HVAC controller with regression model to help reduce energy consumption

A controller for an HVAC system of a building, the controller comprising:

• an input for receiving a forecast of an ambient condition outside of the building;
• a memory for storing past monetary costs of operating the HVAC system in conjunction with past operating conditions and defined comfort limits within the building that change with time, wherein the defined comfort limits include an upper comfort limit and a lower comfort limit that each change with time based on when the building is expected to be occupied, such that a region between the upper comfort limit and the lower comfort limit is narrower when the building is expected to be occupied than when the building is expected to be unoccupied;
• an output for setting a set-point of the HVAC system;
• a control unit coupled to the memory, the input and the output, the control unit executing an optimizer for a regression model that uses a risk acceptance profile based on the past operational costs of the HVAC system to maintain temperature setpoints of the HVAC system within the defined comfort limits, the forecast of the ambient condition, the defined comfort limits, in conjunction with the past monetary costs of operating the HVAC system, to produce a profile that includes a plurality of future set-points for the HVAC system that are within the defined comfort limits, wherein each respective future set-point is set to occur at a different pre-defined future time for a different area of the building;
• wherein the risk acceptance profile is defined by:
• f(r)=μ(r,c)+α·σ(r,c), wherein:
• f(r) is the risk acceptance profile;
• r is a generalized temperature profile;
• c is the past operating conditions;
• μ(r,c) is a mean value of a predicted operational cost of the HVAC system;
• σ(r,c) is a standard deviation of the predicted operational cost of the HVAC system;
• and
• α is a risk avoidance parameter;
• wherein the plurality of calculated set-points are forwarded to the HVAC system via the output of the controller;
• and
• wherein a risk acceptance level of the risk acceptance profile is decreased as the regression model is refined;
• and
• wherein the risk acceptance level is based on the risk avoidance parameter.

A method for controlling an HVAC system of a building, the method comprising:

• obtaining a forecast of an ambient temperature outside of the building;
• obtaining a measure of past operational cost of the HVAC system in conjunction with past operating conditions and defined comfort limits within the building that change with time, wherein the past operational cost includes a past monetary cost of operating the HVAC system, and wherein the defined comfort limits include an upper comfort limit and a lower comfort limit that each change with time based on when the building is expected to be occupied, such that a region between the upper comfort limit and the lower comfort limit is narrower when the building is expected to be occupied than when the building is expected to be unoccupied;
• inputting the forecasted ambient temperature, the defined comfort limits, and the measure of the past operational cost of the HVAC system in conjunction with the past operating conditions to a regression model that uses a risk acceptance profile based on the past operational costs of the HVAC system to maintain temperature setpoints of the HVAC system within the defined comfort limits to produce a temperature profile that includes a plurality of future temperature set-points for the HVAC system that are within the defined comfort limits, wherein each respective future temperature set-point is set to occur at a different predefined future time for a different area of the building;
• wherein the risk acceptance profile is defined by:
• f(r)=μ(r,c)+α·σ(r,c), wherein:
• f(r) is the risk acceptance profile;
• r is a generalized temperature profile;
• c is the past operating conditions;
• μ(r,c) is a mean value of a predicted operational cost of the HVAC system;
• σ(r,c) is a standard deviation of the predicted operational cost of the HVAC system;
• and
• α is a risk avoidance parameter;
• outputting the plurality of future temperature set-points to the HVAC system;
• and
• decreasing a risk acceptance level of the risk acceptance profile as the regression model is refined;
• wherein the risk acceptance level is based on the risk avoidance parameter.