SYSTEM AND METHOD FOR ESTIMATING TEMPERATURE DRIFT AND DRIVE CURVES

• US 20080099570A1
• Filed: 10/04/2006
• Published: 05/01/2008
• Est. Priority Date: 10/04/2006
• Status: Abandoned Application

• Current Assignee: Telkonet Incorporated
• Original Assignee: Telkonet Incorporated
• Inventors: Pitt, Ronald L., Krebs, Steve, Zirpoli, Robert
• Application Number:
  US11/538,454
• Publication Number:
  US 20080099570A1
• Time in Patent Office: Days
• US Class Current: 236/46.00R
• CPC Class Codes: F23N 5/203 using electronic means
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/526 giving audible indications
• CPC Class Codes: F24F 11/56 Remote control
• CPC Class Codes: F24F 11/61 using timers
• CPC Class Codes: F24F 11/64 using pre-stored data
• CPC Class Codes: F24F 2110/10 Temperature
• CPC Class Codes: F24F 2110/50 Air quality properties
• CPC Class Codes: F24F 2120/00 Control inputs relating to users or occupants
• CPC Class Codes: F24F 2120/10 Occupancy
• CPC Class Codes: F24F 2130/30 Artificial light
• CPC Class Codes: Y02B 30/70 Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating

Abstract

A method for estimating temperature drift and drive curves comprises the steps of: (1) obtaining a plurality of temperature data samples over a period of time, the period of time corresponding to one of a drive operation or a drift period of an environmental control unit; (2) if the period of time corresponds to a drive operation, calculating a plurality of drive curves, each curve calculated using a different data sample subset; (3) else if the period of time corresponds to a drift period, calculating a plurality of drift curves, each curve calculated using a different data sample subset; (4) determining which curve best fits the data samples; and (5) using the best fit curve to predict one of a future temperature at a known time or a future time at which a known temperature will be reached.

Claims

1. A method for estimating temperature drift and drive curves, the method comprising:

obtaining a plurality of temperature data samples over a period of time, the period of time corresponding to one of a drive operation or a drift period of an environmental control unit;

if the period of time corresponds to a drive operation, calculating a plurality of drive curves, each curve calculated using a different data sample subset;

else if the period of time corresponds to a drift period, calculating a plurality of drift curves, each curve calculated using a different data sample subset;

determining which curve best fits the data samples; and

using the best fit curve to predict one of a future temperature at a known time or a future time at which a known temperature will be reached.

2. The method of claim 1, wherein the curves are described by equation T=C+Ae⁻

t/B, in which T is temperature, t is time, and A, B, and C are constants.

3. The method of claim 2, wherein constants A, B, and C are calculated using equations:

$B=\frac{\left(t_{\mathrm{mid}}-t_{\mathrm{start}}\right)}{\ln <br><br>\left\{\left(T_{\mathrm{mid}}-T_{\mathrm{start}}\right)/\left(T_{\mathrm{end}}-T_{\mathrm{mid}}\right)\right\}};<br><br>$ $A=\frac{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left(T_n-T_{\mathrm{avg}}\right)}^2}{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left({\mathrm{<br><br>}}^{-\ln /B}-t_{\mathrm{avg}}\right)}^2};<br><br>\mathrm{and}<br><br><br><br>C=T_{\mathrm{avg}}-A<br><br><br><br>t_{\mathrm{avg}};<br><br>$

in which T_start is a first temperature sample at time t_start;

T_end is a last temperature sample at time t_end;

T_mid is a midpoint temperature sample at time t_mid;

T_avg is an average temperature sample across all the samples;

t_avg is an average of times at which all the samples were taken; and

T_n is a temperature at time t_n.

4. The method of claim 3, wherein determining which curve best fits the data samples comprises:

calculating, for each curve, theoretical temperature values for each sample time in the data set using equation T=C+Ae<sup>−

t/B</sup>;

calculating a fit value for each curve using equation

$\mathrm{Fit}=A^2<br><br>\left\{\frac{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left({\mathrm{<br><br>}}^{-\ln /B}-t_{\mathrm{avg}}\right)}^2}{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left(T_n-T_{\mathrm{avg}}\right)}^2}\right\};<br><br>$

and

determining which curve has the highest calculated fit value.

