Опубликована: Янв. 1, 2023

Solar energy plays an important role in the future system. However, inherent uncertainty of solar brings great difficulties to grid connection and short-term planning dispatching. Deep learning method makes it possible predict with its powerful ability, but huge training process parameter adjustment bring actual deployment. Therefore, this paper proposes a new lightweight multi-modal model for irradiance prediction based on knowledge distillation strategy, which greatly reduces complexity while ensuring acceptable accuracy, facilitating Firstly, teacher inputs Informer framework is built guide student model. Then, constructed obtain same input reduced trainable parameters. The optimal settings loss function ratio are studied. Results show that can reduce parameters inference time by 97.7% 52.5%, respectively. normalized root mean square error 24.87% compared without distillation, verifying effectiveness proposed method. soft uses loss, 0.3, best results structure 3 residual blocks LSTM layers proved be task.

Язык: Английский