WO2011100736A2 - Model based system and method for estimating parameters and states in temperature controlled spaces - Google Patents
Model based system and method for estimating parameters and states in temperature controlled spaces Download PDFInfo
- Publication number
- WO2011100736A2 WO2011100736A2 PCT/US2011/024847 US2011024847W WO2011100736A2 WO 2011100736 A2 WO2011100736 A2 WO 2011100736A2 US 2011024847 W US2011024847 W US 2011024847W WO 2011100736 A2 WO2011100736 A2 WO 2011100736A2
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- estimated
- cold room
- zone
- load
- parameter
- Prior art date
Links
Classifications
-
- 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
-
- 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
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/32—Responding to malfunctions or emergencies
- F24F11/38—Failure diagnosis
-
- 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
- F24F11/46—Improving electric energy efficiency or saving
-
- 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
-
- 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
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- 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
- F24F11/41—Defrosting; Preventing freezing
-
- 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
- F24F11/63—Electronic processing
-
- 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
- F24F2140/00—Control inputs relating to system states
- F24F2140/50—Load
Definitions
- the present disclosure generally relates to systems and methods for controlling temperature in an interior space and, more particularly, to systems and methods for estimating parameters related to the heating and/or cooling of an interior space.
- HVAC systems are used to control temperature and other environmental conditions within structures such as residences, office buildings, and manufacturing plants.
- environmental conditions such as temperature, humidity, air purity, air flow, enthalpy (combined value of temperature and humidity), and "fresh air” ventilation can be regulated to ensure that the interior environment of a structure is as desired for particular occupants and equipment housed in the structure, and for processes and procedures conducted within the structure.
- refrigeration systems are used to maintain an interior space, such as a cold room for food storage, at a desired temperature to minimize bacteria growth or other detrimental effects to the contents stored in the space.
- a supermarket refrigeration system may have a cold room for storing goods at a controlled temperature. Food quality is of primary importance to the supermarket operation, and therefore the refrigeration system may be continuously monitored to maintain a desired food temperature.
- an alarm may be triggered when the room temperature exceeds a threshold value.
- An engineer may review alarm conditions to try to determine the root cause of the alarm, such as detecting and diagnosing possible faults in the refrigeration system.
- Conventional monitoring systems typically use a manual process to determine root causes for alarm signals. For example, an engineer may call the store to determine whether warmer goods were recently brought into the cold room, thereby raising the air temperature of the cold room above the threshold value.
- the engineer may wait for a predetermined period of time to see if the air temperature returns to a safe level before determining whether the alarm is true or false. Such delay, however, may adversely affect food quality for an unnecessary period of time.
- a method for estimating a heating/cooling load of a zone within a building may include determining a measured parameter from the zone, generating a reduced order thermodynamic model of the zone, generating an Extended Kalman Filter based on the thermodynamic model of the zone, and processing the measured parameter using the Extended Kalman Filter to estimate at least one unknown state of the zone.
- a method for estimating a temperature in a cold room of a refrigeration system may include determining a measured parameter from the cold room, generating a reduced order thermodynamic model of the cold room, the thermodynamic model including at least one unknown parameter, and identifying the at least one unknown parameter of the thermodynamic model using a system identification method and sensor measurement data.
- An Extended Kalman Filter may be generated based on the thermodynamic model of the cold room, and the measured parameter may be processed using the Extended Kalman Filter to obtain an estimated unknown state of the cold room and to obtain an estimated unknown parameter of the cold room.
- FIG. 1 is a schematic block diagram of a model-based method of estimating load in a building
- FIG. 2 is a schematic illustration of a building
- FIG. 3 is a schematic illustration of a floor of the building of FIG. 2;
- FIG. 4 is a schematic illustration of a building thermodynamic model that may be used in the method illustrated in FIG. 1 ;
- FIG. 5 is a schematic illustration of a time update and a measurement update that may be performed by a Extended Kalman Filter based estimator
- FIG. 6 is a graphical representation of an internal load profile of a building obtained from the method illustrated in FIG. 1 ;
- FIG. 7 is a schematic illustration of a cold room of a supermarket refrigeration system.
