Rogiros Tapakis

Monitoring Cloud Motion in Cyprus for Solar Irradiance Prediction

Solar Energy is the feedstock for various applications of renewable energy sources; thus, the knowledge of the intensity of the incident solar irradiance is essential for monitoring the performance of such systems. The major unpredictable factor in defining the solar irradiance and the performance of solar systems is the presence of clouds in the sky. So far, various researchers proposed several models to correlate solar irradiance to cloud coverage and cloud type. The present work describes the development of a simple method for cloud detection and computation of short-term cloud motion. The minimum accuracy of the model was 95% for the prediction of the cloud location seven timesteps in advance with only three cloud images processed. When including the dimensions of the cloud to the accuracy calculation, the minimum accuracy was 88%.

Monitoring Cloud Coverage in Cyprus for Solar Irradiance Prediction

The penetration and acceptance of Renewable Energy Sources has already taken place in our lives. Solar Energy is the feedstock for various applications of Renewable Energy Systems (RES), thus, the knowledge of the intensity of the incident solar irradiance is essential for monitoring the performance of such systems. The only unpredictable factor in defining the solar irradiance and the performance of the systems is clouds. So far, various researchers proposed several models for the estimation of solar irradiance in correlation to cloud coverage and cloud type. The present work describes the development of a simple method for cloud detection and computation of short-term cloud motion using a Nikon D3100 camera with a 18–55 mm VR lens, positioned on a tripod in Limassol, Cyprus. The method used for distinguishing clouds from the sky is the “Red-Blue threshold”. Additionally, the results of the cloud distinction are used to calculate the future position of clouds. The developed methodology will provide a useful tool for researchers that want to focus on the effect of small local clouds on the energy production of their solar RES. The maximum error in our model was 12% for the prediction of the cloud location eight time steps in advance with only two cloud images processed.