Method and apparatus for determining a thermal setpoint in a HVAC system

Claim 5

The method according to claim 2, wherein in step (b) the crisp room air temperature value T* is calculated using a center of area approach T*=Σ(w.sub.n Tc.sub.n)/Σw.sub.n where each occupant's thermal comfort feedback is expressed as one of a predefined number of fuzzy sets, Tc n is a predetermined center temperature for set n, w n is the weighing factor corresponding to a percentage of feedback for set n, and n is the number of fuzzy sets.

Claim 6

The method according to claim 2, wherein in step (b) the crisp room air temperature value T* is calculated by determining a centroid approach wherein T*=(∫Tda)/A, where each occupant's thermal comfort feedback is expressed as one of a predefined number of fuzzy sets, each set having a predetermined temperature range which overlaps with an adjacent set, where A represents a sum of areas of the pre defined number of sets, the area of each set being truncated in relation to a percentage of feedback, and ∫Tda represents the integral of the product of the temperature T and differential area da of the truncated sets.

Claim 7

The method according to claim 2, wherein in step (c) the new thermal setpoint is determined from the temperature difference (T d ) using a linear rule.

Claim 8

The method according to claim 2, wherein in step (c) the new thermal setpoint (TS) is determined from the temperature difference (T d ) using fuzzy logic approach, comprising the steps of: (c-1) reading at least one weighing factor (w n ) from a look-up table using the temperature difference (T d ); (c-2) calculating a thermal setpoint (TS) using one of a center of area approach and a centroid approach, wherein: the center of area approach is determined: TS=Σ(w n Td n )/Σw n , and the centroid approach is determined: TS=(∫T d da)/A, where each occupant's thermal comfort feedback is expressed as one of a predefined number of sets, each set having a predetermined temperature range which overlaps with an adjacent set, where A represents a sum of areas of the predefined number of sets, the area of each set being truncated in relation weighing factor w n corresponding to a percentage of occupant feedback set n, ∫Tda representing the integral of the product of the temperature T and differential area da of the truncated sets, Td n is a predetermined center temperature of set n, and n is the number of fuzzy sets.

Claim 11

The method according to claim 7, further wherein the feedback information includes items of information selected from the group consisting of (thermal comfort sensation, draft sensation, humidity sensation, description of occupant activity, and description of clothing), and in step (c) the defuzzification process uses at least one of a lookup table, center of area approach and centroid approach.

Claim 12

A control system for adjusting a thermostat setpoint for controlling the temperature of a space controlled by an HVAC system using occupant feedback comprising: a software interface for entering occupant feedback; means for transmitting the occupant feedback; a CPU for receiving feedback information from said transmitting means, wherein said CPU calculates an adjusted thermostat setpoint using a previous thermostat setpoint and the occupant feedback information.

Claim 13

A control system for adjusting a thermostat setpoint for controlling the temperature of a space controlled by an HVAC system using occupant feedback comprising: a software interface for entering occupant feedback; means for transmitting the occupant feedback; a CPU for receiving feedback information from said transmitting means, wherein said CPU calculates an adjusted thermostat setpoint using a previous thermostat setpoint and the occupant feedback information.

Claim 14

The control system of claim 9, wherein said transmitting means includes at least one of an Internet and Intranet communications network.

Claim 15

The control system of claim 10, wherein said software interface is a web page on the Internet, and the occupant feedback includes thermal comfort information.

Claim 16

A control system for adjusting a PMV sensor setpoint for controlling the temperature of a space controlled by an HVAC system using occupant feedback comprising: a software interface for entering occupant feedback; means for transmitting the occupant feedback; a CPU for receiving feedback information from said transmitting means, wherein said CPU calculates an adjusted PMV setpoint using a previous PMV setpoint and the occupant feedback information.

Claim 17

A control system for adjusting a PMV sensor setpoint for controlling the temperature of a space controlled by an HVAC system using occupant feedback comprising: a software interface for entering occupant feedback; means for transmitting the occupant feedback; a CPU for receiving feedback information from said transmitting means, wherein said CPU calculates an adjusted PMV setpoint using a previous PMV setpoint and the occupant feedback information.

Claim 18

The control system of claim 12, wherein said transmitting means includes at least one of an Internet and Intranet communications network.

Claim 19

The control system of claim 13, wherein said software interface is a web based interface on the Internet, and the occupant feedback includes thermal comfort information.

Claim 20

An adaptive method for predicting a setpoint for controlling the temperature of a space controlled by an HVAC system using historical data, comprising the steps: (a) compiling historical data including setpoint data and identification data identifying prevailing characteristics when said setpoint data was determined, wherein said setpoint data includes at least one of thermal setpoint and PMV setpoint data, and said identification data includes time of day, day of week, outside air temperature, and outside humidity; (b) determining a regression relationship between each said item of setpoint data and its associated identification information using regression analysis; and (c) predicting a thermal setpoint in real-time using said regression relationship based on input information including time of day, day of week, outside air temperature, and outside humidity.

Claim 21

An adaptive method for predicting a setpoint for controlling the temperature of a space controlled by an HVAC system using historical data, comprising the steps: (a) compiling historical data including setpoint data and identification data identifying prevailing characteristics when said setpoint data was determined, wherein said setpoint data includes at least one of thermal setpoint and PMV setpoint data, and said identification data includes time of day, day of week, outside air temperature, and outside humidity; (b) determining a regression relationship between each said item of setpoint data and its associated identification information using regression analysis; and (c) predicting a thermal setpoint in real-time using said regression relationship based on input information including time of day, day of week, outside air temperature, and outside humidity.

Claim 22

The adaptive method according to claim 15, wherein said regression relationship in step (b) is determined using a general regression neural network.

Claim 23

The adaptive method according to claim 15, wherein the historical setpoint data was determined using fuzzy feedback received from occupants of a given heating/cooling zone.