- FIG. 8 is a schematic block diagram of a model-based method of predicting an air temperature in a cold room of a supermarket refrigeration system.
- a thermodynamic model of a zone in a building is used in conjunction with an Extended Kalman Filter (EKF) to estimate a heating/cooling load (e.g., internal load) of the zone. Multiple estimated loads over time may be used to generate an estimated load profile, which in turn may be used in energy simulation programs or for diagnostics.
- EKF Extended Kalman Filter
- a thermodynamic model of a cold room is used in conjunction with an E F to estimate a temperature of goods stored in the cold room.
- the EKF may also estimate some unknown parameters that may be used in the thermodynamic model to generate a predicted air temperature of the cold room. The predicted air temperature may be compared with an actual measured temperature of the cold room to determine whether a triggered alarm condition is true or false.
- FIG. 1 schematically illustrates a method 20 for estimating real time load in a zone of a building.
- the zone to which the method is applied may be scaled. That is, the zone may be defined as a single room within the building, a group of rooms, an entire floor, or the entire interior space of the building.
- the method may further be
- a building 10 is schematically illustrated in FIG. 2, while a floor 12 of the building 10 is illustrated in FIG. 3.
- An interior zone T zone is located on the floor 12 of the building 10.
- the interior zone T zon e is surrounded by four neighboring zones, a north neighboring zone T n , an east neighboring zone T e , a south neighboring zone T s , and a west neighboring zone T w .
- the method 20 may be used to estimate real time load in the interior zone T zon e of the building 10.
- real time data is provided by various sensors, such as temperature sensors and airflow sensors, provided in the zone.
- the sensors may be provided as part of an HVAC system used to control temperature and other air qualities in the zone.
- the real time data may be taken from a Building Management System (BMS) or similar system provided to control the HVAC system.
- BMS Building Management System
- the real time data may include temperature, airflow rate, or other qualities that may be directly measured.
- a plurality of the same type of sensor may be used in the neighboring zones to measure a parameter at different areas within the building. For example, multiple temperatures sensors may be provided, such as at north, south, east and west neighboring zones within the building, to provide temperature data at those multiple positions.
- the real time data is input into a reduced order thermodynamic model of the building zone at block 24.
- An exemplary low order state space thermodynamic model 25 which may be employed within the block 24 and may be based on non-linear algebraic and differential equations, is schematically shown in FIG. 4.
- the model 25 uses a number of parameters, such as ambient temperature Tamb, zone well-mixed air temperature T zon e, internal surface connective heat transfer coefficient hi, external surface convective heat transfer coefficient ho, and surface area A, that may be measurable or otherwise known.
- Other parameters and/or states used in the model 25 may be unknown, such as outside surface temperature T 0S ur, and inside surface temperature Ti Sur .
- thermodynamic model 25 may also be stated mathematically.
- the state space formation from the thermodynamics model 25 is illustrated below for the interior zone Tzone, assuming adiabatic boundary conditions for the floor and ceiling:
- T zone Zone well mixed air temperature [°C];
- T w West neighboring zone air temperature [°C]
- T n North neighboring zone air temperature [°C]
- T e East neighboring zone air temperature [°C]
- T s South neighboring zone air temperature [°C]
- T ow West wall outside surface temperature [°C]
- T iw West wall inside surface temperature [°C]
- T in North wall inside surface temperature [°C]
- T os South wall outside surface temperature [°C]
- the surface area [m 2 ], j e (w, n,e,s) is the index for surrouding zones: west, north, east and west;
- T sa The supply air temperature [°C];
- C pa The specific heat capacity of dry air [J/kg.°C].
- thermodynamic model 25 the results from the thermodynamic model 25 are then input into block 26 where an Extended Kalman Filter (EKF) 27 can be used to estimate unknown states in the process, such as loads.
- EKF 27 may also estimate one or more unknown parameters and/or states used in the thermodynamic model, such as unmeasured surface temperatures. The uncertainty of real time data may be considered during design of the EKF 27.
