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Patent US6145751 - Method and apparatus for determining a thermal setpoint in a HVAC system - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsThe 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...http://www.google.com/patents/US6145751?utm_source=gb-gplus-sharePatent US6145751 - Method and apparatus for determining a thermal setpoint in a HVAC systemAdvanced Patent SearchPublication numberUS6145751 APublication typeGrantApplication numberUS 09/228,428Publication dateNov 14, 2000Filing dateJan 12, 1999Priority dateJan 12, 1999Fee statusPaidAlso published asCA2289237A1, CA2289237CPublication number09228428, 228428, US 6145751 A, US 6145751A, US-A-6145751, US6145751 A, US6145751AInventorsOsman AhmedOriginal AssigneeSiemens Building Technologies, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (12), Non-Patent Citations (39), Referenced by (53), Classifications (12), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetMethod and apparatus for determining a thermal setpoint in a HVAC systemUS 6145751 AAbstract 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 data determined using occupant feedback is used to develop a neural network for predicting setpoint values.
What is claimed is: 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. 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.sub.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*=&#931;(w.sub.n Tc.sub.n)/&#931;w.sub.n where each occupant's thermal comfort feedback is expressed as one of a predefined number of fuzzy sets, Tc.sub.n is a predetermined center temperature for set n, w.sub.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*=(∫Tda)/A,where each occupant's thermal comfort feedback is expressed as one of a predefined number of fuzzy sets, each set having a predetermined temperature range which overlaps with an adjacent set, where A represents a sum of areas of the pre defined number of sets, the area of each set being truncated in relation to a percentage of feedback, and ∫Tda represents the integral of the product of the temperature T and differential area da of the truncated sets. 5. The method according to claim 2, wherein in step (c) the new thermal setpoint is determined from the temperature difference (T.sub.d) 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 (T.sub.d) using fuzzy logic approach, comprising the steps of:(c-1) reading at least one weighing factor (w.sub.n) from a look-up table using the temperature difference (T.sub.d); (c-2) calculating a thermal setpoint (TS) using one of a center of area approach and a centroid approach, wherein:the center of area approach is determined: TS=Σ(w.sub.n Td.sub.n)/Σw.sub.n, and the centroid approach is determined: TS=(∫T.sub.d da)/A, where each occupant's thermal comfort feedback is expressed as one of a predefined number of sets, each set having a predetermined temperature range which overlaps with an adjacent set, where A represents a sum of areas of the predefined number of sets, the area of each set being truncated in relation weighing factor w.sub.n corresponding to a percentage of occupant feedback set n, ∫Tda representing the integral of the product of the temperature T and differential area da of the truncated sets, Td.sub.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. 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.
DESCRIPTION OF THE PREFERRED EMBODIMENT The relationship between fuzzy expression of human comfort and actual room air temperature is well established. See, e.g. Fountain, M. E., and C. Huizenga., "A Thermal Sensation Prediction Tool for Use by the Profession," ASHRAE Transaction, 1997, Vol. 103, Part 2, and Berglund, L., "Mathematical Models for Predicting the Thermal Comfort Response of Building Occupants," ASHRAE Transaction, 1978, Part 1. A fuzzy set demonstrating such a relationship is shown in FIG. 2.
According to the center of area method, the crisp room air temperature T* is given by equation 1: ##EQU1## where, w.sub.n is the weighting factor for set n, Tc.sub.n is the center temperature of set n, and n is the number of sets.
Thus, for the sets shown in FIG. 3, the value T* is [(0.75.multidot.Tc.sub.1)+(0.25.multidot.Tc.sub.2)]/(0.75+0.25). As shown in FIG. 3, the Tc.sub.1, the center temperature of the slightly cool set is 71 degrees and Tc.sub.2, the center temperature of the cool set is 69 degrees and Tc.sub.2. Thus, the value T* is [(0.75.multidot.71=53.25)+(0.25.multidot.69=17.25)]/(1)=70.5 degrees.
The integral of the product of the temperature T and differential area da can 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 the curve of FIG. 4A broken into six sub-areas. Areas 1 and 4 are right triangles having a positive slope. The area A of a triangle is (base positive slope (Area 1 and Area 4) ∫Tda=bh.sup.2 /6. For right triangle having a negative slope (Area 6), the value of the integral∫Tda=bh.sup.2 /3. Further, the area of the rectangular areas Area 2, 3 and 5 is (base integrals∫Tda=bh.sup.2.
