Intra-hour direct normal irradiance forecasting through adaptive clear-sky modelling and cloud tracking

Hybrid fossil-solar power cycles possess high solar-to-electric conversion efficiency and the potential to provide stable power output without the need for costly storage systems. However, the power output of the concentrating solar power (CSP) component of the plant can fluctuate in sync with intermittent direct normal irradiance (DNI). For hybrid plants, the controllable fossil-fuel unit can be used to compensate for shortfalls in CSP output during periods of light DNI intermittency or provide the entire plant output during periods of high DNI intermittency. This fossil-fuel unit requires 5-10 min to ramp its power output up or down to perform these balancing functions. Thus, by accurately predicting future DNI, and hence CSP output, the fossil-fuel unit can guarantee stable plant wide power output during periods of intermittency. This paper develops an intra-hour DNI prediction system using a ground-based cloud motion vector (CMV) framework and real-time DNI measurements. This system comprises a clear-sky DNI model and a cloud fraction prediction algorithm. The presented CSM is based on the Ineichen model (Ineichen, 2008), where the model parameters are adaptively estimated from identified clear-sky DNI measurements over a moving window. For the cloud fraction prediction model, this paper presents an enhanced sector-ladder method (Quesada-Ruiz et al., 2014) that uses the weighted mean of circular quantities (Fisher, 1995) and autoregressive filtering to improve cloud flow predictions. Furthermore, a method to forewarn against periods of high DNI intermittency using the generated DNI predictions is presented. The proposed DNI prediction system is evaluated using 37 days of sky-camera images and DNI data collected over the summer of 2014/2015 at the University of Queensland. Over all test days, the adaptive CSM has an average root mean square error of 3.06%, which represents a 19% improvement over a CSM that uses the optimal model parameters from the previous day's data. Additionally, the modifications to the cloud flow prediction algorithm (the sector-ladder method) are shown to improve the cloud velocity prediction accuracy by a factor of seven over a period of visually determined constant cloud velocity. We find the overall prediction accuracy of the DNI prediction system to be statistically similar to the accepted short-term benchmark of persistence; however, it performs more consistently over a range of weather conditions and is able to forewarn against periods of impending intermittency with 93% accuracy. The latency from data collection to prediction is less than 30 s, making the method eminently suitable for real-time applications.