Abstract
Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.
Highlights
Solar photovoltaics (PV) is developing at an unprecedented speed, which is attributed to dramatic cost reductions, technology advancements and government policy support [1,2,3]
The goal of this paper is to establish two ultra-short-term rolling forecasting models of global horizonal irradiance (GHI) based on Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN) using the observation of a sky imager (TSI-880) and GHI in Dunhuang, China
Nowcasting of GHI is not affected by the cloud motion, it can be used as a verification method for the accuracy of the cloud detection and the significance of the ternary linear regression equation
Summary
Solar photovoltaics (PV) is developing at an unprecedented speed, which is attributed to dramatic cost reductions, technology advancements and government policy support [1,2,3]. According to the IEA (International Renewable Energy Agency) report, global solar PV power generation increased by 22% to 720 TWh in 2019 [4]. Accurate forecasting of solar irradiance is crucial to the dispatch and management of power systems [5]. Further ultra-short-term systems for forecasting highly spatially and temporally resolved solar irradiance in a timeframe of 15 min have been put forward to optimize the operation of solar power plants [10,11,12]. The forecasting of cloud fraction and location is very difficult according to physical and dynamics principles, it is vital to the ultra-short-term forecasting of solar irradiance [17]
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