  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.

Systems and Control;Artificial Intelligence;Machine Learning;Systems and Control;Methodology