Accordingly, for the set shown in FIG. 4B, the value T* is 70.36.degree. F. using the centroid method. Once the crisp value of the room air temperature T* is known, a simple rule can be developed to adjust the setpoint based on the temperature difference (T.sub.d) between the perceived room air temperature (T.sub.room) and the current setpoint (T.sub.setpoint), T.sub.d =T.sub.room -T.sub.setpoint. A linear rule relating the setpoint to the temperature difference (T.sub.d) is shown, for example, in FIG. 5. Notably, the linear rule specifies slight changes in the thermostat setpoint in response to the temperature difference (T.sub.d) to avoid overcompensating for a given temperature difference.
The weighing factor(s) for a given temperature difference (T.sub.d) may be directly determined using the above input sets LP 30-LN 42. Notably, FIG. 6A shows that any given temperature difference (T.sub.d) is included within either one or two fuzzy sets.
By manner of illustration, FIG. 6A depicts a situation in which a temperature difference (T.sub.d) is -5.8.degree. F. which falls within the sets MN and SN. The weighing factors w1 and w2 are graphically determined in FIG. 6A by projecting the horizontal lines h1, and h2 from the intersection of vertical line v1 projected from the temperature difference (T.sub.d). Thus, the sets MN and SN have respective weighing factors w1=0.8 and w2=0.2.
As shown in the flow chart of FIG. 7A, fuzzy comfort sensation from the individual occupants are collected as fuzzy sets 10-22 (FIG. 2) and defuzzified in a step 60 using the center of area or centroid approach, thereby yielding a crisp room air temperature. Then in step 62, the temperature difference (T.sub.d) between the perceived room air temperature (T.sub.room) and the current setpoint (T.sub.setpoint) is calculated, T.sub.d =T.sub.room -T.sub.setpoint, In step 64, the temperature difference T.sub.d is fuzzified using fuzzy sets 30-42 (FIG. 6A). Finally, in step 66 the setpoint information is defuzzified using a center of area or centroid approach.
A preferred embodiment of the apparatus of the present invention will now be explained with reference to FIG. 7B. The improved method for updating a thermal setpoint requires occupant feedback in determining the setpoint for a thermostat or a PMV 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 device. The information is then transmitted over the existing Internet and/or Intranet (such as Novell which in turn communicates with the HVAC system (not shown) via a local controller 74. 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 identify the occupant, the relevant area of the building, and the relevant thermostat(s). Alternatively, the occupant interface can reside on a server or host computer, with the data being transmitted over a LAN. The new setpoint value is calculated in the building automation system using one of the above described methods.
TABLE I__________________________________________________________________________                         Estimated                              Correction                         Relative                              Factor        Metabolic Rate Per                  Estimated                         Velocity                              for Effecti        Unit Body Surface                  Mechanical                         in "Still"                              Radiation        Area M/A.sub.Du                  Efficiency                         Air  AreaActivity     kcal/m.sup.2 hr                  &#951;  m/s  f.sub.eff__________________________________________________________________________Seated, quiet        50        0      0    0.65Seated, drafting        60        0      0-0.1                              0.65Seated, typing        70        0      0-0.1                              0.65Standing at attention        65        0      0    0.75Standing, washing dishes        80        0-0.05 0-0.2                              0.75Shoemaker    100       0-0.10 0-0.2                              0.65Sweeping a bare floor (38        100strokes/min.)          0-0.5  0.2-0.5                              0.75Seated, heavy leg and arm        110movements (metal worker at                  0-0.15 0.1-0.3                              0.65a bench)Walking about, moderate        140       0-0.10 0-0.9                              0.75lifting or pushing (carpentermetalworker, industrialpainter)Pick and shovel work, stone        220       0-0.20 0-0.9                              0.75mason workWalking on the level withVelocity:mph2.0          100       0      0.9  0.752.5          120       0      1.1  0.753.0          130       0      1.3  0.753.5          160       0      1.6  0.754.0          190       0      1.8  0.755.0          290       0      2.2  0.75Walking up a grade:VelocityGrademph5    1       120       0.07   0.4  0.755    2       150       0.10   0.9  0.755    3       200       0.11   1.3  0.755    4       305       0.10   1.8  0.7515   1       145       0.15   0.4  0.7515   2       230       0.19   0.9  0.7515   3       350       0.19   1.3  0.7525   1       180       0.20   0.4  0.7525   2       335       0.21   0.9  0.75__________________________________________________________________________
