Abstract
The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.
Highlights
Energy guarantees social progress and forms the basis for sustainable development
Value varied from 24.6% to 29.0%, and the normalized mean bias error (nMBE) value varied from 12.3% to 19.2%, so the global horizontal solar irradiance (GHI) values were overestimated
For the smart persistence (SP) model, the deviation was the minimum with the 15 min time horizons, the RMSE value was about 90.1 W/m2, the normalized root mean square error (nRMSE) was about 29.9%, but the RMSE value increased horizons from 15–180 min, and the SP model served as the reference model
Summary
Energy guarantees social progress and forms the basis for sustainable development. Under the pressure of environmental pollution and the shortage of traditional fossil energy, the world is focusing its attention on the research of new energy sources. Photovoltaic power plants have developed rapidly in recent decades [4,5]. The outputs from photovoltaic power plants have the characteristics of great fluctuation and intermittence. Grid-connected large scale photovoltaic power plants create great difficulties in the management, dispatch, and safety operations of the power system. Accurate forecasting, regarding photovoltaic power, is extremely challenging in large-scale applications of photovoltaics (PV). The first step in predicting PV output is to forecast the surface solar irradiance as the fluctuation of surface solar irradiance, which is caused by the complex variation of clouds, affecting photovoltaic power output [5,6]. The accurate temporal prediction of the surface solar irradiance is of great significance for the prediction, planning, and operational control of photovoltaic power
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