- the EKF 27 can include a time update 30 and a measurement update 32.
- time update 30 initial estimates of the unknown state and an error covariance are provided from the thermodynamic model 25 at time k-1. Based on the initial estimates, a predicted unknown state and a predicted error covariance at time k are generated.
- a measured parameter is used to update the predicted unknown state and error covariance.
- a Kalman gain is computed. The predicted unknown state is then updated with the computed Kalman gain and the measured parameter. The predicted error covariance is also updated using the computed Kalman gain. The updated predicted unknown state and the updated predicted error covariance are then fed back to the time update 30, thereby to refine the thermodynamic model.
- the measured parameter may be a measured air temperature from the zone.
- the thermodynamic model 25 and EKF 27 may be used to estimate unknown parameters, such as unmeasured room surface temperatures.
- model 25 and EKF 27 may be used to estimate unknown states of the zone, such as loads.
- an estimated load profile 29 may be generated based on multiple load estimates taken over time. As shown in greater detail in FIG. 6, the estimated load profile 29 may chart a load, such as lumped load Qj nt , over a period of time, such as a day.
- the estimated load profile 29 may provide various types of information either directly or inferentially. For example, the estimated load profile 29 may facilitate a better understanding of building usage, such as occupancy, plug loads, lighting loads, and process loads, in a dynamic environment.
- the estimated load profile 29 may further enable refinements to existing processes, such as building energy monitoring, diagnostic, or control tools.
- Building energy monitoring tools include energy simulation programs, such as the EnergyPlus® program provided by the U.S. Department of Energy, which may be used to simulate building energy use over time.
- the estimated load profile 29 may be provided as an input load profile to such an energy simulation program, thereby to provide a more accurate estimate of energy usage in a building.
- the estimated load profile 29 may also be used in a building energy diagnostics tool or program to determine faults or alarm conditions.
- the estimated load profile 29 may indicate load anomalies, such as an unexpectedly large load during a period of the day when such a load would not normally be encountered.
- the load anomaly may be used to generate an alarm to check for localized faults, such as envelope leaks or light usage when the building is unoccupied.
- the estimated load profile 29 may additionally or alternatively be used in building energy control tools or software used to operate the temperature control equipment.
- a controller such as an HVAC controller 51 (See FIG. 7), may be provided for performing one or more steps of the method 20.
- the HVAC controller 51 may include a memory for storing the reduced order thermodynamic model 25, the Extended Kalman filter 27, and other data or algorithms.
- the HVAC controller 51 may further be operatively coupled to sensors or other inputs to provide the measured parameter or other data.
- the HVAC controller 51 may further be programmed to process the measured parameter using the Extended Kalman Filter 27 to estimate the at least one unknown state of the zone.
- the model-based estimation may also be applied in other applications, such as in a supermarket refrigeration system 50 schematically illustrated in FIG. 7.
- the refrigeration system 50 may include a cold room 52 for storing goods, and a fan 54 and evaporator 56 operatively coupled to the cold room for maintaining the room at a desired temperature.
- the temperature of the cold room 52 may be monitored to make sure it keeps the goods below a threshold temperature.
- the HAVC controller 51 is shown as being part of the cold room 52, it will be understood that such an illustration is merely exemplary.
- the FTVAC controller 51 may be employed in combination or conjunction with the cold room 52 (or the building itself) in other configurations.
- Fig. 8 schematically illustrates a method 60 for estimating states and predicting temperatures in the cold room 52.
- measured data (alternatively referred to herein as "known parameters") are provided by one or more sensors, such as an air temperature sensor positioned in the cold room.
- the measured data may be used in a reduced order thermodynamic model of the cold room at block 64.
- the thermodynamic model may be based on non-linear algebraic and differential equations, and may use a number of known parameters, such as the measured cold room air temperature T , and a number of unknown parameters, such as an infiltration load Q in .
