Cloud Movement Forecasting based on Wind Modeled from Satellite Imagery and a Modified Flocking Algorithm

Marius Penteliuc

Photovoltaic (PV) panels are increasingly used for generating power by transforming Sun irradiance into electric output. Transient clouds are absorbing irradiance and passing over a solar farm greatly affects its power production capability. Identification and forecasting of cloud movement are crucial for operators to take appropriate actions to meet energy demand such as starting backup generators and compensating with power reserves. Many methods rely on ground cameras and sensors, or satellite imagery, but they lack granularity, generalization, and assume idealistic linear motion of clouds over long distances. I propose and describe a solution to forecasting short-term cloud movement using only data obtained from satellite imagery without the need for gathering auxiliary information from other sources. It is based on a nature-inspired flocking behavior simulator and produces granular forecasts of cloud positions for the entire scene. It also generates a wind map that covers the scene and is continuously updated with each forecast. The proposed solution provides short-term wind and cloud forecasts of up to several hours while being lightweight and using fewer resources than a compared neural network. The scalability of the implementation is tested and the speed-up values are up to 1000 times.