CA2289237C - Method and apparatus for determining a thermal setpoint in a hvac system - Google Patents
Method and apparatus for determining a thermal setpoint in a hvac system Download PDFInfo
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- CA2289237C CA2289237C CA002289237A CA2289237A CA2289237C CA 2289237 C CA2289237 C CA 2289237C CA 002289237 A CA002289237 A CA 002289237A CA 2289237 A CA2289237 A CA 2289237A CA 2289237 C CA2289237 C CA 2289237C
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Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/1919—Control of temperature characterised by the use of electric means characterised by the type of controller
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/20—Feedback from users
Abstract
The present invention discloses methods for determining setpoint information in a HVAC system. Setpoint values are determined using occupant feedback provided by individual occupants over at least one of an Internet or Intranet communications network, According to a first aspect of the invention a setpoint is determined using fuzzy logic. According to a second aspect, historical setpoint date determined using occupant feedback is used to develop a mural network for predicting setpoint values.
Description
~
2 A THERMAL SETPOINT IN A HVAC SYSTEM
3 FIELD OF THE INVENTION
4 The present invention relates generally to the licld of HVAC (heating, ventilating, and air-conditioning) systems. In particular the present invention discloses 6 an improved method for updating a thermal setpoint in 1-1 VAC system.
According to a 7 first aspect ofthe present invention, occupant feedback is used in determining the sctpoint 8 for a thermostat or a PMV sensor. Moreover, according to n sccmul aspect of tl~c pr~:wnt A
9 invention, a setpoint is predicted using an adaptation algoritlmn developed using I~istorical data.
12 The primary function of a HVAC system is to provide thermal comfort for 13 building occupants. Paradoxically, conventional HVAC control systems do not solicit 14 feedback from more than one building occupant. Typically, a single thermostat or a temperature sensor is the only feedback mechanism for determining thermal comfort for 16 a given heating zone. Moreover, the thermostat or temperature sensor may be influenced 17 by the micro-climate around it and thus may not always rcilcct the human sense of 18 . comfort. In such a system, the location of the thermostat becomes extremely important 19 in correlating the thermostat sensed temperature with the actual room air temperature.
_, t 1 The human level of comfort has a strong correlation with the room air temperature as 2 demonstrated by early research (Fountain, M.E., and C. Huizenga., "A Thermal Sensation 3 Prediction Tool for Use by the Profession," ASHRAE Transaction, 1997, Vol.
103, Part 4 2). See, e.g., FIG. 1.
On any given day the thermostat setpoint may need adjustment depending 6 on prevailing outdoor and indoor load conditions. For example, a mild day may require 7 less heating whereas an unseasonably warm day may require lowering of the thermal 8 setpoint to provide additional cooling.
9 Building occupants can react to the changing environment by adjusting the thermostat setpoint. However, in most large buildings a single thermostat serves an area 11 or zone occupied by multiple occupants. Hence, it is impractical to change the thermostat 12 setpoint manually based on the comfort level of each individual occupant.
Moreover, 13 occupant access to the thermostat is often restricted to prevent abuse. The end result is 14 ~ that in an existing building control system, the building occupants have limited opportunities to provide feedback.
16 In response to these problems, one object of the present invention is to 17 provide an improved mechanism for determining a thermal setpoint using feedback from 18 multiple occupants of a given heating/cooling area.
19 More particularly, an object of the present invention is to provide an improved HVAC system wherein many occupana may be provided with an interface for 21 communicating thermal comfort feedback.
i i _~
1 Another object of the present invention is to utilize an existing 2 communications network such as Internet or Intranet to cant' the feedback information 3 to the HVAC controller in the improved system.
4 Another object of the present invention is to collect setpoint data, determined using occupant feedback, for subsequent use in an adaptive setpoint model 6 for predicting a setpoint in the improved system.
7 Yet another object of the present invention is to provide a predicted setpoint 8 using a neural network and historical setpoint data in the improved system.
9 These and other objects of the present invention are discussed or will be apparent from the following detailed description of the invention, while referring to the 11 attached drawings.
13 The ability for individual occupants to provide direct feedback to the 14 building control system would be extremely beneficial in providing thermal comfort.
1 S According to one aspect of the present invention, an existing Intranet and/or Internet 16 communications network is used to convey thermal comfort feedback from individual 17 occupants to the HVAC system. Notably, the present invention provides a practical 18 method for any number of occupants to provide thermal comfort feedback to the H VAC
19 system.
' Moreover, since the perception of comfort is subjective, it lends itself to 21 being expressed in terms of fuzzy expression (i.e., too warm or cold).
Fuzzy set theory i i 1 can therefore be utilized to convert the occupant feedback (fuzzy, subjective input) into 2 a crisp output used by the building control system in updating the thermostat setpoint.
4 FIGURE 1 is a graph showing human level of comfort in relation to room air temperature;
G FIG. 2 shows fuzzy sets used to calculate room air temperature according 7 to the present invention;
8 FIG. 3 shows an intersection of two fuzzy sets;
9 FIGs. 4A and 4B show a truncated view of the intersection of FIG. 3;
FIG. 5 is a graph showing a linear rule used to determine the new thermal 1'1 setpoint according to a first embodiment;
12 FIGS. 6A and 6B are fuzzy sets used to determine the new thermal setpoint 13 according to a first embodiment;
14 FIG. 7A is a flowchart of the steps used to determine the new thermal setpoint according to a first embodiment;
16 FIG. 7B is a diagram of the apparatus for adjusting a thermal setpoint using 17 occupant feedback according to the present invention;
18 FIGS. 8 shows fuzzy sets used to calculate PMV according to a second 19 embodiment of the present invention;
FIG. 9 is a schematic diagram of the process for calculating PMV;
v . _ ~ ~
1 FIGS. l0A-lOC are graphs correlating air temperature with radiant air 2 temperature;
3 FIG. 11 shows fuzzy sets used to calculate air velocity according to the 4 present invention;
FIG. 12 shows fuzzy sets used to calculate humidity according to the 6 present invention;
7 FIG. 13 is a schematic view of an artificial neural network according to a 8 third embodiment of the present invention; and 9 FIG. 14 shows the general regression neural network architecture according to the third embodiment of the present invention.
11 DESC=RIPTION OF THE PREFERRED EMBOD1M1?N'1' 12 The relationship between fuzzy expression of human comfort and actual 13 room air temperature is well established. See, e.g. Fountain, M.E., and C.
Huizenga., "A
14 Thermal Sensation Prediction Tool for Use by the Profession," ASHRAE
Transaction, 1997, Vol. 103, Part 2, and Berglund, L., "Mathematical Models for Predicting the 16 Thermal Comfort Response of Building Occupants," ASHRAE Transaction, 1978, Part 17 1. A fuzzy set demonstrating such a relationship is shown in FIG. 2.
18 The subjective occupant expression of thermal comfort is represented in 19 FIG. 2 as a collection of fuzzy sets (i.e., cold 10, cool 12, slightly cool 14, neutral 16, slightly warm 18, warm 20, and hot 22). Moreover, a value of a temperature may have 21 a zero membership (weighting factor of 0), a full membership (weighting factor of 1 ) or
According to a 7 first aspect ofthe present invention, occupant feedback is used in determining the sctpoint 8 for a thermostat or a PMV sensor. Moreover, according to n sccmul aspect of tl~c pr~:wnt A
9 invention, a setpoint is predicted using an adaptation algoritlmn developed using I~istorical data.
12 The primary function of a HVAC system is to provide thermal comfort for 13 building occupants. Paradoxically, conventional HVAC control systems do not solicit 14 feedback from more than one building occupant. Typically, a single thermostat or a temperature sensor is the only feedback mechanism for determining thermal comfort for 16 a given heating zone. Moreover, the thermostat or temperature sensor may be influenced 17 by the micro-climate around it and thus may not always rcilcct the human sense of 18 . comfort. In such a system, the location of the thermostat becomes extremely important 19 in correlating the thermostat sensed temperature with the actual room air temperature.
_, t 1 The human level of comfort has a strong correlation with the room air temperature as 2 demonstrated by early research (Fountain, M.E., and C. Huizenga., "A Thermal Sensation 3 Prediction Tool for Use by the Profession," ASHRAE Transaction, 1997, Vol.
103, Part 4 2). See, e.g., FIG. 1.
On any given day the thermostat setpoint may need adjustment depending 6 on prevailing outdoor and indoor load conditions. For example, a mild day may require 7 less heating whereas an unseasonably warm day may require lowering of the thermal 8 setpoint to provide additional cooling.