5. The method of claim 4, wherein using the best fit curve to predict a future temperature or a future time comprises using the curve having the highest calculated fit value.

6. An apparatus for governing levels of an environmental attribute of a room by controlling operation of an environmental control unit for affecting the levels of the attribute of the room, the apparatus including an interface coupled to the environmental control unit to control the operation thereof by control signals, the apparatus comprising:

a controller including a processor coupled to a memory, the memory storing an environmental control program including program instructions for controlling the operation of the environmental control unit by generating the control signals and further storing data; and

an environmental attribute sensor coupled to the controller for providing, at any given time, a signal representing the level of the attribute within the room;

wherein the controller is configured to obtain a plurality of attribute data samples from the environmental attribute sensor over a period of time and to store the data samples, the period of time corresponding to one of a drive operation or a drift period of the environmental control unit;

wherein, if the period of time corresponds to a drive operation, the controller is further configured to calculate a plurality of drive curves, each curve calculated using a different data sample subset;

else wherein, if the period of time corresponds to a drift period, the controller is further configured to calculate a plurality of drift curves, each curve calculated using a different data sample subset;

wherein the controller is further configured to determine which curve best fits the data samples;

wherein the controller is further configured to use the best fit curve to predict one of a future attribute level at a known time or a future time at which a known attribute level will be reached; and

wherein the controller is further configured to use the predicted future attribute level or the predicted future time to control the operation of the environmental control unit.

7. The apparatus of claim 6, wherein the attribute of the room is temperature;

wherein the environmental attribute sensor comprises a temperature sensor; and

wherein the environmental control unit includes at least one of a heating unit, an air conditioning unit and a ventilating unit.

8. The apparatus of claim 7, wherein the curves are described by equation T=C+Ae⁻

t/B, in which T is temperature, t is time, and A, B, and C are constants.

9. The apparatus of claim 8, wherein the controller calculates constants A, B, and C using equations:

$B=\frac{\left(t_{\mathrm{mid}}-t_{\mathrm{start}}\right)}{\ln <br><br>\left\{\left(T_{\mathrm{mid}}-T_{\mathrm{start}}\right)/\left(T_{\mathrm{end}}-T_{\mathrm{mid}}\right)\right\}};<br><br>$ $A=\frac{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left(T_n-T_{\mathrm{avg}}\right)}^2}{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left({\mathrm{<br><br>}}^{-\ln /B}-t_{\mathrm{avg}}\right)}^2};<br><br>\mathrm{and}<br><br><br><br>C=T_{\mathrm{avg}}-A<br><br><br><br>t_{\mathrm{avg}};<br><br>$

in which T_start is a first temperature sample at time t_start;

T_end is a last temperature sample at time t_end;

T_mid is a midpoint temperature sample at time t_mid;

T_avg is an average temperature sample across all the samples;

t_avg is an average of times at which all the samples were taken; and

T_n is a temperature at time t_n.

10. The apparatus of claim 9, wherein the controller determines which curve best fits the data samples by:

calculating, for each curve, theoretical temperature values for each sample time in the data set using equation T=C+Ae<sup>−

t/B</sup>;

calculating a fit value for each curve using equation

$\mathrm{Fit}=A^2<br><br>\left\{\frac{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left({\mathrm{<br><br>}}^{-\ln /B}-t_{\mathrm{avg}}\right)}^2}{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left(T_n-T_{\mathrm{avg}}\right)}^2}\right\};<br><br>$

and

determining which curve has the highest calculated fit value.

11. The apparatus of claim 10, wherein the controller uses the best fit curve to predict a future temperature or a future time by using the curve having the highest calculated fit value.