The time dependent PMV can be calculated as follows: ##EQU2## where T.sub.cl is given by: ##EQU3## and h.sub.c is given by: ##EQU4##
In the above equations, the values of I.sub.c1 and f.sub.c1 can be calculated from a lookup table (Table II). M/A.sub.DU and η can be determined from a lookup table (Table I), and Ta and V are determined from defuzzified inputs (FIGS. 2 and 12, respectively). T.sub.mrt can be calculated from an empirical relationship between T.sub.mrt and room air temperature, type of activity and relative air velocity as shown in FIGS. 10A, 10B and 10C. Finally, Pa can be calculated by solving the following three equations simultaneously:
P.sub.w is the partial vapor pressure
P.sub.a is the partial air pressure
R.sub.a is the universal gas constant 1545.32 ft-1bf./(1b.-Mole-
TABLE III______________________________________Typical Metabolic Heat Generation for Various Activities               Btu/(h                        met.sup.a______________________________________RestingSleeping              13        0.7Reclining             15        0.8Seated, quiet         18        1.0Standing, relaxed     22        1.2Walking (on level surface)2.9 ft/s (2 mph)      37        2.04.4 ft/s (3 mph)      48        2.65.9 ft/s (4 mph)      70        3.8Office ActivitiesReading, seated       18        1.0Writing               18        1.0Typing                20        1.1Filing, seated        22        1.2Filing, standing      26        1.4Walking about         31        1.7Lifting/packing       39        2.1Driving/FlyingCar                   18 to 37  1.0 to 2.0Aircraft, routine     22        1.2Aircraft, instrument landing                 33        1.8Aircraft, combat      44        2.4Heavy vehicle         59        3.2Miscellaneous Occupational ActivitiesCooking               29 to 37  1.6 to 2.0Housecleaning         37 to 63  2.0 to 3.4Seated heavy limb movement                 41        2.2Machine worksawing (table saw)    33        1.8light (electrical industry)                 37 to 44  2.0 to 2.4heavy                 74        4.0Handling 110 lb bags  74        4.0Pick and shovel work  74 to 88  4.0 to 4.8Miscellaneous Leisure ActivitiesDancing, social       44 to 81  2.4 to 4.4Calisthenics/exercise 55 to 74  3.0 to 4.0Tennis, singles       66 to 74  3.6 to 4.0Basketball            90 to 140 5.0 to 7.6Wrestling, competitive                 130 to 160                           7.0 to 8.7______________________________________
The input to a GRNN is a series of data that can have multiple dimensions. For sample values of X.sub.i and Y.sub.i of input vector X and scalar output Y, an estimate of the desired value of Y at any given value of X is found using all of the sample values using the following equation: ##EQU5## where σ is the single smoothing parameter of the GRNN, and the scalar function D.sub.i.sup.2, representing the Euclidean distance from the given value to the known points, is given by
Equations 6 and 7 are the essence of the GRNN method. For a small value of the smoothing parameter, σ, the estimated density assumes non-Gaussian shapes but with the chance that the estimate may vary widely between the known irregular points. When σ is large, a very smooth regression surface is achieved. The estimate Y(X) is a weighted average of all the observed samples, Y.sub.i, where each sample is weighted exponentially according to its Euclidean distance from each X.sub.i denoted by D.sub.i.
By manner of illustration, FIG. 14, shows the neural network architecture for the above described GRNN algorithm. For a given X, the connections between the input and the first layers compute the scalar D.sub.i, based on observed samples X.sub.i and smoothing parameter σ, and then takes the exponent of D.sub.i.sup.2.
A node in the second layer sums the exponential values for all samples. The other nodes calculate the product of the exponential value and the corresponding observed output Y.sub.i for each sample observation. A node in the third layer sums the product values. In turn, the sum is supplied to the output node where the ratio between the sum of the exponent and the product values is calculated.
The weighting coefficients between the layers are dependent only upon the observed samples of X.sub.i, Y.sub.i and smoothing parameter σ. As a result, only a suitable single value of σ is needed to predict the output. The value of σ can be calculated using a simple yet effective scheme known as the "Holdout" method (Spect 1991), which is specifically incorporated by reference herein. Holdout is one of several methods which are available to find an optimum value of the smoothing parameter, σ.