9 Building occupants can react to the changing environment by adjusting the thermostat setpoint. However, in most large buildings a single thermostat serves an area 11 or zone occupied by multiple occupants. Hence, it is impractical to change the thermostat 12 setpoint manually based on the comfort level of each individual occupant.
Moreover, 13 occupant access to the thermostat is often restricted to prevent abuse. The end result is 14 ~ that in an existing building control system, the building occupants have limited opportunities to provide feedback.
16 In response to these problems, one object of the present invention is to 17 provide an improved mechanism for determining a thermal setpoint using feedback from 18 multiple occupants of a given heating/cooling area.
19 More particularly, an object of the present invention is to provide an improved HVAC system wherein many occupana may be provided with an interface for 21 communicating thermal comfort feedback.
i i _~
1 Another object of the present invention is to utilize an existing 2 communications network such as Internet or Intranet to cant' the feedback information 3 to the HVAC controller in the improved system.
4 Another object of the present invention is to collect setpoint data, determined using occupant feedback, for subsequent use in an adaptive setpoint model 6 for predicting a setpoint in the improved system.
7 Yet another object of the present invention is to provide a predicted setpoint 8 using a neural network and historical setpoint data in the improved system.
9 These and other objects of the present invention are discussed or will be apparent from the following detailed description of the invention, while referring to the 11 attached drawings.
13 The ability for individual occupants to provide direct feedback to the 14 building control system would be extremely beneficial in providing thermal comfort.
1 S According to one aspect of the present invention, an existing Intranet and/or Internet 16 communications network is used to convey thermal comfort feedback from individual 17 occupants to the HVAC system. Notably, the present invention provides a practical 18 method for any number of occupants to provide thermal comfort feedback to the H VAC
19 system.
' Moreover, since the perception of comfort is subjective, it lends itself to 21 being expressed in terms of fuzzy expression (i.e., too warm or cold).
Fuzzy set theory i i 1 can therefore be utilized to convert the occupant feedback (fuzzy, subjective input) into 2 a crisp output used by the building control system in updating the thermostat setpoint.
4 FIGURE 1 is a graph showing human level of comfort in relation to room air temperature;
G FIG. 2 shows fuzzy sets used to calculate room air temperature according 7 to the present invention;
8 FIG. 3 shows an intersection of two fuzzy sets;
9 FIGs. 4A and 4B show a truncated view of the intersection of FIG. 3;
FIG. 5 is a graph showing a linear rule used to determine the new thermal 1'1 setpoint according to a first embodiment;
12 FIGS. 6A and 6B are fuzzy sets used to determine the new thermal setpoint 13 according to a first embodiment;
14 FIG. 7A is a flowchart of the steps used to determine the new thermal setpoint according to a first embodiment;
16 FIG. 7B is a diagram of the apparatus for adjusting a thermal setpoint using 17 occupant feedback according to the present invention;
18 FIGS. 8 shows fuzzy sets used to calculate PMV according to a second 19 embodiment of the present invention;
FIG. 9 is a schematic diagram of the process for calculating PMV;
v . _ ~ ~
1 FIGS. l0A-lOC are graphs correlating air temperature with radiant air 2 temperature;
3 FIG. 11 shows fuzzy sets used to calculate air velocity according to the 4 present invention;
FIG. 12 shows fuzzy sets used to calculate humidity according to the 6 present invention;
7 FIG. 13 is a schematic view of an artificial neural network according to a 8 third embodiment of the present invention; and 9 FIG. 14 shows the general regression neural network architecture according to the third embodiment of the present invention.
11 DESC=RIPTION OF THE PREFERRED EMBOD1M1?N'1' 12 The relationship between fuzzy expression of human comfort and actual 13 room air temperature is well established. See, e.g. Fountain, M.E., and C.
Huizenga., "A
14 Thermal Sensation Prediction Tool for Use by the Profession," ASHRAE
Transaction, 1997, Vol. 103, Part 2, and Berglund, L., "Mathematical Models for Predicting the 16 Thermal Comfort Response of Building Occupants," ASHRAE Transaction, 1978, Part 17 1. A fuzzy set demonstrating such a relationship is shown in FIG. 2.
18 The subjective occupant expression of thermal comfort is represented in 19 FIG. 2 as a collection of fuzzy sets (i.e., cold 10, cool 12, slightly cool 14, neutral 16, slightly warm 18, warm 20, and hot 22). Moreover, a value of a temperature may have 21 a zero membership (weighting factor of 0), a full membership (weighting factor of 1 ) or
5 1 a partial membership (weighting factor between 0 and I ). A temperature may 2 simultaneously belong to two adjacent sets. As shown in FIG. 2, a temperature of seventy 3 degrees falls within the intersection of the cool set 12 and the slightly cool set 14.
4 The weighing factor for each fuzzy set represents a percentage of user's responding. By manner of illustration, FIG. 3 depicts a situation where 75% of the user's
4 The weighing factor for each fuzzy set represents a percentage of user's responding. By manner of illustration, FIG. 3 depicts a situation where 75% of the user's
6 felt slightly cool, and the remaining 25% felt cool. Hence, the respective weights of the
7 cool 12 and slightly cool 14 sets are 0.75 and 0.25. The fuzzy sets can be used to convert
8 the user's subjective feedback into a crisp room air temperature value using a method
9 hereinafter termed defuzzification. Several different methods of defuzzification are known. Two popular methods are the center of area method and the centroid method.
11 Each of these methods will be understood from the following example.
12 . According to the center or area method, the crisp room air temperature 'I'' 13 is given by equation 1:
(w~TC~) 14 T*- ~ (1) n wn 1 S where, w" is the weighting factor for set n, Tc" is the center temperature of set n, and n is 16 the number of sets.
17 Thus, for the sets shown in FIG. 3, the value T' is [(.75 ~ Tc,) + (.25 ~
Tc2)]
18 / (.75 + .25). As shown in FIG. 3, the Tc,, the center temperature of the slightly cool set 19 is 71 degrees and Te2, the center temperature of the cool set is G9 degrees and Tc2. Thus, the value T~ is [(.75 ~ 71= 53.25) + (.25 ~ 69=17.25)] / ( 1 ) = 70.5 degrees.
1 According to the centroid method, an area is created by joining two 2 truncated areas formed by the intersection of the weighting factor lines and the two 3 triangles representing the two intersecting sets (cool and slightly cool).
By manner of 4 illustration, FIG. 4A depicts an area 24 created by joining the two truncated areas 12 and 14. The crisp room air temperature T' is given by the centroid of the area parallel to the 6 y-axis. In this case, the expression for the room air temperature is given by equation 2:
7 T'=(fTda)/A (2) 8 The integral of the product of the temperature T and differential area da can 9 be calculated by splitting the area under the curve into smaller areas comprising rectangles and right triangles. By manner of illustration, FIG. 4B shows the area under 11 the curve of FIG. 4A broken into six sub-areas. Areas 1 and 4 are right triangles having 12 a positive slope. The area A of a triangle is (base ~ hcight)/2. For a right triangle having 13 a positive slope (Area 1 and Area 4) f Tda = bh2/6. For right triangle having a negative 14 slope (Area 6), the value of the integral f Tda = bh2/3. Further, the area of the rectangular areas Area 2, 3 and 5 is (base ~ height), and the value of the integral f Tda = bh2.
16 Accordingly, for the set shown in FIG. 4B, the value T' is 70.36 °F
using 17 the centroid method. Once the crisp value of the room air temperature T' is known, a 18 simple rule can be developed to adjust the setpoint based on the temperature difference 19 . (T~ between the perceived room air temperature (T~m) and the current setpoint (TS°,~~~~), Td = T~°°", - T~~~",c. A linear rule relating the setpoint to the temperature difference (Td) 21 is shown, for example, in FIG. 5. Notably, the linear rule specifies slight changes in the 1 thermostat setpoint in response to the temperature difference (Td) to avoid 2 overcompensating for a given temperature difference.
3 Further, as shown in Fig. 5, there is an inverse correlation between the 4 change in thermostat set point and the error between the room temperature and current thermostat set point. When the error is negative, i.e., the room temperature is lower than 6 the set point, the change in thermostat set point is positive (provide heating). In contrast, 7 when the error is positive i.e., the room temperature is wanner than the set point, the 8 change in thermostat set point is negative (provide cooling).
