Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery

Abstract

The atmospheric conditions existing where concentrated solar power plants (CSP) are installed need to be carefully studied. A very important reason for this is because the presence of clouds causes drops in electricity generated from solar energy. Therefore, forecasting the cloud displacement trajectory in real time is one of the functions and tools that CSP operators must develop for plant optimization, and to anticipate drops in solar irradiance. For short forecast of cloud movement (10 min) is enough with describe the cloud advection while for longer forecast (over 15 min), it is necessary to predict both advection and cloud changes. In this paper, we present a model that predict only the cloud advection displacement trajectory for different sky conditions and cloud types at the pixel level, using images obtained from a sky camera, as well as mathematical methods and the Lucas-Kanade method to measure optical flow. In the short term, up to 10 min the future position of the cloud front is predicted with 92% certainty while for 25–30 min, the best predicted precision was 82%.

Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images

“…where I x = dI dx , I y = dI dy , and I t = dI dt are the image intensity differentials. The components of the velocity (V x , V y ) of the image or optical flow are calculated using the Lucas-Kanade method [42], which solves the basic optical flow equations for all the pixels in the neighborhood using a least squares criterion. It assumes that the image content displacement between two close instants is small and approximately constant in the neighborhood of a considered point p. The optical flow methods can be divided into two main classes: sparse optical flow (SOF) and dense optical flow (DOF) and the rainymotion Python library is used to perform these techniques [39].…”

“…This method was used in [41] as a technique for predicting cloud movement using ground-based sky imagery for use in short-term solar forecasting. It accurately tracked the movement of the clouds and predicted the trajectory using the Lucas-Kanade algorithm [42]. To forecast the movement, a linear regression through all the previous locations of each feature is applied.…”

“…Results show that this algorithm is well suited to cloud tracking because the movement of clouds is small between images, and the longer the forecast, the larger the error present. Mondragon et al [42] presented a model that predicts cloud advection displacement for different sky condi-tions and cloud types at the pixel level, using images obtained from a sky camera, as well as mathematical methods and the Lucas-Kanade method to measure optical flow. Zaher and Ghanem [43] also showed that optical flow algorithms have good accuracy for low motion, but this accuracy decreases significantly at high speeds.…”

Nowcasting System Based on Sky Camera Images to Predict the Solar Flux on the Receiver of a Concentrated Solar Plant

“…Sky cameras have been used to characterize the atmosphere from a terrestrial perspective [12,13]. This technology can be installed in any geographical location, providing a hemispheric view of the sky in real time.…”