End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

2023/04/14 by Rushil Vohra, Vohra, Rushil, Ali Rajaei +3 · 2 citations
Computer Science · Engineering · #Computer science #Electric Power System Optimization #Electrical engineering #Energy Load and Power Forecasting #Engineering #Environmental science #FOS: Computer and information sciences #FOS: Electrical engineering #Geography #Machine Learning (cs.LG) #Meteorology #Modal #Nowcasting #Renewable energy #Solar Radiation and Photovoltaics #Systems and Control (eess.SY) #Wind power #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2304.07151

openalex publication_date 2023/04/14 · openalex created_date 2023/04/19 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• Learning-Augmented Power System Operations: A Unified Optimization View
• Structured Differentiable Optimization for Efficient Decision-focused Learning in Power Systems

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