Integration of distributed renewable generation (DRG) in distribution networks can be effectively promoted by scheduling flexible resources such as heating

ventilation

and air conditioning (HVAC) loads. However

finding the optimal scheduling for them is not trivial because DRG outputs are highly uncertain. To address this issue

this paper proposes a learning-based joint chance-constrained approach to coordinate HVAC loads with DRG. Unlike cutting-edge works adopting individual chance constraints to manage uncertainties

this paper controls the violation probability of all critical constraints with joint chance constraints (JCCs). This joint manner can explicitly guarantee operational security of the entire system based on operators' preferences. To overcome intractability of JCCs

we first prove that JCCs can be safely approximated by robust constraints with proper uncertainty sets. A famous machine learning algorithm

one-class support vector clustering

is then introduced to construct a small enough polyhedron uncertainty set for these robust constraints. A linear robust counterpart is further developed based on the strong duality to ensure computational efficiency. Numerical results based on various distributed uncertainties confirm the advantages of the proposed model in optimality and feasibility.