DESCRIPTIONS OF THE DRAWINGS FIG. 1 is a graph showing human level of comfort in relation to room air temperature;
FIELD OF THE INVENTION The present invention relates generally to the field of HVAC (heating, ventilating, and air-conditioning) systems. In particular the present invention discloses an improved method for updating a thermal setpoint in HVAC system. According to a first aspect of the present invention, occupant feedback is used in determining the setpoint for a thermostat or a PMV sensor. Moreover, according to a second aspect of the present invention, a setpoint is predicted using an adaptation algorithm developed using historical data.
BACKGROUND The primary function of a HVAC system is to provide thermal comfort for building occupants. Paradoxically, conventional HVAC control systems do not solicit 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 a given heating zone. Moreover, the thermostat or temperature sensor may be influenced by the micro-climate around it and thus may not always reflect the human sense of comfort. In such a system, the location of the thermostat becomes extremely important in correlating the thermostat sensed temperature with the actual room air temperature. The human level of comfort has a strong correlation with the room air temperature as demonstrated by early research (Fountain, M. E., and C. Huizenga., "A Thermal Sensation Prediction Tool for Use by the Profession," ASHRAE Transaction, 1997, Vol. 103, Part 2). See, e.g., FIG. 1.
SUMMARY OF THE INVENTION The ability for individual occupants to provide direct feedback to the building control system would be extremely beneficial in providing thermal comfort. According to one aspect of the present invention, an existing Intranet and/or Internet communications network is used to convey thermal comfort feedback from individual occupants to the HVAC system. Notably, the present invention provides a practical method for any number of occupants to provide thermal comfort feedback to the HVAC system.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS5170935 *Nov 27, 1991Dec 15, 1992Massachusetts Institute Of TechnologyAdaptable control of HVAC systemsUS5285959 *May 14, 1992Feb 15, 1994Matsushita Electric Industrial Co., Ltd.Air heating apparatusUS5301101 *Apr 14, 1993Apr 5, 1994Honeywell Inc.Receding horizon based adaptive control having means for minimizing operating costsUS5333953 *Jul 19, 1993Aug 2, 1994Yamatake-Honeywell Co., Ltd.Method and apparatus for calculating thermal sensitivityUS5436852 *Sep 22, 1994Jul 25, 1995Yamatake-Honeywell Co., Ltd.Method and apparatus for calculating predicted mean thermal sensitivityUS5560711 *Sep 30, 1994Oct 1, 1996Goldstar Co., Ltd.Thermal comfort sensing deviceUS5570838 *Dec 23, 1994Nov 5, 1996Ford Motor CompanyMethod and control system for controlling an automotive HVAC system for increased occupant comfortUS5572195 *Aug 1, 1994Nov 5, 1996Precision Tracking Fm, Inc.Sensory and control system for local area networksUS5579993 *Jan 6, 1995Dec 3, 1996Landis & Gyr Powers, Inc.HVAC distribution system identificationUS5737934 *Jun 12, 1996Apr 14, 1998Honeywell Inc.Thermal comfort controllerUS5762265 *Oct 4, 1996Jun 9, 1998Matsushita Electric Industrial Co., Ltd.Air-conditioning control unitUS5822740 *Jun 28, 1996Oct 13, 1998Honeywell Inc.Adaptive fuzzy controller that modifies membership functions* Cited by examinerNon-Patent CitationsReference1ASHRAE Fundamentals Handbook "Thermal Comfort" 1997.2 *ASHRAE Fundamentals Handbook Thermal Comfort 1997.3Bauman, Fred s., Carter, Thomas G., Baughman, Anne V., Arens, Edward A. "Field Study of the Impact of a Desktop Task/Ambient Conditioning System in Office Buildings" ASHRAE.4 *Bauman, Fred s., Carter, Thomas G., Baughman, Anne V., Arens, Edward A. Field Study of the Impact of a Desktop Task/Ambient Conditioning System in Office Buildings ASHRAE.5Berglund, L., "Mathematical