A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining (30, 31, 32) a value of an objective function Fij of a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning (33, 34, 35, 36) a likelihood score Li,j to each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting (37, 38) those near-optimal DR strategies with large overall likelihood scores Lj to create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched (39) in real-time for an optimal DR strategy for a given weather pattern.