9 Alternatively, fuzzy logic can be used in place of the linear rule.
Specifically, a first collection of input fuzzy sets may be used to determine the weighting 11 values of different sets describing the control action necessary to adjust the setpoint. An 12 example of such fuzzy input SCIs IS S11oW11 111 hlG. (~A il which I.N 3(), MN 32, ~N 34, 13 ZE 36, SP 38, MP 40, LP 42 represent the spectrum from large positive adjustment to 14 large negative adjustment to the tl-.ermostat.
The weighing factors) for a given temperature difference (Td) may be 16 directly determined using the above input sets LP 30 - LN 42. Notably, FIG.
6A shows 17 that any given temperature difference (T~ is included within either one or two fuzzy sets.
18 By manner of illustration, FIG. 6A depicts a situation in which a 19 temperature difference (T~ is -5.8 °F which falls within the sets MN
and SN. The weighing factors wl and w2 are graphically determined in FIG. 6A b~~
projecting the 21 horizontal lines hl, and h2 from the intersection of vertical line vl projected from the ,~
1 temperature difference (T~. Thus, the sets MN and SN have respective weighing factors 2 wl=0.8 and w2=0.2.
3 A fuzzy output set 50, shown in FIG. 6B, is determined using the weighting 4 factors determined from the fuzzy input set (FIG. 6A). This set 50 may be used to calculate the crisp thermostat adjustment value using known defuzzification methods, 6 such as the center of area or centroid methods which were discussed above with respect 7 to FIGS. 3 and 4.
8 It should be appreciated that an inverse relationship exists between the 9 fuzzy sets shown in FIGS. 6A and 6B. For example, the large negative (LN) set in Fig.
6A is represented by the large positive (LP) set in Fig. 6B, thereby providing a large 11 positive change in thermostat set point (heating) in response to a large negative error 12 bctwecn~the actual room tempcraturc and the thermostat srt p~iW shown is I~ig. (W. 'I'I~c 13 overall process of calculating a new thermostat setpoint using fuzzy sets is shown, for 14 example, in FIG. 7A.
As shown in the flow chart of FIG. 7A, fuzzy comfort sensation from the 16 individual occupants are collected as fuzzy sets 10-22 (FIG. 2) and defuzzified in a step 17 60 using the center of area or centroid approach, thereby yielding a crisp room air 18 temperature. Then in step 62, the temperature difference (T~ between the perceived room 19 air temperature (T,,~"~ and the current setpoint (Tsec~~n,) is calculated, Td = T«m - Tse,~~~t.
In step 64, the temperature difference Td is fuzzifi:d using fuzzy sets 30-42 (FIG. 6A).
21 Finally, in step 66 the setpoint information is defuzzified using a center of area or centroid 22 approach.
1 A preferred embodiment of the apparatus of the present invention will now 2 be explained with reference to FIG. 7B. The improved method for updating a thermal 3 setpoint requires occupant feedback in determining the setpoint for a thermostat or a PMV
4 sensor. According to the embodiment of FIG. 7B, occupants enter thermal comfort information using an interface 68 such as a personal computer, terminal or like input 6 device. The information is then transmitted over the existing Internet and/or Intranet 7 (such as Novell ~) communications network 70 to a building automation system 8 which in turn communicates with the HVAC system (not shown) via a local controller 74.
9 For example, building occupants may enter thermal comfort feedback into a web based interface reached via the Internet. A unique user identification code may be used to 11 identify the occupant, the relevant area of the building, and the relevant thermostat(s).
12 Alternatively, the occupant interface can reside on a server or host computer, with the 13 data being transmitted over a LAN. The new setpoint value is calculated in the building 14 automation system using one of the above described methods.
According to one embodiment of the present invention, the thermostat (not 1 G shown) is replaced by a thermal comfort sensor (not shown) such as a PMV
sensor. PMV
17 sensors utilize various environmental and user related factors to provide a more accurate 18 reflection of thermal comfort than conventional thermostats. Specifically, PMV sensor 19 output is based on the following six factors: dry bulb temperature, relative humidity, mean radiant temperature, air velocity, metabolic rate, and clothing insulation. PMV
21 sensors output an index. value which is related to the human sense of thermal comfort.
1 For example, a PMV output of +3 means hot while a I'MV of -3 indicates a cold 2 condition.
3 ASHRAE (1978) has published a scale correlating the PMV scale directly 4 to human comfort perception as follows:
Numerical value in PMV le Sensation sca 6 ~ +3 Hot 7 +2 W arm g +1 Slightly warm 9 0 Neutral - 1 Slightly cool 11 - 2 Cool 12 - 3 cold 13 Berglund, Larry, "Mathematical Models for Predicting the Thermal Comfort Response 14 of Building Occupants," ASHRAE Transaction, 1978, which is specifically incorporated 1 S by reference herein.
16 Currently, PMV sensors are prohibitively expensive (about $500-1000) 17 relative to the $100 price of a thermostat). However, as the sensor technology improves, 18 it is expected that PMV sensors will replace traditional thermostats for comfort control.
19 Notably, the use of a PMV sensor may facilitate an improvement in thermal comfort over the method and apparatus of the first embodiment. A collection of fuzzy sets used for 21 calculating the PMV value is shown, for example, in FIG. 8. The process of determining 22 the PMV value and adjusting the PMV sensor setpoint is identical to the process 23 described in relation to the first embodiment, except that in FIG. 8 the PMV scale value 24 is utilized in place of the room air temperature used in FIG. 2.
A further improvement in comfort control may be achieved by using a more 26 detailed tool describing the six input used to calculate the PMV values.
Notably, f 1 ASHRAE has recently developed a tool to predict the PMV value along with other 2 environment comfort indices (See, e.g., Fountain, M.E., and C. Huizenga., "A
Thermal 3 Sensation Prediction Tool for Use by the Profession," ASHRAE Transaction, 1997, Vol.
4 103, Part 2). However, the inputs utilized by such tools are difficult to obtain for S individual occupants in conventional HVAC systems.
G In contrast, the feedback mechanism of the present invention facilitates 7 occupant feedback. Occupant feedback regarding the various PMV inputs (fuzzy inputs) 8 is collected using the system of FIG. 7B, and is used to determine the PMV
setpoint. This 9 aspect of the present invention is shown in the block diagram of FIG. 9, and is described as follows.
11 The PMV calculator 102 of the present invention utilizes the following six 12 factors: comfort sensation (air temperature), radiant air temperature, draft sensation (air 13 velocity), humidity, metabolic rate (occupant activity), and clothing insulation. Five of 14 the factors (every factor except radiant air temperature) are obtained from the occupants via at least one of the Internet and the Lntranet as described previously with respect to the 16 first embodiment. The remaining factor, radiant air temperature, is determined from a 17 look-up table using the calculated air temperature.
18 The first factor, room air temperature 90 is obtained by converting 19 (defuzzifying) the subjective perceptions (fuzzy input) of individual occupants using one of the defuzzification methods previously described with respe~a to FIG. 2.
21 The second factor, radiant air temperature 92, can be correlated to the room 22 air temperature using published data. By manner of illustration, FIGS. l0A-lOC are 1 graphs correlating room air temperature with radiant air temperature. See, e.g., Fangcr, 2 P.O., "Calculation of thermal comfort: Introduction of a Basic Comfort Egua Lion, "
3 ASHRAE Transaction, 1967).
4 The third and fourth factors, room air velocity 94 and room air humidity 9G
S sensation, can be determined by converting the subjective perceptions draft sensation and 6 humidity sensation using fuzzy sets in the same manner as used in converting the thermal 7 comfort sensation into room air temperature. Such fuzzy sets are shown, for example, in 8 FIGS. 11 and 12.
9 Notably, the relationship between the draft sensation and the air velocity (FIG. 11) was developed using published experimental results (see, e.g., l~ountain, M.E., 11 ~ and E.A. Arens., "Air Movement and Thermal Comfort," ASHIL4E Journal, August, 12 1993, each of which is specifically incorporated by rclcrence herein.).
L,ikcwisc, II»
13 relationship, between humidity sensation and room air humidity (FIG. I Z) is based on I4 published research results (see, e.g., McArthur, J.W., "Humidity and Predicted-Mean-Vote-based (PMV-based) Comfort Control," ASHRAE Transaction, 198G) .
