This study investigates whether thermal storage systems can be controlled effectively in situations where cooling loads, non-cooling electrical loads, weather information, as well as the cost of electricity are uncertain and have to be predicted. The analysis shows that the reduction in achievable utility cost-savings is small when relying on real-time pricing (RTP) electricity rates that are made available by the utility only 1 h ahead instead of an entire day-ahead. Real-time pricing rate data from two utilities, one in the South and one on the West Coast of the United States, were used in both a day-ahead (24 h rate certainty) and an hour-ahead (1 h rate certainty) fashion. Using measured data for a hotel and an office building in the United States, predictive optimal control strategies delivered greatly superior utility cost-saving performance compared to conventional partial-storage thermal energy storage strategies, even with inaccurate forecasts. The more accurate the prediction becomes, the greater will be the cost-saving performance of cool storage systems under predictive optimal control, and the smaller will be the impact associated with uncertain RTP rates in the hour-ahead RTP tariff case. Consequently, uncertain electrical utility rates do not imperil the superior cost-saving benefits of cool storage when governed by predictive optimal control.