End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting
This addresses the problem of optimizing energy management for renewable power integration, though it is incremental as it combines existing techniques in a novel way.
The paper tackled short-term renewable power prediction by combining multi-modal learning (all-sky imagery and meteorological data) with end-to-end training to minimize system costs, resulting in a 30% reduction in system cost compared to uni-modal baselines.
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 optimal power flow (OPF) formulation simulating the energy management. For the first time, MM is combined with E2E training of the model that minimises the expected total system cost. The case study tests the proposed methodology on the real sky and meteorological data from the Netherlands. In our study, the proposed MM-E2E model reduced system cost by 30% compared to uni-modal baselines.