17 The fifth and sixth inputs are objective occupant data which can be 18 converted into crisp inputs using look-up tables. Specifically, an occupant's metabolic 19 rate 98 can be predicted from the occupant's description of activity, i.e., sitting, light work ete, using published tables. Table I shows the metabolic rate for different activity 21 levels. Moreover, clothing insulation (expressed in the units of CLO) can also be 22 predicted based on the occupant's description of clothing. See, e.g. Table II.
~
-~ 14 Activity Metabolic Rate Estimated Estimated Correction Per Unit Body SurtaceMechanical Relative Factor Area M/A~ Efficiency Velocity for Effects - in "Still" Radiation - Air Area 2 ~
kca7/m ! m/s fed hr ~, quiet 50 0 0 0, 83 ~ted, drafting 60 0 0-0.1 0.65 ~' ~d~ typing 70 0 0-0.1 0.85 ,~ps,d~g at attention65 0 0 0. ?5 ceding, washing 80 0-0. 05 0-0. 2 0. ?5 dishes Shoemaker 100 0-0.10 0-0.2 0.85 Seeping a bare floor (98 stroltes/mfn. ) 100 0-0. 5 - 0. 2-0. 0. T5 Seated, heavy leg and arm ~rements (metal 110 0-0. 15 0.1-0. 3 0. 85 worker at a bench) Walking about, moderate piling or pushing (carpenter metalworker, industrial140 0-0. 10 0-0. 9 0. ?5 ,Tainter) hick and shovel work, stone 220 0-0. 20 0-0 0 vaa:on wo7rk . .
Wstidng on the level with Velocity;
mph 2. 0 100 0 0. 9 0. T5 2. 5 120 0 1. 1 0. T5 3. 0 130 0 1. 3 . 0. T5 3. 5 180 0 1. 6 0. T5 4.0 190 0 - 1.8 0.95 5. 0 290 0 2. 2 0. T5 ~alklng up a grade:
~ Grade Velocity mph 1 120 0. OT 0. 4 0. ?5 2 150 0. 10 0. 9 O. T5 5 3 - 200 0.11 L 3 0. 95 .
5 4 305 0. 10 i. 8 0. T5 I 145 0.15 0. 4 0. T$
i5 2 ~ 230 0.19 - 0.9 0.75 15 $ 350 0. 19 1. 3 0. ?5 ~ I . - 180 0.20 0.4 0.95 25 2 335_ ' 0.21 0.9 0.T5 TABLE I
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shin O.J3 font-sleeve pajoms0.96LSO1.32 0.37 top. lass pe~ama 0..11 uousa:. shoo 3/1 slaw robe, allppan (no sueka) KoaoIoaEth rtlrtØ34L101.26 sAavsleoW bin.
peaty bore. smdsls KeKO-knEth skinØ671.221.29 loni-shxw sbln.
1611 slip. pasty Awe Knee-knpb skin, L10l.39i.46 bmj-sloew shirt, bdf slip. panty bola. IonE-sleeve "~e,~r Srae as above. LOt1.60130 0.33 replace eweret 0.10 whh salt jaeka Ands-I~tb sldtt.1.101.391.16 loot-rfeevs ah4t, ~ jet. P~ ~
l..oetp-sleeve O.n1.301.23 ooverslb, T-ahYt Orenll~ loo=-sleeve0.191.461.27 0.13 shirt, Trbin 0.10 Iotulaoed eoveralis.1.371.91.26 0.33 Ion=-sleeve WI
:basal undenvar, loo= wdetwear bottoms TABLE II
i r 1 The PMV calculator uses the six crisp values to calculate the PMV value.
2 The time dependent PMV can be calculated as follows:
-0.042(MlADU) M _ PMV 0.352 a + 0.032 ADU (1 ~) -0.35 43 - 0.061 M (1 - r~) - PQ - 0.42 M (1 - rl) - 50 ADU Aou -0.0023 M (44 - Po) - 0.0014 M (34 - Q) - 3.410-8f~~ (T ~ + 273)°
ADU ADu -(Tnrt ~' 273)4 - f~l h~ (T I - a) (3) 3 where T~, is given by:
T 1 = 35.7 - 0.032 M ( 1 - '~) - 0.18 ~~ 3.4 x 10-8 f~~ (T ~ + 273 )4 ADU
- (Tmrr + 273 )4 + f~~ h~ (T l - a) (4) 4 and h~ is given by:
i r ;y h~ _ {2.05 (T~l - T )o.2s for 2.05 (T~~ - Q~o.2s ~ 10.4 for 2.05 (T~~ -T (5) 1 In the above equations, the values of l~, and f~, can be calculated from a 2 lookup table (Table II). M/Ape and rl can be determined from a lookup table (Table I), 3 and Ta and V are determined from defuzzified inputs (FIGs. 2 and 12, respectively). T"", 4 can be calculated from an empirical relationship between Tmrt and room air temperature, type of activity and relative air velocity as shown in FIGS. 10A, l OB and l OC. Finally, 6 Pa can be calculated by solving the following three equations simultaneously:
7 w = U.G2198 [PW / (P - PW)]
8 v = [RaT (l~ +1.6078W)] / P (7) 9 P=PW+Pa (8) where, 11 v is the room volume expressed in cubic feet 12 w is the humidity ratio obtained from defuzzification of user input (FIG.
12) 13 T is the air temperature obtained from defuzzification of user input (FIG.
2) 14 PW is the partial vapor pressure P is the total pressure 16 Pa is the partial air pressure 17 Ra is the universal gas constant 1545.32 ft-lbf./(lb.-Mole-°R) '18-TypIaJ Meubolk Heat Genaation for Vary Adlvlfl,s Htd(6As)~, RauiaE
ShieplsE 13 0.7 KecHaaE Is a.t Seated. quia 1 E 1.0 Stwdia=. reload Z2 Lt WdklaE (on level wrfaa) 29 ftls (2 mph) 37 Z.0 1.1 Ws (3 mph) 1E 2.6 3.9 IJs (I mph) 70 3.i tNlix Aaivhies RaadinE. seated 1 E 1.0 WdtinE I1 1.0 TypIaE 20 1.1 FIUaE. wted ~~ Ll FflinE, staadiaE 26 1.1 WalkiaE d~out 31 !.7 1.(hiajlpockinE 39 tl DrlvinjlFlyiat Car 1 E 1.0 w to Z.0 Akenh, roudae Z2 1.2 Aircnh. instrunKnt IandlaE 17 1.1 Ahcralt. combat II ~.I
Heavy vehlele 39 3.Z
MisuAaoeous tkcupatioad Acrivl0es CookInE 29 l0 1.610 37 =.0 HouudeaninE 37 to 2.010 61 7.4 Seated. heavy limb movement I I 2.T
Machine work awlaj (table aw) 33 Lt IiEht (eleeuicd industry) 37 to 2.0 to W Z.I
heavy 71 4.0 Haaduttj 110 ib bajs 71 4.0 Piek and shovel work 71 so 4.010 EE 4.t Miseellaneous Leirarre Aalvitlu Datteiop soetd N w =A to E I 1.4 CdiaheniaJsserclse 5f to 3.0 to ~< 4.0 .
Teaals. do jles 66 to 7.6 to 71 1.0 Bsskatbd! 90 so 5.0 to !10 7.6 , WststUn~, comoaidw 170 7.0 w b 160 t.7 ~
TABLE I I I
r , r 1 According to a third embodiment of the present invention, an adaptation 2 algorithm (neural network) may be used to predict the thermal setpoint (or PMV value) 3 using historical data. Specifically, a relationship is developed between historical thermal 4 (or .PMV) setpoint information and associated identification such as outside air temperature, outside air humidity, day of the week and the time of the day.
According 6 to a preferred embodiment, the historical thermal (or PMV) setpoint data is determined 7 using occupant feedback according to the first or second embodiments of the present 8 invention. The historical data is used to establish a relationship between the thermal (or 9 PMV) setpoint and other input variables that influence the setpoint. Once the relationship is established, the thermal (or PMV) setpoints can be predicted in real-time using the 11 prevailing conditions (outside air temperature, outside air humidity, day of the week and 12 the time~of the day).
13 The aforementioned relationship may be determined using an artificial 14 neural network (ANN). A schematic of an ANN is shown in FIG. 13.
1 S According to a preferred embodiment embodiment of the present invention 16 the neural net is a general regression neural network (GRNN) which captures the input -17 output regression (linear or nonlinear) characteristics of the system. The General 18 Regression Neural Network (GRNN) is chosen to replicate the relationship between the 19 setpoint and other input variables due to its simplicity, robustness and excellent capability in system identification. Unlike a conventional neural network, it requires minimal 21 computational time to effectively capture the system characteristics.