- the state space formation from thermodynamics model of the cold room may be stated mathematically as:
- R Ice thermal resistance [m 2 -°C/W], with a g ⁇ (1-R) > 0 being a growth rate when defrosting is off and b g R ⁇ 0 being a decay rate when defrosting is active;
- R c Air side thermal resistance [m 2 -°C/W];
- one or more of the unknown parameters may be identified using a system identification method.
- External influences on the system behavior (which may be considered inputs to the system) are identified from measurement data and the dynamic model at the block 64.
- sensor measurement data including cold room temperature, fan status, defrosting status, and door status, are needed to identify unknown parameters such as lumped parameter H* (related to overall cold room surface heat transfer coefficient and cold room surface area), and Qd* (the energy input during defrosting).
- an Extended Kalman Filter (such as the EKF 27) based on the thermodynamic model is used to estimate unknown states and unknown parameters of the cold room.
- the unknown states may include a temperature of the goods T g00 ds in the cold room, while the unknown parameters may include R c (air side thermal resistance of the evaporator coil 56), Qizie (infiltration load), a g (a parameter indicating ice growth rate on the evaporator coil 56), and b g (a parameter indicating ice decay rate on the evaporator coil 56).
- the uncertainty of real time data may be considered during design of the EKF.
- the EKF may include time update and measurement update components. With the estimated unknown parameters from the EKF, the thermodynamic model may then be used to generate a predicted room temperature, as shown at block 70.
- the estimated states such as the estimated goods temperature T g00 ds ? an the predicted room temperature may be used for monitoring, diagnostics, or other purposes.
- the method allows monitoring personnel to automatically diagnose the root cause of a temperature alarm, such as when warmer goods are brought into the cold room, without requiring a call or other query to the store to ask for that information.
- the predicted temperature may be compared to measured real-time data, such as the cold room temperature T R , to determine whether an alarm condition is true or false. For example, when the predicted and actual room temperatures converge, the alarm may be false, whereas when they diverge, the alarm may be true.
- a controller such as a cold room controller (e.g., the HAVC controller 51) , may be provided for performing one or more steps of the method 60.
- the cold room controller may include a memory for storing the reduced order thermodynamic model of the cold room, the Extended Kalman Filter, and other data or algorithms.
- the cold room controller may further be operatively coupled to sensors or other inputs to provide the measured parameters, information regarding the unknown parameters, or other data.
- the cold room controller may further be programmed to process the measured parameter using the Extended Kalman Filter to obtain the estimated unknown state of the cold room and the estimated unknown parameter of the cold room.
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Signal Processing (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
- Air Conditioning Control Device (AREA)
- Devices That Are Associated With Refrigeration Equipment (AREA)
Abstract
A method (20) for estimating a heating/cooling load of a zone within a building (10) may include determining a measured parameter from the zone (22), generating a reduced order thermodynamic model (25) of the zone (24), generating an Extended Kalman Filter (27) based on the thermodynamic model (25) of the zone (26), and processing the measured parameter using the Extended Kalman Filter (27) to estimate at least one unknown state of the zone, such as an estimated load (28). A similar method may be used to estimate a temperature in a cold room (52) of a refrigeration system (50).
Description
MODEL BASED SYSTEM AND METHOD FOR ESTIMATING PARAMETERS AND STATES IN TEMPERATURE CONTROLLED SPACES
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This is an international patent application filed pursuant to the Patent
Cooperation Treaty claiming priority under 35 USC §119(e) to US Provisional Patent Application Serial No. 61/304,613 filed on February 15, 2010.
TECHNICAL FIELD OF THE DISCLSOURE
[0002] The present disclosure generally relates to systems and methods for controlling temperature in an interior space and, more particularly, to systems and methods for estimating parameters related to the heating and/or cooling of an interior space.
BACKGROUND OF THE DISCLSOURE
[0003] A variety of systems can be used to control temperature within a given space. HVAC systems, for example, are used to control temperature and other environmental conditions within structures such as residences, office buildings, and manufacturing plants. By way of example, environmental conditions such as temperature, humidity, air purity, air flow, enthalpy (combined value of temperature and humidity), and "fresh air" ventilation can be regulated to ensure that the interior environment of a structure is as desired for particular occupants and equipment housed in the structure, and for processes and procedures conducted within the structure. Similarly, refrigeration systems are used to maintain an interior space, such as a cold room for food storage, at a desired temperature to minimize bacteria growth or other detrimental effects to the contents stored in the space.
