End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting. (arXiv:2304.07151v1 [eess.SY])

With the increasing penetration of renewable power sources such as wind and
solar, accurate short-term, nowcasting renewable power prediction is becoming
increasingly important. This paper investigates the multi-modal (MM) learning
and end-to-end (E2E) learning for nowcasting renewable power as an intermediate
to energy management systems. MM combines features from all-sky imagery and
meteorological sensor data as two modalities to predict renewable power
generation that otherwise could not be combined effectively. The combined,
predicted values are then input to a differentiable …

arxiv data energy features flow intermediate management multiple paper power prediction renewable renewables sensor solar systems values