Notably, this type 22 of neural network requires only a single parameter (Q), and unlike back propagation, does i 1 not involve any iterative training process. The following is a brief account of GRNN that 2 illustrates its implementation in identification of the HVAC components.
3 The input to a GRNN is a series of data that can have multiple dimensions.
4 For sample values of X,. and Y; of input vector X and scalar output Y, an estimate of the S desired value of Y at any given value of X is found using all of the sample values using G the following equation:
Yr exP( _ r ) ~~ - r=i 2az (9) exp( _ r ) r=i 2Q2 8 where a is the single smoothing parameter of the GRNN, and the scalar function D;Z, 9 representing the Euclidean distance from the given value to the known points, is given by Dt - ( X - Xr )T (X -- Xr ) ( I 0) 11 Equations 6 and 7 are the essence of the GRNN method. For a small value 12 of the smoothing parameter, Q, the estimated density assumes non-Gaussian shapes but 13 with the chance that the estimate may vary widely between the known irregular points.
14 When Q is large, a very smooth regression surface is achieved. The estimate Y(X) is a weighted average of all the observed samples, Yr, where each sample is weighted 16 exponentially according to its Euclidean distance from each Xr denoted by D;.
1 By manner of illustration, FIG. 14, shows the neural network architecture 2 for the above described GRNN algorithm. For a given X, the connections between the 3 input and the first layers compute the scalar D;, based on observed samples X; and 4 smoothing parameter Q, end then takes the exponent of D; Z.
A node in the second layer sums the exponential values for all samples.
6 The other nodes calculate the product of the exponential value and the corresponding 7 observed output Y,. for each sample observation. A node in the third layer sums the 8 product values. In turn, the sum is supplied to the output node where the ratio between 9 the sum of the exponent and the product values is calculated.
The weighting coefficients between the layers are dependent only upon the 11 observed samples ofX,., Y,. and smoothing parameter a. As a result, only a suitable single 12 value of v is needed to predict the output. The value of Q can be calculated using a 13 simple yet effective scheme known as the "Holdout" method (Spect 1991), which is 14 specifically incorporated by reference herein. Holdout is one of several methods which are available to find an optimum value of the smoothing parameter, a.
16 The implementation of GRNN also offers advantages over the conventional 17 methods of identification. In a traditional regression method for identification, the 18 operator has to input a priori knowledge of the equation type or has to search for the best 19 fit equation exhaustively. The code requirement for a non-linear regression is intensive and may be prohihitive for on-line use in a controller. In contrast, the GRNN
does not 21 require any user input for the functional form of the characteristics and uses a strikingly 22 simple code. Moreover, the GRNN algorithm can be imbedded into a hardware i is 1 processor, thereby simplifying the software development process since software coding 2 during field installation is not necessary. The choice of sample size and specific sample 3 values are important in designing a GRNN in general.
4 An improved method for updating a thermal setpoint in an HVAC system S according to the present invention has been shown and described. According to a first 6 aspect of the present invention, occupant feedback is used in determining the setpoint for 7 a thermostat or a PMV sensor. Moreover, according to a second aspect of the present 8 invention, a setpoint is predicted using an adaptation algorithm developed using historical 9 data.
While various embodiments of the present invention have been shown and i 1 described, it should be understood that other modifications, substitutions and alternatives 12 are apparent to one of ordinary skill in the art. Such modifications, substitutions and 13 alternatives can be made without departing from the spirit and scope of the invention, 14 which should be determined from the appended claims.
Various features of the invention are set forth in the appended claims.
__.. __T_..__ ___
11 Each of these methods will be understood from the following example.
12 . According to the center or area method, the crisp room air temperature 'I'' 13 is given by equation 1:
(w~TC~) 14 T*- ~ (1) n wn 1 S where, w" is the weighting factor for set n, Tc" is the center temperature of set n, and n is 16 the number of sets.
17 Thus, for the sets shown in FIG. 3, the value T' is [(.75 ~ Tc,) + (.25 ~
Tc2)]
18 / (.75 + .25). As shown in FIG. 3, the Tc,, the center temperature of the slightly cool set 19 is 71 degrees and Te2, the center temperature of the cool set is G9 degrees and Tc2. Thus, the value T~ is [(.75 ~ 71= 53.25) + (.25 ~ 69=17.25)] / ( 1 ) = 70.5 degrees.
1 According to the centroid method, an area is created by joining two 2 truncated areas formed by the intersection of the weighting factor lines and the two 3 triangles representing the two intersecting sets (cool and slightly cool).
By manner of 4 illustration, FIG. 4A depicts an area 24 created by joining the two truncated areas 12 and 14. The crisp room air temperature T' is given by the centroid of the area parallel to the 6 y-axis. In this case, the expression for the room air temperature is given by equation 2:
7 T'=(fTda)/A (2) 8 The integral of the product of the temperature T and differential area da can 9 be calculated by splitting the area under the curve into smaller areas comprising rectangles and right triangles. By manner of illustration, FIG. 4B shows the area under 11 the curve of FIG. 4A broken into six sub-areas. Areas 1 and 4 are right triangles having 12 a positive slope. The area A of a triangle is (base ~ hcight)/2. For a right triangle having 13 a positive slope (Area 1 and Area 4) f Tda = bh2/6. For right triangle having a negative 14 slope (Area 6), the value of the integral f Tda = bh2/3. Further, the area of the rectangular areas Area 2, 3 and 5 is (base ~ height), and the value of the integral f Tda = bh2.
16 Accordingly, for the set shown in FIG. 4B, the value T' is 70.36 °F
using 17 the centroid method. Once the crisp value of the room air temperature T' is known, a 18 simple rule can be developed to adjust the setpoint based on the temperature difference 19 . (T~ between the perceived room air temperature (T~m) and the current setpoint (TS°,~~~~), Td = T~°°", - T~~~",c. A linear rule relating the setpoint to the temperature difference (Td) 21 is shown, for example, in FIG. 5. Notably, the linear rule specifies slight changes in the 1 thermostat setpoint in response to the temperature difference (Td) to avoid 2 overcompensating for a given temperature difference.
3 Further, as shown in Fig. 5, there is an inverse correlation between the 4 change in thermostat set point and the error between the room temperature and current thermostat set point. When the error is negative, i.e., the room temperature is lower than 6 the set point, the change in thermostat set point is positive (provide heating). In contrast, 7 when the error is positive i.e., the room temperature is wanner than the set point, the 8 change in thermostat set point is negative (provide cooling).
9 Alternatively, fuzzy logic can be used in place of the linear rule.
Specifically, a first collection of input fuzzy sets may be used to determine the weighting 11 values of different sets describing the control action necessary to adjust the setpoint. An 12 example of such fuzzy input SCIs IS S11oW11 111 hlG. (~A il which I.N 3(), MN 32, ~N 34, 13 ZE 36, SP 38, MP 40, LP 42 represent the spectrum from large positive adjustment to 14 large negative adjustment to the tl-.ermostat.
The weighing factors) for a given temperature difference (Td) may be 16 directly determined using the above input sets LP 30 - LN 42. Notably, FIG.
6A shows 17 that any given temperature difference (T~ is included within either one or two fuzzy sets.
18 By manner of illustration, FIG. 6A depicts a situation in which a 19 temperature difference (T~ is -5.8 °F which falls within the sets MN
and SN. The weighing factors wl and w2 are graphically determined in FIG. 6A b~~
projecting the 21 horizontal lines hl, and h2 from the intersection of vertical line vl projected from the ,~
1 temperature difference (T~. Thus, the sets MN and SN have respective weighing factors 2 wl=0.8 and w2=0.2.
3 A fuzzy output set 50, shown in FIG. 6B, is determined using the weighting 4 factors determined from the fuzzy input set (FIG. 6A). This set 50 may be used to calculate the crisp thermostat adjustment value using known defuzzification methods, 6 such as the center of area or centroid methods which were discussed above with respect 7 to FIGS. 3 and 4.
8 It should be appreciated that an inverse relationship exists between the 9 fuzzy sets shown in FIGS. 6A and 6B. For example, the large negative (LN) set in Fig.
6A is represented by the large positive (LP) set in Fig. 6B, thereby providing a large 11 positive change in thermostat set point (heating) in response to a large negative error 12 bctwecn~the actual room tempcraturc and the thermostat srt p~iW shown is I~ig. (W. 'I'I~c 13 overall process of calculating a new thermostat setpoint using fuzzy sets is shown, for 14 example, in FIG. 7A.