[0004] Conventional systems used to control temperature of a space are typically limited as to the type of parameters about which feedback is provided. Such systems may include various sensors for detecting parameters, such as temperature, in real time. The number of parameters about which data may be provided, therefore, is typically limited to those parameters that are capable of being directly measured or inferred from such measurements. The limited amount of feedback data, in turn, may limit or prevent the
ability to perform certain processes, such as system diagnostics, or materially reduce the precision and accuracy of those processes.
[0005] In certain applications, the limited feedback provided by conventional systems may lead to inefficient operation or monitoring of those systems. A supermarket refrigeration system, for example, may have a cold room for storing goods at a controlled temperature. Food quality is of primary importance to the supermarket operation, and therefore the refrigeration system may be continuously monitored to maintain a desired food temperature. In some systems, an alarm may be triggered when the room temperature exceeds a threshold value. An engineer may review alarm conditions to try to determine the root cause of the alarm, such as detecting and diagnosing possible faults in the refrigeration system. Conventional monitoring systems typically use a manual process to determine root causes for alarm signals. For example, an engineer may call the store to determine whether warmer goods were recently brought into the cold room, thereby raising the air temperature of the cold room above the threshold value.
Additionally or alternatively, the engineer may wait for a predetermined period of time to see if the air temperature returns to a safe level before determining whether the alarm is true or false. Such delay, however, may adversely affect food quality for an unnecessary period of time.
[0006] It would therefore be advantageous if an improved system for predicting cold room temperatures in an interior space is developed.
SUMMARY OF THE DISCLOSURE
[0007] A method for estimating a heating/cooling load of a zone within a building may include determining a measured parameter from the zone, generating a reduced order thermodynamic model of the zone, generating an Extended Kalman Filter based on the thermodynamic model of the zone, and processing the measured parameter using the Extended Kalman Filter to estimate at least one unknown state of the zone.
[0008] A method for estimating a temperature in a cold room of a refrigeration system may include determining a measured parameter from the cold room, generating a reduced order thermodynamic model of the cold room, the thermodynamic model including at least one unknown parameter, and identifying the at least one unknown parameter of the
thermodynamic model using a system identification method and sensor measurement data. An Extended Kalman Filter may be generated based on the thermodynamic model of the cold room, and the measured parameter may be processed using the Extended Kalman Filter to obtain an estimated unknown state of the cold room and to obtain an estimated unknown parameter of the cold room.
[0009] These are other aspects and features of the disclosure will become more apparent upon reading the following detailed description when taken in conjunction with the accompanied drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 is a schematic block diagram of a model-based method of estimating load in a building;
[0011] FIG. 2 is a schematic illustration of a building;
[0012] FIG. 3 is a schematic illustration of a floor of the building of FIG. 2;
[0013] FIG. 4 is a schematic illustration of a building thermodynamic model that may be used in the method illustrated in FIG. 1 ;
[0014] FIG. 5 is a schematic illustration of a time update and a measurement update that may be performed by a Extended Kalman Filter based estimator;
[0015] FIG. 6 is a graphical representation of an internal load profile of a building obtained from the method illustrated in FIG. 1 ;
[0016] FIG. 7 is a schematic illustration of a cold room of a supermarket refrigeration system; and
[0017] FIG. 8 is a schematic block diagram of a model-based method of predicting an air temperature in a cold room of a supermarket refrigeration system.
[0018] While the present disclosure is susceptible of various modifications and alternative constructions, certain illustrative embodiments thereof will be shown and described below in detail. It should be understood, however, that there is no intention to be limited to the specific embodiments disclosed, but on the contrary, the intention is to
cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the present disclosure.