12. An apparatus for governing levels of an environmental attribute of a room by controlling operation of an environmental control unit for affecting the levels of the attribute of the room, the apparatus including an interface coupled to the environmental control unit to controlling the operation thereof by control signals, the apparatus comprising:

a controller including a processor coupled to a memory, the memory storing an environmental control program including program instructions for controlling the operation of the environmental control unit by generating the control signals and further storing data including a drift relationship, a drive relationship, a predetermined set point representing a predetermined level of the attribute, a predetermined maximum range relative to the set point, a predetermined minimum range relative to the set point and a predetermined maximum recovery time, the controller further including a timer coupled to the processor for timing events related to the environmental control, the events including generation of the control signals and reception of signals representing the levels within the room;

an input device coupled to the controller for inputting at least one of the predetermined set point, maximum range, minimum range and recovery time;

an environmental attribute sensor coupled to the controller for providing, at any given time, one the signal representing the level of the attribute within the room; and

an occupancy sensor coupled to the controller for determining whether the room is occupied, and for sending a the control signal to the controller to take one of a first action and a second action, the first action being taken if the room is unoccupied to allow the level in the room to drift towards an ambient level of the attribute in a region adjacent the room and the second action being taken if the room is occupied to operate the environmental control unit to drive the attribute level in the room away from the ambient level;

wherein the environmental control program includes instructions to allow the drift only to the maximum range when the space is unoccupied, and then, when reoccupied, to activate the environmental control unit for driving the attribute level in the room towards a target level of the attribute within the minimum range of the set point;

wherein the maximum range is limited such that an amount of drive time for the environmental control unit to drive the attribute level in the room from the maximum range to the target level of the attribute is no greater than the predetermined maximum recovery time;

wherein the controller is configured to obtain a plurality of attribute data samples from the environmental attribute sensor over at least a first period of time and a second period of time, the first period of time corresponding to a drive operation of the environmental control unit, the second period of time corresponding to a drift period of the environmental control unit;

wherein the controller is further configured to determine the drive relationship by calculating a plurality of drive curves, each drive curve calculated using a different data sample subset and determining which drive curve best fits the data samples; and

wherein the controller is further configured to determine the drift relationship by calculating a plurality of drift curves, each drift curve calculated using a different data sample subset and determining which drift curve best fits the data samples.

13. The apparatus of claim 12, wherein:

the attribute of the room is temperature;

the environmental attribute sensor comprises a temperature sensor; and

the environmental control unit includes at least one of a heating unit, an air conditioning unit and a ventilating unit.

14. The apparatus of claim 13, wherein the drive and drift curves are described by equation T=C+Ae⁻

t/B, in which T is temperature, t is time, and A, B, and C are constants.

15. The apparatus of claim 14, wherein the controller calculates constants A, B, and C using equations:

$B=\frac{\left(t_{\mathrm{mid}}-t_{\mathrm{start}}\right)}{\ln <br><br>\left\{\left(T_{\mathrm{mid}}-T_{\mathrm{start}}\right)/\left(T_{\mathrm{end}}-T_{\mathrm{mid}}\right)\right\}};<br><br>$ $A=\frac{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left(T_n-T_{\mathrm{avg}}\right)}^2}{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left({\mathrm{<br><br>}}^{-\ln /B}-t_{\mathrm{avg}}\right)}^2};<br><br>\mathrm{and}<br><br><br><br>C=T_{\mathrm{avg}}-A<br><br><br><br>t_{\mathrm{avg}};<br><br>$

in which T_start is a first temperature sample at time t_start;

T_end is a last temperature sample at time t_end;

T_mid is a midpoint temperature sample at time t_mid;

T_avg is an average temperature sample across all the samples;

t_avg is an average of times at which all the samples were taken; and

T_n is a temperature at time t_n.

16. The apparatus of claim 15, wherein the controller determines which drive curve and which drift curve best fits the data samples by:

calculating, for each drive curve and each drift curve, theoretical temperature values for each sample time in the data set using equation T=C+Ae<sup>−

t/B</sup>;

calculating a fit value for each drive curve and each drift curve using equation

$\mathrm{Fit}=A^2<br><br>\left\{\frac{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left({\mathrm{<br><br>}}^{-\ln /B}-t_{\mathrm{avg}}\right)}^2}{\underset{n=\mathrm{start}}{\overset{\mathrm{end}}{\sum <br><br>}}<br><br>{\left(T_n-T_{\mathrm{avg}}\right)}^2}\right\};<br><br>$

and

determining which drive curve and which drift curve has the highest calculated fit value.