As shown in the flow chart of FIG. 7A, fuzzy comfort sensation from the 16 individual occupants are collected as fuzzy sets 10-22 (FIG. 2) and defuzzified in a step 17 60 using the center of area or centroid approach, thereby yielding a crisp room air 18 temperature. Then in step 62, the temperature difference (T~ between the perceived room 19 air temperature (T,,~"~ and the current setpoint (Tsec~~n,) is calculated, Td = T«m - Tse,~~~t.
In step 64, the temperature difference Td is fuzzifi:d using fuzzy sets 30-42 (FIG. 6A).
21 Finally, in step 66 the setpoint information is defuzzified using a center of area or centroid 22 approach.
1 A preferred embodiment of the apparatus of the present invention will now 2 be explained with reference to FIG. 7B. The improved method for updating a thermal 3 setpoint requires occupant feedback in determining the setpoint for a thermostat or a PMV
4 sensor. According to the embodiment of FIG. 7B, occupants enter thermal comfort information using an interface 68 such as a personal computer, terminal or like input 6 device. The information is then transmitted over the existing Internet and/or Intranet 7 (such as Novell ~) communications network 70 to a building automation system 8 which in turn communicates with the HVAC system (not shown) via a local controller 74.
9 For example, building occupants may enter thermal comfort feedback into a web based interface reached via the Internet. A unique user identification code may be used to 11 identify the occupant, the relevant area of the building, and the relevant thermostat(s).
12 Alternatively, the occupant interface can reside on a server or host computer, with the 13 data being transmitted over a LAN. The new setpoint value is calculated in the building 14 automation system using one of the above described methods.
According to one embodiment of the present invention, the thermostat (not 1 G shown) is replaced by a thermal comfort sensor (not shown) such as a PMV
sensor. PMV
17 sensors utilize various environmental and user related factors to provide a more accurate 18 reflection of thermal comfort than conventional thermostats. Specifically, PMV sensor 19 output is based on the following six factors: dry bulb temperature, relative humidity, mean radiant temperature, air velocity, metabolic rate, and clothing insulation. PMV
21 sensors output an index. value which is related to the human sense of thermal comfort.
1 For example, a PMV output of +3 means hot while a I'MV of -3 indicates a cold 2 condition.
3 ASHRAE (1978) has published a scale correlating the PMV scale directly 4 to human comfort perception as follows:
Numerical value in PMV le Sensation sca 6 ~ +3 Hot 7 +2 W arm g +1 Slightly warm 9 0 Neutral - 1 Slightly cool 11 - 2 Cool 12 - 3 cold 13 Berglund, Larry, "Mathematical Models for Predicting the Thermal Comfort Response 14 of Building Occupants," ASHRAE Transaction, 1978, which is specifically incorporated 1 S by reference herein.
16 Currently, PMV sensors are prohibitively expensive (about $500-1000) 17 relative to the $100 price of a thermostat). However, as the sensor technology improves, 18 it is expected that PMV sensors will replace traditional thermostats for comfort control.
19 Notably, the use of a PMV sensor may facilitate an improvement in thermal comfort over the method and apparatus of the first embodiment. A collection of fuzzy sets used for 21 calculating the PMV value is shown, for example, in FIG. 8. The process of determining 22 the PMV value and adjusting the PMV sensor setpoint is identical to the process 23 described in relation to the first embodiment, except that in FIG. 8 the PMV scale value 24 is utilized in place of the room air temperature used in FIG. 2.
A further improvement in comfort control may be achieved by using a more 26 detailed tool describing the six input used to calculate the PMV values.
Notably, f 1 ASHRAE has recently developed a tool to predict the PMV value along with other 2 environment comfort indices (See, e.g., Fountain, M.E., and C. Huizenga., "A
Thermal 3 Sensation Prediction Tool for Use by the Profession," ASHRAE Transaction, 1997, Vol.
4 103, Part 2). However, the inputs utilized by such tools are difficult to obtain for S individual occupants in conventional HVAC systems.
G In contrast, the feedback mechanism of the present invention facilitates 7 occupant feedback. Occupant feedback regarding the various PMV inputs (fuzzy inputs) 8 is collected using the system of FIG. 7B, and is used to determine the PMV
setpoint. This 9 aspect of the present invention is shown in the block diagram of FIG. 9, and is described as follows.
11 The PMV calculator 102 of the present invention utilizes the following six 12 factors: comfort sensation (air temperature), radiant air temperature, draft sensation (air 13 velocity), humidity, metabolic rate (occupant activity), and clothing insulation. Five of 14 the factors (every factor except radiant air temperature) are obtained from the occupants via at least one of the Internet and the Lntranet as described previously with respect to the 16 first embodiment. The remaining factor, radiant air temperature, is determined from a 17 look-up table using the calculated air temperature.
18 The first factor, room air temperature 90 is obtained by converting 19 (defuzzifying) the subjective perceptions (fuzzy input) of individual occupants using one of the defuzzification methods previously described with respe~a to FIG. 2.
21 The second factor, radiant air temperature 92, can be correlated to the room 22 air temperature using published data. By manner of illustration, FIGS. l0A-lOC are 1 graphs correlating room air temperature with radiant air temperature. See, e.g., Fangcr, 2 P.O., "Calculation of thermal comfort: Introduction of a Basic Comfort Egua Lion, "
3 ASHRAE Transaction, 1967).
4 The third and fourth factors, room air velocity 94 and room air humidity 9G
S sensation, can be determined by converting the subjective perceptions draft sensation and 6 humidity sensation using fuzzy sets in the same manner as used in converting the thermal 7 comfort sensation into room air temperature. Such fuzzy sets are shown, for example, in 8 FIGS. 11 and 12.
9 Notably, the relationship between the draft sensation and the air velocity (FIG. 11) was developed using published experimental results (see, e.g., l~ountain, M.E., 11 ~ and E.A. Arens., "Air Movement and Thermal Comfort," ASHIL4E Journal, August, 12 1993, each of which is specifically incorporated by rclcrence herein.).
L,ikcwisc, II»
13 relationship, between humidity sensation and room air humidity (FIG. I Z) is based on I4 published research results (see, e.g., McArthur, J.W., "Humidity and Predicted-Mean-Vote-based (PMV-based) Comfort Control," ASHRAE Transaction, 198G) .
17 The fifth and sixth inputs are objective occupant data which can be 18 converted into crisp inputs using look-up tables. Specifically, an occupant's metabolic 19 rate 98 can be predicted from the occupant's description of activity, i.e., sitting, light work ete, using published tables. Table I shows the metabolic rate for different activity 21 levels. Moreover, clothing insulation (expressed in the units of CLO) can also be 22 predicted based on the occupant's description of clothing. See, e.g. Table II.
~
-~ 14 Activity Metabolic Rate Estimated Estimated Correction Per Unit Body SurtaceMechanical Relative Factor Area M/A~ Efficiency Velocity for Effects - in "Still" Radiation - Air Area 2 ~
kca7/m ! m/s fed hr ~, quiet 50 0 0 0, 83 ~ted, drafting 60 0 0-0.1 0.65 ~' ~d~ typing 70 0 0-0.1 0.85 ,~ps,d~g at attention65 0 0 0. ?5 ceding, washing 80 0-0. 05 0-0. 2 0. ?5 dishes Shoemaker 100 0-0.10 0-0.2 0.85 Seeping a bare floor (98 stroltes/mfn. ) 100 0-0. 5 - 0. 2-0. 0. T5 Seated, heavy leg and arm ~rements (metal 110 0-0. 15 0.1-0. 3 0. 85 worker at a bench) Walking about, moderate piling or pushing (carpenter metalworker, industrial140 0-0. 10 0-0. 9 0. ?5 ,Tainter) hick and shovel work, stone 220 0-0. 20 0-0 0 vaa:on wo7rk . .
Wstidng on the level with Velocity;
mph 2. 0 100 0 0. 9 0. T5 2. 5 120 0 1. 1 0. T5 3. 0 130 0 1. 3 . 0. T5 3. 5 180 0 1. 6 0. T5 4.0 190 0 - 1.8 0.95 5. 0 290 0 2. 2 0. T5 ~alklng up a grade:
~ Grade Velocity mph 1 120 0. OT 0. 4 0. ?5 2 150 0. 10 0. 9 O. T5 5 3 - 200 0.11 L 3 0. 95 .
