Abstract
Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar unshadowing curve to the mean value of the irradiance predicted by the hydrometeorological institute for that hourly interval. Image processing for cloud detection and localization is performed using a computer vision library and the Java programming language. The algorithm developed in this work leads to improved accuracy and resolution of irradiance prediction for the next hour interval. The predicted irradiance curve can be used as a predicted reference for solar energy production in energy storage system optimization.
Highlights
In this paper, a prediction of solar irradiance was proposed based on a sky image that includes the sky, clouds and their movement, and sun coverage
If the energy storage system consists of different storages with different capacities, efficiency, and response speed, the efficient use of these storages is possible if an accurate forecast of irradiation is available with a maximum of 1 min time sample in a 1-h forecast window
2, PI ( t ) is the estimated sun un-coverage level measured in percentages, PAI (1h) MHS represents predicted average irradiance estimation of direct solar irradiance based on the 1-h time period provided by the meteorological and hydrological service meaW
Summary
A prediction of solar irradiance was proposed based on a sky image that includes the sky, clouds and their movement, and sun coverage. Optimal use of energy storage devices with different efficiency, capacity, and response times can be achieved if the more accurate irradiance prediction for the time interval of the hour is available. Optimal HSV thresholds for best results in cloud edge detection can be determined by an artificial neural network-based system described in our previous work [15] since the sky image often contains different parameters (number of clouds, cloud sizes, and histogram values) and colors that may require different settings for image processing. If the energy storage system consists of different storages with different capacities, efficiency, and response speed, the efficient use of these storages is possible if an accurate forecast of irradiation is available with a maximum of 1 min time sample in a 1-h forecast window For this purpose, it is necessary to obtain a curve of the percentage of the uncovered sun with clouds for 1 h in the future. Master Control Algorithm of Sun Un-Coverage Forecast in a Hybrid Storage System
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