DETAILED DESCRIPTION OF THE DRAWINGS
[0019] Referring now to the drawings, embodiments of model-based estimation are provided for improving operation, monitoring, and/or control of temperature controlled systems. In one embodiment, a thermodynamic model of a zone in a building is used in conjunction with an Extended Kalman Filter (EKF) to estimate a heating/cooling load (e.g., internal load) of the zone. Multiple estimated loads over time may be used to generate an estimated load profile, which in turn may be used in energy simulation programs or for diagnostics. In another embodiment, a thermodynamic model of a cold room is used in conjunction with an E F to estimate a temperature of goods stored in the cold room. The EKF may also estimate some unknown parameters that may be used in the thermodynamic model to generate a predicted air temperature of the cold room. The predicted air temperature may be compared with an actual measured temperature of the cold room to determine whether a triggered alarm condition is true or false.
[0020] FIG. 1 schematically illustrates a method 20 for estimating real time load in a zone of a building. The zone to which the method is applied may be scaled. That is, the zone may be defined as a single room within the building, a group of rooms, an entire floor, or the entire interior space of the building. The method may further be
simultaneously applied to multiple different zones within the same building. For example, a building 10 is schematically illustrated in FIG. 2, while a floor 12 of the building 10 is illustrated in FIG. 3. An interior zone Tzone is located on the floor 12 of the building 10. In the illustrated embodiment, the interior zone Tzone is surrounded by four neighboring zones, a north neighboring zone Tn, an east neighboring zone Te, a south neighboring zone Ts, and a west neighboring zone Tw. The method 20 may be used to estimate real time load in the interior zone Tzone of the building 10.
[0021] Returning to FIG. 1, at block 22, real time data is provided by various sensors, such as temperature sensors and airflow sensors, provided in the zone. The sensors may be provided as part of an HVAC system used to control temperature and other air qualities in the zone. Accordingly, the real time data may be taken from a Building
Management System (BMS) or similar system provided to control the HVAC system. The real time data may include temperature, airflow rate, or other qualities that may be directly measured. A plurality of the same type of sensor may be used in the neighboring zones to measure a parameter at different areas within the building. For example, multiple temperatures sensors may be provided, such as at north, south, east and west neighboring zones within the building, to provide temperature data at those multiple positions.
[0022] The real time data is input into a reduced order thermodynamic model of the building zone at block 24. An exemplary low order state space thermodynamic model 25, which may be employed within the block 24 and may be based on non-linear algebraic and differential equations, is schematically shown in FIG. 4. The model 25 uses a number of parameters, such as ambient temperature Tamb, zone well-mixed air temperature Tzone, internal surface connective heat transfer coefficient hi, external surface convective heat transfer coefficient ho, and surface area A, that may be measurable or otherwise known. Other parameters and/or states used in the model 25 may be unknown, such as outside surface temperature T0Sur, and inside surface temperature TiSur.
[0023] The thermodynamic model 25 may also be stated mathematically. The state space formation from the thermodynamics model 25 is illustrated below for the interior zone Tzone, assuming adiabatic boundary conditions for the floor and ceiling:
X = f(X, U)
y = CX (1)
Where,
U = [u1 u2 u3 u u5 u6 ] = [msa Tsa Tw Tn Te Ts ] is the input vector and y - Tzone(t) is the history of room temperature from sensor measurements. The state vector is:
Where,
Tzone: Zone well mixed air temperature [°C];
Tw: West neighboring zone air temperature [°C];
Tn: North neighboring zone air temperature [°C];
Te: East neighboring zone air temperature [°C];
Ts: South neighboring zone air temperature [°C];
Tow: West wall outside surface temperature [°C];
Tiw: West wall inside surface temperature [°C];
Ton: North wall outside surface temperature [°C];
Tin: North wall inside surface temperature [°C];
Toe- East wall outside surface temperature [°C];
Tie.- East wall inside surface temperature [°C];
Tos: South wall outside surface temperature [°C];
TjS South wall inside surface temperature [°C];
Qint : The lumped load including all equipment load, lighting load and people load (convective part), infiltration load, and load due to interzone air mixing [W],
A/. The surface area [m2], j e (w, n,e,s) is the index for surrouding zones: west, north, east and west;
hi : The internal surface convective heat transfer coefficient [W/m .°C];
h0 : The external surface convective heat transfer coefficient [W/m .°C];
majr : The supply air mass flow rate [kg/s];
mair '■ The air mass for the given zone [kg];
Tsa : The supply air temperature [°C]; and
Cpa : The specific heat capacity of dry air [J/kg.°C].