5 4 305 0. 10 i. 8 0. T5 I 145 0.15 0. 4 0. T$
i5 2 ~ 230 0.19 - 0.9 0.75 15 $ 350 0. 19 1. 3 0. ?5 ~ I . - 180 0.20 0.4 0.95 25 2 335_ ' 0.21 0.9 0.T5 TABLE I
Typical InwIstlsa atad PersseabOlty Values for Clolbftt~ 8ntembla' Etwanbb Daerlptloa~(cl~)Ido)~d ~a l:
WtJkini shoes, O.M1.02l.10 0.34 short:trove 0.1!
shin Trotaea, shonsleew0371.20L l3 0.76 shin 0.11 Troruen.lanlshxwshin0.61LZli.l0 O.~I
O.IS
Serve as above. 0.%1.341 .7 plw wit jecka Seine as above. 1.111.691.72 0.3?
plus vest etW 0.37 T shire Ttousets.Ian1slceveL011.36I .1 chin.Ioa1-skew swewer, Tshln Saate as above, 1.701.111.37 plus suit Jacks enA
love: wderwear botttMu Sweat pws, sweat0.711.731.19 0.4I
shin O.J3 font-sleeve pajoms0.96LSO1.32 0.37 top. lass pe~ama 0..11 uousa:. shoo 3/1 slaw robe, allppan (no sueka) KoaoIoaEth rtlrtØ34L101.26 sAavsleoW bin.
peaty bore. smdsls KeKO-knEth skinØ671.221.29 loni-shxw sbln.
1611 slip. pasty Awe Knee-knpb skin, L10l.39i.46 bmj-sloew shirt, bdf slip. panty bola. IonE-sleeve "~e,~r Srae as above. LOt1.60130 0.33 replace eweret 0.10 whh salt jaeka Ands-I~tb sldtt.1.101.391.16 loot-rfeevs ah4t, ~ jet. P~ ~
l..oetp-sleeve O.n1.301.23 ooverslb, T-ahYt Orenll~ loo=-sleeve0.191.461.27 0.13 shirt, Trbin 0.10 Iotulaoed eoveralis.1.371.91.26 0.33 Ion=-sleeve WI
:basal undenvar, loo= wdetwear bottoms TABLE II
i r 1 The PMV calculator uses the six crisp values to calculate the PMV value.
2 The time dependent PMV can be calculated as follows:
-0.042(MlADU) M _ PMV 0.352 a + 0.032 ADU (1 ~) -0.35 43 - 0.061 M (1 - r~) - PQ - 0.42 M (1 - rl) - 50 ADU Aou -0.0023 M (44 - Po) - 0.0014 M (34 - Q) - 3.410-8f~~ (T ~ + 273)°
ADU ADu -(Tnrt ~' 273)4 - f~l h~ (T I - a) (3) 3 where T~, is given by:
T 1 = 35.7 - 0.032 M ( 1 - '~) - 0.18 ~~ 3.4 x 10-8 f~~ (T ~ + 273 )4 ADU
- (Tmrr + 273 )4 + f~~ h~ (T l - a) (4) 4 and h~ is given by:
i r ;y h~ _ {2.05 (T~l - T )o.2s for 2.05 (T~~ - Q~o.2s ~ 10.4 for 2.05 (T~~ -T (5) 1 In the above equations, the values of l~, and f~, can be calculated from a 2 lookup table (Table II). M/Ape and rl can be determined from a lookup table (Table I), 3 and Ta and V are determined from defuzzified inputs (FIGs. 2 and 12, respectively). T"", 4 can be calculated from an empirical relationship between Tmrt and room air temperature, type of activity and relative air velocity as shown in FIGS. 10A, l OB and l OC. Finally, 6 Pa can be calculated by solving the following three equations simultaneously:
7 w = U.G2198 [PW / (P - PW)]
8 v = [RaT (l~ +1.6078W)] / P (7) 9 P=PW+Pa (8) where, 11 v is the room volume expressed in cubic feet 12 w is the humidity ratio obtained from defuzzification of user input (FIG.
12) 13 T is the air temperature obtained from defuzzification of user input (FIG.
2) 14 PW is the partial vapor pressure P is the total pressure 16 Pa is the partial air pressure 17 Ra is the universal gas constant 1545.32 ft-lbf./(lb.-Mole-°R) '18-TypIaJ Meubolk Heat Genaation for Vary Adlvlfl,s Htd(6As)~, RauiaE
ShieplsE 13 0.7 KecHaaE Is a.t Seated. quia 1 E 1.0 Stwdia=. reload Z2 Lt WdklaE (on level wrfaa) 29 ftls (2 mph) 37 Z.0 1.1 Ws (3 mph) 1E 2.6 3.9 IJs (I mph) 70 3.i tNlix Aaivhies RaadinE. seated 1 E 1.0 WdtinE I1 1.0 TypIaE 20 1.1 FIUaE. wted ~~ Ll FflinE, staadiaE 26 1.1 WalkiaE d~out 31 !.7 1.(hiajlpockinE 39 tl DrlvinjlFlyiat Car 1 E 1.0 w to Z.0 Akenh, roudae Z2 1.2 Aircnh. instrunKnt IandlaE 17 1.1 Ahcralt. combat II ~.I
Heavy vehlele 39 3.Z
MisuAaoeous tkcupatioad Acrivl0es CookInE 29 l0 1.610 37 =.0 HouudeaninE 37 to 2.010 61 7.4 Seated. heavy limb movement I I 2.T
Machine work awlaj (table aw) 33 Lt IiEht (eleeuicd industry) 37 to 2.0 to W Z.I
heavy 71 4.0 Haaduttj 110 ib bajs 71 4.0 Piek and shovel work 71 so 4.010 EE 4.t Miseellaneous Leirarre Aalvitlu Datteiop soetd N w =A to E I 1.4 CdiaheniaJsserclse 5f to 3.0 to ~< 4.0 .
Teaals. do jles 66 to 7.6 to 71 1.0 Bsskatbd! 90 so 5.0 to !10 7.6 , WststUn~, comoaidw 170 7.0 w b 160 t.7 ~
TABLE I I I
r , r 1 According to a third embodiment of the present invention, an adaptation 2 algorithm (neural network) may be used to predict the thermal setpoint (or PMV value) 3 using historical data. Specifically, a relationship is developed between historical thermal 4 (or .PMV) setpoint information and associated identification such as outside air temperature, outside air humidity, day of the week and the time of the day.
According 6 to a preferred embodiment, the historical thermal (or PMV) setpoint data is determined 7 using occupant feedback according to the first or second embodiments of the present 8 invention. The historical data is used to establish a relationship between the thermal (or 9 PMV) setpoint and other input variables that influence the setpoint. Once the relationship is established, the thermal (or PMV) setpoints can be predicted in real-time using the 11 prevailing conditions (outside air temperature, outside air humidity, day of the week and 12 the time~of the day).
13 The aforementioned relationship may be determined using an artificial 14 neural network (ANN). A schematic of an ANN is shown in FIG. 13.
1 S According to a preferred embodiment embodiment of the present invention 16 the neural net is a general regression neural network (GRNN) which captures the input -17 output regression (linear or nonlinear) characteristics of the system. The General 18 Regression Neural Network (GRNN) is chosen to replicate the relationship between the 19 setpoint and other input variables due to its simplicity, robustness and excellent capability in system identification. Unlike a conventional neural network, it requires minimal 21 computational time to effectively capture the system characteristics.
Notably, this type 22 of neural network requires only a single parameter (Q), and unlike back propagation, does i 1 not involve any iterative training process. The following is a brief account of GRNN that 2 illustrates its implementation in identification of the HVAC components.
3 The input to a GRNN is a series of data that can have multiple dimensions.
4 For sample values of X,. and Y; of input vector X and scalar output Y, an estimate of the S desired value of Y at any given value of X is found using all of the sample values using G the following equation:
Yr exP( _ r ) ~~ - r=i 2az (9) exp( _ r ) r=i 2Q2 8 where a is the single smoothing parameter of the GRNN, and the scalar function D;Z, 9 representing the Euclidean distance from the given value to the known points, is given by Dt - ( X - Xr )T (X -- Xr ) ( I 0) 11 Equations 6 and 7 are the essence of the GRNN method. For a small value 12 of the smoothing parameter, Q, the estimated density assumes non-Gaussian shapes but 13 with the chance that the estimate may vary widely between the known irregular points.