Models for Predicting The Thermal Comfort Response of Building Occupants," ASHRAE Transaction, 1978, part 1.6 *Berglund, L., Mathematical Models for Predicting The Thermal Comfort Response of Building Occupants, ASHRAE Transaction, 1978, part 1.7Chan, W.T. Daniel, Burnett, John, de Dear, J. Richard Ng, Stephen C.H. "A Large-Scale Survey of Thermal Comfort in Office Premises in Hong Kong" ASHRAE.8 *Chan, W.T. Daniel, Burnett, John, de Dear, J. Richard Ng, Stephen C.H. A Large Scale Survey of Thermal Comfort in Office Premises in Hong Kong ASHRAE.9Charles, C., B.C. Krafthefer, M.L. Rhodes and M.A. Listvan., "Silicon Infrared Sensors for Thermal Comfort and Control," ASHRAE Journal, Apr., 1993.10 *Charles, C., B.C. Krafthefer, M.L. Rhodes and M.A. Listvan., Silicon Infrared Sensors for Thermal Comfort and Control, ASHRAE Journal, Apr., 1993.11Cox, Earl "Fuzzy Fundamentals" IEEE Spectrum Oct. 1992.12 *Cox, Earl Fuzzy Fundamentals IEEE Spectrum Oct. 1992.13Fanger, P.O., "Calculation of Thermal Comfort; Introduction of a Basic Comfort Equation," ASHRAE Transaction, 1967.14 *Fanger, P.O., Calculation of Thermal Comfort; Introduction of a Basic Comfort Equation, ASHRAE Transaction, 1967.15Fountain, M.E., and C. Huizenga., "A Thermal Sensation Prediction Tool for Use by the Profession," ASHRAE Transaction, 1997, vol. 103, Part 2.16 *Fountain, M.E., and C. Huizenga., A Thermal Sensation Prediction Tool for Use by the Profession, ASHRAE Transaction, 1997, vol. 103, Part 2.17Fountain, M.E., and E.A. Arens., "Air Thermal Sensation Prediction Tool for Use by the Profession," ASHRAE Transaction, 1997, vol. 103, Part 2.18 *Fountain, M.E., and E.A. Arens., Air Thermal Sensation Prediction Tool for Use by the Profession, ASHRAE Transaction, 1997, vol. 103, Part 2.19 *Jitkhjorwanich, Kitchai, Pitts, Adrian C. Malama, Albert, Sharples, Steve Thermal Comfort in Transitional Spaces in the Cool Season of Bangkok ASHRAE.20Jones, Jones W. and Ogawa, Y. "Transient Interaction Between the Human and the Thermal Environment" ASHRAE Transactions Research.21 *Jones, Jones W. and Ogawa, Y. Transient Interaction Between the Human and the Thermal Environment ASHRAE Transactions Research.22Malama, Albert, Sharples, Steve, Pitts, Adrian, Jitkajornwanich, Kitchai "An Investigation of the Thermal Comfort Adaptive Model in a Tropical Upland Climate" ASHRAE.23 *Malama, Albert, Sharples, Steve, Pitts, Adrian, Jitkajornwanich, Kitchai An Investigation of the Thermal Comfort Adaptive Model in a Tropical Upland Climate ASHRAE.24McArthur, J.W., "Humidity and Predicted-Mean-Vote-Based (PMV-based) Comfort Control," ASHRAE Transaction, 1986.25 *McArthur, J.W., Humidity and Predicted Mean Vote Based (PMV based) Comfort Control, ASHRAE Transaction, 1986.26Newsham, G.R., and Tiller, D.K. "A Field Study of Office Thermal Comfort Using Questionnaire Software".27 *Newsham, G.R., and Tiller, D.K. A Field Study of Office Thermal Comfort Using Questionnaire Software .28Scheatzle, D.G. and Arch. D. "The Development of PMV-Based Control for a Resident in a Hot Arid Climate" ASHRAE.29 *Scheatzle, D.G. and Arch. D. The Development of PMV Based Control for a Resident in a Hot Arid Climate ASHRAE.30Scholten, A. "Fuzzy Logic Control" Engineered systems, Jun. 1995.31 *Scholten, A. Fuzzy Logic Control Engineered systems, Jun. 1995.32Vaneck, T.W. "Fuzzy Guidance Controller for an Autonomous Boat" IEEE 1997.33 *Vaneck, T.W. Fuzzy Guidance Controller for an Autonomous Boat IEEE 1997.34Y.F. Li and c.C. Lau "Development of Fuzzy Algorithms for Servo Systems" IEEE Control Systems Magazine, Apr. 1989.35 *Y.F. Li and c.C. Lau Development of Fuzzy Algorithms for Servo Systems IEEE Control Systems Magazine, Apr. 1989.36 *Yamatake Honeywell, Integrated Environmental Comfort Control, Yamatake Web page, 1998.37Yamatake-Honeywell, "Integrated Environmental Comfort Control," Yamatake Web page, 1998.38Zadeh, L.A. "The Calculus of Fuzzy If/Then Rules" A1 Expert, Mar. 1992.39 *Zadeh, L.A. The Calculus of Fuzzy If/Then Rules A1 Expert, Mar. 1992.