. Because surface solar irradiance is affected by meteorological factors (mainly clouds), the photovoltaic output power fluctuates and changes considerably. With increases in the Sensors 2020, 20, 2606; doi:10.3390/s20092606 www.mdpi.com/journal/sensors
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