14 When Q is large, a very smooth regression surface is achieved. The estimate Y(X) is a weighted average of all the observed samples, Yr, where each sample is weighted 16 exponentially according to its Euclidean distance from each Xr denoted by D;.
1 By manner of illustration, FIG. 14, shows the neural network architecture 2 for the above described GRNN algorithm. For a given X, the connections between the 3 input and the first layers compute the scalar D;, based on observed samples X; and 4 smoothing parameter Q, end then takes the exponent of D; Z.
A node in the second layer sums the exponential values for all samples.
6 The other nodes calculate the product of the exponential value and the corresponding 7 observed output Y,. for each sample observation. A node in the third layer sums the 8 product values. In turn, the sum is supplied to the output node where the ratio between 9 the sum of the exponent and the product values is calculated.
The weighting coefficients between the layers are dependent only upon the 11 observed samples ofX,., Y,. and smoothing parameter a. As a result, only a suitable single 12 value of v is needed to predict the output. The value of Q can be calculated using a 13 simple yet effective scheme known as the "Holdout" method (Spect 1991), which is 14 specifically incorporated by reference herein. Holdout is one of several methods which are available to find an optimum value of the smoothing parameter, a.
16 The implementation of GRNN also offers advantages over the conventional 17 methods of identification. In a traditional regression method for identification, the 18 operator has to input a priori knowledge of the equation type or has to search for the best 19 fit equation exhaustively. The code requirement for a non-linear regression is intensive and may be prohihitive for on-line use in a controller. In contrast, the GRNN
does not 21 require any user input for the functional form of the characteristics and uses a strikingly 22 simple code. Moreover, the GRNN algorithm can be imbedded into a hardware i is 1 processor, thereby simplifying the software development process since software coding 2 during field installation is not necessary. The choice of sample size and specific sample 3 values are important in designing a GRNN in general.
4 An improved method for updating a thermal setpoint in an HVAC system S according to the present invention has been shown and described. According to a first 6 aspect of the present invention, occupant feedback is used in determining the setpoint for 7 a thermostat or a PMV sensor. Moreover, according to a second aspect of the present 8 invention, a setpoint is predicted using an adaptation algorithm developed using historical 9 data.
While various embodiments of the present invention have been shown and i 1 described, it should be understood that other modifications, substitutions and alternatives 12 are apparent to one of ordinary skill in the art. Such modifications, substitutions and 13 alternatives can be made without departing from the spirit and scope of the invention, 14 which should be determined from the appended claims.
Various features of the invention are set forth in the appended claims.
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Claims (17)
1. A method for adjusting a thermostat setpoint for controlling the temperature of a space controlled by an HVAC system using feedback information provided by individual occupants located in the space, comprising the steps of:
(a) transmitting feedback information from plural occupants to a CPU over at least one of an Internet and Intranet communications network;
(b) calculating an average feedback temperature value; and (c) adjusting the thermostat setpoint based on said calculated average value.
(a) transmitting feedback information from plural occupants to a CPU over at least one of an Internet and Intranet communications network;
(b) calculating an average feedback temperature value; and (c) adjusting the thermostat setpoint based on said calculated average value.
2. A method for calculating a new thermal setpoint for controlling the temperature of a space controlled by an HVAC system using the existing thermal setpoint and subjective thermal comfort fuzzy feedback information provided by one or more individual occupants, comprising:
(a) transmitting thermal comfort fuzzy feedback from plural occupants to a CPU over at least one of an Internet and Intranet communications network, wherein said thermal comfort feedback is selected from a predetermined number of thermal comfort perception sets fuzzy sets , each said thermal comfort perception set being assigned a predetermined value;
(b) converting the thermal comfort fuzzy feedback received from occupants of a given heating/cooling zone into a numerical crisp room air temperature value T*
using a defuzzification process wherein said crisp room air temperature value T* is determined as a weighted average of the thermal comfort feedback; and (c) calculating the new thermal setpoint using a temperature difference (T d) between the crisp room air temperature T* and the existing thermal setpoint.
(a) transmitting thermal comfort fuzzy feedback from plural occupants to a CPU over at least one of an Internet and Intranet communications network, wherein said thermal comfort feedback is selected from a predetermined number of thermal comfort perception sets fuzzy sets , each said thermal comfort perception set being assigned a predetermined value;
(b) converting the thermal comfort fuzzy feedback received from occupants of a given heating/cooling zone into a numerical crisp room air temperature value T*
using a defuzzification process wherein said crisp room air temperature value T* is determined as a weighted average of the thermal comfort feedback; and (c) calculating the new thermal setpoint using a temperature difference (T d) between the crisp room air temperature T* and the existing thermal setpoint.
3. 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* = .SIGMA.(W n TC n) / .SIGMA.W 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.
4. 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* =
(.intg.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 predefined number of sets, the area of each set being truncated in relation to a percentage of feedback, and .intg.
Tda represents the integral of the product of the temperature T and differential area da of the truncated sets.
(.intg.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 predefined number of sets, the area of each set being truncated in relation to a percentage of feedback, and .intg.
Tda represents the integral of the product of the temperature T and differential area da of the truncated sets.
5. The method according to claim 2, wherein in step (c) the new thermal setpoint is determined from the temperature difference (Td) using a linear rule.
6. The method according to claim 2, wherein in step (c) the new thermal setpoint (TS) is determined from the temperature difference (Td) 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 = .SIGMA.(w n Td n)/.SIGMA.w n, and the centroid approach is determined: TS=(.intg.T d da)/.LAMBDA., 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, .intg.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.
(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 = .SIGMA.(w n Td n)/.SIGMA.w n, and the centroid approach is determined: TS=(.intg.T d da)/.LAMBDA., 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, .intg.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.
7. A method for calculating a new thermal setpoint for a PMV sensor controlling the thermal comfort of a space in an HVAC system using the existing thermal setpoint and subjective fuzzy feedback information, comprising:
(a) receiving feedback information including fuzzy feedback information over at least one of an Internet and Intranet communications network, wherein said fuzzy feedback includes thermal comfort perception selected from a predetermined number of thermal comfort perception sets fuzzy sets , each said thermal comfort perception set being assigned a predetermined value;
(b) converting the fuzzy feedback received from occupants of a given heating/cooling zone into crisp numerical values, including room air temperature value T*, using a defuzzification process wherein said crisp values are determined as a weighted average of the fuzzy feedback; and (c) calculating a PMV setpoint value for the PMV sensor using the crisp values.
(a) receiving feedback information including fuzzy feedback information over at least one of an Internet and Intranet communications network, wherein said fuzzy feedback includes thermal comfort perception selected from a predetermined number of thermal comfort perception sets fuzzy sets , each said thermal comfort perception set being assigned a predetermined value;
(b) converting the fuzzy feedback received from occupants of a given heating/cooling zone into crisp numerical values, including room air temperature value T*, using a defuzzification process wherein said crisp values are determined as a weighted average of the fuzzy feedback; and (c) calculating a PMV setpoint value for the PMV sensor using the crisp values.
8. 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.
9. 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 the previous thermostat setpoint and the feedback information.
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 the previous thermostat setpoint and the feedback information.
10. The control system of claim 9, wherein said transmitting means includes at least one of an Internet and Intranet communications network.
11. 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.
12. 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 o:cupant feedback;
a CPU for receiving feedback information from said transmitting means, wherein said CPU calculates an adjusted themostat setpoint using the previous thermostat setpoint and the feedback information.
a software interface for entering occupant feedback;
means for transmitting the o:cupant feedback;
a CPU for receiving feedback information from said transmitting means, wherein said CPU calculates an adjusted themostat setpoint using the previous thermostat setpoint and the feedback information.
13. The control system of claim 12, wherein said transmitting means includes at least one of an Internet and Intranet communications network.
14. 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.
15. 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.
(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.
16. The adaptive method according to claim 15, wherein said regression relationship in step (b) is determined using a general regression neural network.
17. 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.
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| US09/228,428 US6145751A (en) | 1999-01-12 | 1999-01-12 | Method and apparatus for determining a thermal setpoint in a HVAC system |
| US09/228,428 | 1999-01-12 |
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| CA002289237A Expired - Fee Related CA2289237C (en) | 1999-01-12 | 1999-11-10 | Method and apparatus for determining a thermal setpoint in a hvac system |
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-
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| CA2289237A1 (en) | 2000-07-12 |
| US6145751A (en) | 2000-11-14 |
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