* Cited by examinerReferenced byCiting PatentFiling datePublication dateApplicantTitleUS6430953 *Nov 30, 2000Aug 13, 2002Lg Electronics Inc.Air conditioner for multiple roomUS6726111Aug 6, 2001Apr 27, 2004Tjernlund Products, Inc.Method and apparatus for centrally controlling environmental characteristics of multiple air systemsUS6782294Feb 19, 2003Aug 24, 2004Arecont Intellectual Property Holdings, LlcInternet based distributed control systemUS6848623Sep 25, 2003Feb 1, 2005Tjernlund Products, Inc.Method and apparatus for centrally controlling environmental characteristics of multiple air systemsUS6865449May 17, 2002Mar 8, 2005Carrier CorporationLocation adjusted HVAC controlUS7024258Mar 17, 2003Apr 4, 2006Siemens Building Technologies, Inc.System and method for model-based control of a building fluid distribution systemUS7089087 *May 17, 2002Aug 8, 2006Carrier CorporationLimited access comfort controlUS7099748 *Jun 29, 2004Aug 29, 2006York International Corp.HVAC start-up control system and methodUS7117045Sep 9, 2002Oct 3, 2006Colorado State University Research FoundationCombined proportional plus integral (PI) and neural network (nN) controllerUS7275533Nov 13, 2003Oct 2, 2007Exhausto, Inc.Pressure controller for a mechanical draft systemUS7651034Oct 7, 2005Jan 26, 2010Tjernlund Products, Inc.Appliance room controllerUS7839275Nov 9, 2005Nov 23, 2010Truveon Corp.Methods, systems and computer program products for controlling a climate in a buildingUS7848900Sep 16, 2008Dec 7, 2010Ecofactor, Inc.System and method for calculating the thermal mass of a buildingUS7854389 *Aug 30, 2006Dec 21, 2010Siemens Industry Inc.Application of microsystems for comfort controlUS7870090Dec 22, 2005Jan 11, 2011Trane International Inc.Building automation system date managementUS7904186Dec 22, 2005Mar 8, 2011Trane International, Inc.Building automation system facilitating user customizationUS7908116Jul 31, 2008Mar 15, 2011Ecofactor, Inc.System and method for using a network of thermostats as tool to verify peak demand reductionUS7908117Jul 31, 2008Mar 15, 2011Ecofactor, Inc.System and method for using a network of thermostats as tool to verify peak demand reductionUS7917232Dec 22, 2005Mar 29, 2011Trane International Inc.Building automation system data managementUS8010237Jul 6, 2009Aug 30, 2011Ecofactor, Inc.System and method for using ramped setpoint temperature variation with networked thermostats to improve efficiencyUS8019567Sep 16, 2008Sep 13, 2011Ecofactor, Inc.System and method for evaluating changes in the efficiency of an HVAC systemUS8024054Dec 22, 2005Sep 20, 2011Trane International, Inc.Building automation system facilitating user customizationUS8050801Aug 22, 2005Nov 1, 2011Trane International Inc.Dynamically extensible and automatically configurable building automation system and architectureUS8055386Dec 22, 2005Nov 8, 2011Trane International Inc.Building automation system data managementUS8055387Dec 22, 2005Nov 8, 2011Trane International Inc.Building automation system data managementUS8078330Aug 14, 2007Dec 13, 2011Intercap Capital Partners, LlcAutomatic energy management and energy consumption reduction, especially in commercial and multi-building systemsUS8090477Aug 20, 2010Jan 3, 2012Ecofactor, Inc.System and method for optimizing use of plug-in air conditioners and portable heatersUS8099178Dec 22, 2005Jan 17, 2012Trane International Inc.Building automation system facilitating user customizationUS8131497Dec 2, 2010Mar 6, 2012Ecofactor, Inc.System and method for calculating the thermal mass of a buildingUS8131506Feb 28, 2011Mar 6, 2012Ecofactor, Inc.System and method for using a network of thermostats as tool to verify peak demand reductionUS8180492Jul 13, 2009May 15, 2012Ecofactor, Inc.System and method for using a