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.

翻译：随着风能、太阳能等可再生能源渗透率的不断提高，精准的短期功率预测（即功率临近预报）正变得日益重要。本文研究了多模态学习与端到端学习方法，将其作为能源管理系统的中间环节应用于可再生能源功率临近预报。多模态融合了全天空成像与气象传感器数据两种模态的特征，用于预测可再生能源发电功率——这在以往难以通过有效方式实现结合。将组合后的预测值输入可微分的最优潮流模型，模拟能源管理过程。本研究首次将多模态与端到端训练相结合，以最小化预期总系统成本为目标。案例研究基于荷兰真实天空与气象数据对所提方法进行了验证。结果表明，与单模态基线模型相比，所提多模态-端到端模型使系统成本降低了30%。