[0024] Returning back to FIG. 1, the results from the thermodynamic model 25 are then input into block 26 where an Extended Kalman Filter (EKF) 27 can be used to estimate unknown states in the process, such as loads. The EKF 27 may also estimate one or more unknown parameters and/or states used in the thermodynamic model, such as unmeasured surface temperatures. The uncertainty of real time data may be considered during design of the EKF 27.
[0025] A schematic representation of the EKF 27 is illustrated in FIG. 5. In the illustrated embodiment, the EKF 27 can include a time update 30 and a measurement update 32. In the time update 30, initial estimates of the unknown state and an error covariance are provided from the thermodynamic model 25 at time k-1. Based on the initial estimates, a predicted unknown state and a predicted error covariance at time k are generated. In the measurement update, a measured parameter is used to update the predicted unknown state and error covariance. First, a Kalman gain is computed. The predicted unknown state is then updated with the computed Kalman gain and the measured parameter. The predicted error covariance is also updated using the computed Kalman gain. The updated predicted unknown state and the updated predicted error covariance are then fed back to the time update 30, thereby to refine the thermodynamic model.
[0026] In an exemplary embodiment, the measured parameter may be a measured air temperature from the zone. The thermodynamic model 25 and EKF 27 may be used to
estimate unknown parameters, such as unmeasured room surface temperatures.
Additionally, the model 25 and EKF 27 may be used to estimate unknown states of the zone, such as loads.
[0027] At block 28, an estimated load profile 29 may be generated based on multiple load estimates taken over time. As shown in greater detail in FIG. 6, the estimated load profile 29 may chart a load, such as lumped load Qjnt, over a period of time, such as a day. The estimated load profile 29 may provide various types of information either directly or inferentially. For example, the estimated load profile 29 may facilitate a better understanding of building usage, such as occupancy, plug loads, lighting loads, and process loads, in a dynamic environment.
[0028] The estimated load profile 29 may further enable refinements to existing processes, such as building energy monitoring, diagnostic, or control tools. Building energy monitoring tools include energy simulation programs, such as the EnergyPlus® program provided by the U.S. Department of Energy, which may be used to simulate building energy use over time. The estimated load profile 29 may be provided as an input load profile to such an energy simulation program, thereby to provide a more accurate estimate of energy usage in a building. The estimated load profile 29 may also be used in a building energy diagnostics tool or program to determine faults or alarm conditions. The estimated load profile 29 may indicate load anomalies, such as an unexpectedly large load during a period of the day when such a load would not normally be encountered. The load anomaly may be used to generate an alarm to check for localized faults, such as envelope leaks or light usage when the building is unoccupied. The estimated load profile 29 may additionally or alternatively be used in building energy control tools or software used to operate the temperature control equipment.
[0029] A controller, such as an HVAC controller 51 (See FIG. 7), may be provided for performing one or more steps of the method 20. The HVAC controller 51 may include a memory for storing the reduced order thermodynamic model 25, the Extended Kalman filter 27, and other data or algorithms. The HVAC controller 51 may further be operatively coupled to sensors or other inputs to provide the measured parameter or other data. The HVAC controller 51 may further be programmed to process the measured
parameter using the Extended Kalman Filter 27 to estimate the at least one unknown state of the zone.
[0030] The model-based estimation may also be applied in other applications, such as in a supermarket refrigeration system 50 schematically illustrated in FIG. 7. The refrigeration system 50 may include a cold room 52 for storing goods, and a fan 54 and evaporator 56 operatively coupled to the cold room for maintaining the room at a desired temperature. The temperature of the cold room 52 may be monitored to make sure it keeps the goods below a threshold temperature. Notwithstanding the fact that the HAVC controller 51 is shown as being part of the cold room 52, it will be understood that such an illustration is merely exemplary. In other embodiments, the FTVAC controller 51 may be employed in combination or conjunction with the cold room 52 (or the building itself) in other configurations.
