Predictive building control system and method for optimizing energy use and thermal comfort for a building or network of buildings

A method for controlling temperature in a thermal zone within a building, comprising:

• using a processor, receiving a desired temperature range for the thermal zone;
• determining a forecast ambient temperature value for an external surface of the building proximate the thermal zone;
• using a predictive model for the building, determining set points for a heating, ventilating, and air conditioning (“HVAC”) system associated with the thermal zone that minimize energy use by the building;
• the desired temperature range and the forecast ambient temperature value being inputs to the predictive model;
• the predictive model being trained using respective historical measured value data for at least one of the inputs and by one or more artificial intelligence-based modules;
• controlling the HVAC system with the set points to maintain an actual temperature value of the thermal zone within the desired temperature range for the thermal zone;
• receiving a demand response signal from a utility demand response control system associated with the building and further determining the set points using the demand response signal as one of the inputs to the predictive model, the demand response signal indicating a desired reduction in electric power demand received by the building from a utility grid;
• and
• determining a demand response capacity of the building as a difference between a high electric power demand limit and a low electric power demand limit, wherein the high electric power demand limit and the low electric power demand limit are associated with a lower limit of the desired temperature range and a higher limit of the desired temperature range, and wherein the demand response capacity or a portion thereof is available for bidding into at least one of a frequency response market, an electric power market, and an ancillary services market associated with the utility grid.