Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation. This issue has driven the necessity of widely exploiting advanced predictive control techniques under uncertainty to ensure long-term economics and decarbonization. In this paper, we propose a real-time uncertainty-aware energy dispatch framework, which is composed of two key elements: (i) A hybrid forecast-and-optimize sequential task, integrating deep learning-based forecasting and stochastic optimization, where these two stages are connected by the uncertainty estimation at multiple temporal resolutions; (ii) An efficient online data augmentation scheme, jointly involving model pre-training and online fine-tuning stages. In this way, the proposed framework is capable to rapidly adapt to the real-time data distribution, as well as to target on uncertainties caused by data drift, model discrepancy and environment perturbations in the control process, and finally to realize an optimal and robust dispatch solution. The proposed framework won the championship in CityLearn Challenge 2022, which provided an influential opportunity to investigate the potential of AI application in the energy domain. In addition, comprehensive experiments are conducted to interpret its effectiveness in the real-life scenario of smart building energy management.

翻译：聚合电力系统中的分布式能源资源会显著增加不确定性，尤其是由可再生能源发电波动引起的不确定性。这一问题促使人们必须广泛开发基于不确定性的先进预测控制技术，以确保长期的经济性与脱碳目标。本文提出了一种实时感知不确定性的能量调度框架，其包含两个关键要素：（i）一种混合预测-优化序列任务，将基于深度学习的预测与随机优化相结合，这两个阶段通过多时间分辨率的不确定性估计连接起来；（ii）一种高效的在线数据增强方案，联合涉及模型预训练与在线微调阶段。通过这种方式，所提出的框架能够快速适应实时数据分布，并针对控制过程中由数据漂移、模型差异和环境扰动引起的不确定性，最终实现最优且鲁棒的能量调度方案。该框架在2022年CityLearn挑战赛中荣获冠军，这一赛事为探索人工智能在能源领域应用的潜力提供了重要契机。此外，通过综合实验验证了其在智能建筑能量管理实际场景中的有效性。