Enhancing intra-hour solar irradiation estimation through knowledge distillation and infrared sky images

Recent years have seen increased interest in solar as a popular renewable energy source. The adoption of solar energy is directed towards increasing research interest in the incorporation of solar energy estimation and forecasting in the current system. Solar irradiance is the sun’s energy incident on earth, and is dependent on many atmospheric parameters, mainly decided by clouds. The use of a Ground-based sky imaging system is the best for forecasting and estimating for near-real-time temporal spectrum. However, creating efficient solar estimation models for edge devices is still a relatively unexplored field. This paper captures the essence of bringing edge computing on devices for solar estimation on ground-based infrared sky images by proposing a novel knowledge distillation method for enhancing lightweight CNN-regression models. In our study, we leveraged a MobilenetV2-based Teacher model and transferred the knowledge into a simple student model by introducing a sigmoid-based loss in the Knowledge Distillation algorithm. The proposed solution shows an impressive reduction of the Mean Square Error (MSE) of the model from 3015.63 to 2540.67. This research advances the field of solar irradiance estimation by emphasizing the importance of creating efficient and edge device deployable models in this context.