Collaborative energy management system

A method of providing collaborative energy management in a multi-zone space, comprising:

• collecting environment data from sensors in the multi-zone space to generate a correlation model that correlates historical environment data with HVAC settings;
• receiving preferences from a plurality of occupants within the multi-zone space;
• generating individualized pricing signals for each of the plurality of occupants wherein each individualized pricing signal is based on a discomfort cost incurred to a set of co-occupants due to a temperature preference of a selected occupant received from the selected occupant and an incremental energy cost to the space to accommodate the preference received from the selected occupant, wherein the discomfort cost is calculated by summing the values of a set of discomfort functions, each function corresponding to an occupant in the set of co-occupants, wherein each discomfort function is a relationship between the corresponding occupant's level of discomfort and an ambient temperature, and wherein each of the discomfort function values used in the discomfort cost results from the inputted preference of the corresponding selected occupant;
• transmitting the individualized pricing signal generated for each occupant to that occupant;
• receiving energy usage goals for the multi-zone space;
• calculating new HVAC settings for the multi-zone space utilizing the correlation model and a consensus algorithm incorporating the individualized pricing signals for each of the plurality of occupants and the energy usage goals;
• and
• applying the HVAC settings to an HVAC system associated with the multi-zone space.

A computer program product stored on a non-transitory computer readable storage medium, which when executed by a computer system, provides collaborative energy management in a multi-zone space, comprising:

• program code that collects environment data from sensors in the multi-zone space to generate a correlation model that correlates historical environment data with HVAC settings;
• program code that receives preferences from a plurality of occupants within the multi-zone space;
• program code that receives energy goals for the multi-zone space;
• program code for generating individualized pricing signals for each of the plurality of occupants, wherein each individualized pricing signal is based on a discomfort cost incurred to a set of co-occupants due to a temperature preference of a selected occupant received from the selected occupant and an incremental energy cost to the space to accommodate the preference received from the selected occupant, wherein the discomfort cost is calculated by summing the values of a set of discomfort functions, each function corresponding to an occupant in the set of co-occupants, wherein each discomfort function is a relationship between the corresponding occupant's level of discomfort and an ambient temperature, and wherein each of the discomfort function values used in the discomfort cost results from the inputted preference of the corresponding selected occupant;
• program code that transmits the individualized pricing signal generated for each occupant to that occupant;
• program code that calculates new HVAC settings for the multi-zone space utilizing the correlation model and a consensus algorithm incorporating the individualized pricing signals for each of the plurality of occupants and the energy goals;
• and program code that outputs the HVAC settings to an HVAC system associated with the multi-zone space for application by the HVAC system.