networked electronic device as an occupancy sensor for an energy management systemUS8180824Feb 23, 2009May 15, 2012Trane International, Inc.Log collection data harvester for use in a building automation systemUS8219660Feb 26, 2010Jul 10, 2012Trane International Inc.Simultaneous connectivity and management across multiple building automation system networksUS8260733Oct 10, 2006Sep 4, 2012Garbortese Holdings, LlcNeural network system and method for controlling information output based on user feedbackUS8290627Dec 22, 2005Oct 16, 2012Trane International Inc.Dynamically extensible and automatically configurable building automation system and architectureUS8340826Dec 16, 2011Dec 25, 2012Ecofactor, Inc.System and method for optimizing use of plug-in air conditioners and portable heatersUS8412488Mar 1, 2012Apr 2, 2013Ecofactor, Inc.System and method for using a network of thermostats as tool to verify peak demand reductionUS8423322Sep 12, 2011Apr 16, 2013Ecofactor, Inc.System and method for evaluating changes in the efficiency of an HVAC systemUS8498753May 4, 2010Jul 30, 2013Ecofactor, Inc.System, method and apparatus for just-in-time conditioning using a thermostatUS8556188May 26, 2010Oct 15, 2013Ecofactor, Inc.System and method for using a mobile electronic device to optimize an energy management systemUS8596550May 11, 2010Dec 3, 2013Ecofactor, Inc.System, method and apparatus for identifying manual inputs to and adaptive programming of a thermostatUS8635338Apr 25, 2012Jan 21, 2014Trane International, Inc.Log collection data harvester for use in a building automation systemUS20090204262 *Sep 8, 2005Aug 13, 2009Daikin Industries, Ltd.Environmental control apparatus, environmental control system, environmental control method, and environmental control programUSH2176 *Oct 5, 2000Dec 5, 2006Johnson Controls Technology CompanySystem for processing interior environment complaints from building occupantsCN101652732BFeb 13, 2007Jun 15, 2011开利公司Lifestyle activity choice comfort settingsEP2657803A1 *Apr 27, 2012Oct 30, 2013AFRISO-Euro-Index GmbHRoom temperature control assembly and method for areas heated with hot waterWO2004077188A1 *Apr 29, 2003Sep 10, 2004Becker Richard DMethods and systems for regulating room temperature with plug-in heaters or air conditionersWO2005024311A1Sep 10, 2004Mar 17, 2005Teck Hoe KhooControl method and apparatus for an air conditioner using occupant feedbackWO2006090547A1 *Jan 18, 2006Aug 31, 2006Matsushita Electric Works LtdEnvironmental apparatus control systemWO2008008337A2 *Jul 9, 2007Jan 17, 2008Edwards Vacuum IncMethod for controlling temperatureWO2008100257A1 *Feb 13, 2007Aug 21, 2008Carrier CorpLifestyle activity choice comfort settingsWO2010111444A1 *Mar 25, 2010Sep 30, 2010Siemens Industry Inc.System and method for climate control set-point optimization based on individual comfortWO2011141506A1 *May 11, 2011Nov 17, 2011Commissariat A L'energie Atomique Et Aux Energies AlternativesCustomized control of the thermal comfort of an occupant of a building* Cited by examinerClassifications U.S. Classification236/51, 236/78.00B, 165/208, 374/115International ClassificationG05D23/19, F24F11/00Cooperative ClassificationF24F11/0012, F24F11/006, F24F2011/0057, G05D23/1902European ClassificationF24F11/00R5, G05D23/19BLegal EventsDateCodeEventDescriptionApr 5, 2012FPAYFee paymentYear of fee payment: 12Mar 11, 2010ASAssignmentEffective date: 20090923Owner name: SIEMENS INDUSTRY, INC.,GEORGIAFree format text: MERGER;ASSIGNOR:SIEMENS BUILDING TECHNOLOGIES, INC.;REEL/FRAME:024066/0464Apr 14, 2008FPAYFee paymentYear of fee payment: 8Apr 14, 2004FPAYFee paymentYear of fee payment: 4Jan 12, 1999ASAssignmentOwner name: SIEMENS BUILDING TECHNOLOGIES, INC., ILLINOISFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:AHMED, OSMAN;REEL/FRAME:009701/0757Effective date: 19990106RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google