[0031] Fig. 8 schematically illustrates a method 60 for estimating states and predicting temperatures in the cold room 52. At block 62, measured data (alternatively referred to herein as "known parameters") are provided by one or more sensors, such as an air temperature sensor positioned in the cold room.
[0032] The measured data may be used in a reduced order thermodynamic model of the cold room at block 64. The thermodynamic model may be based on non-linear algebraic and differential equations, and may use a number of known parameters, such as the measured cold room air temperature T , and a number of unknown parameters, such as an infiltration load Qin. The state space formation from thermodynamics model of the cold room may be stated mathematically as:
X = f{X,U)
(4) y = CX
U = |wy (i) ud (t) ujn (t) uT
is input vector and y = TR (t) is the history of room temperature from sensor measurements. The state vector is
Where,
Uf : Fan status ON/OFF (1 or 0);
Ud : Defrosting status ON/OFF (1 or 0);
uin: Door status OPEN/CLOSE (1 or 0);
uf. Evaporator coil surface temperature [°C];
TR: Cold room air temperature [°C];
T goods'- Goods temperature [°C];
R: Ice thermal resistance [m2-°C/W], with ag · (1-R) > 0 being a growth rate when defrosting is off and bg R< 0 being a decay rate when defrosting is active;
Rc: Air side thermal resistance [m2-°C/W];
Qin: Infiltration load [W];
U: Overall heat transfer coefficient between goods and air [W/m -°C];
Mair: Thermal mass of the air in the cold room [J/°C];
Mgoods'- Thermal mass of the goods in the cold room [J/°C].
[0033] At block 66, one or more of the unknown parameters may be identified using a system identification method. External influences on the system behavior (which may be considered inputs to the system) are identified from measurement data and the dynamic
model at the block 64. In this process, sensor measurement data , including cold room temperature, fan status, defrosting status, and door status, are needed to identify unknown parameters such as lumped parameter H* (related to overall cold room surface heat transfer coefficient and cold room surface area), and Qd* (the energy input during defrosting).
[0034] At block 68, an Extended Kalman Filter (EKF) (such as the EKF 27) based on the thermodynamic model is used to estimate unknown states and unknown parameters of the cold room. The unknown states may include a temperature of the goods Tg00ds in the cold room, while the unknown parameters may include Rc (air side thermal resistance of the evaporator coil 56), Qi„ (infiltration load), ag (a parameter indicating ice growth rate on the evaporator coil 56), and bg (a parameter indicating ice decay rate on the evaporator coil 56). The uncertainty of real time data may be considered during design of the EKF. As with the previous embodiment, the EKF may include time update and measurement update components. With the estimated unknown parameters from the EKF, the thermodynamic model may then be used to generate a predicted room temperature, as shown at block 70.
[0035] The estimated states, such as the estimated goods temperature Tg00ds? an the predicted room temperature may be used for monitoring, diagnostics, or other purposes. By providing an estimated goods temperature Tg00ds, the method allows monitoring personnel to automatically diagnose the root cause of a temperature alarm, such as when warmer goods are brought into the cold room, without requiring a call or other query to the store to ask for that information. Additionally, the predicted temperature may be compared to measured real-time data, such as the cold room temperature TR, to determine whether an alarm condition is true or false. For example, when the predicted and actual room temperatures converge, the alarm may be false, whereas when they diverge, the alarm may be true.
[0036] A controller, such as a cold room controller (e.g., the HAVC controller 51) , may be provided for performing one or more steps of the method 60. The cold room controller may include a memory for storing the reduced order thermodynamic model of the cold room, the Extended Kalman Filter, and other data or algorithms. The cold room
controller may further be operatively coupled to sensors or other inputs to provide the measured parameters, information regarding the unknown parameters, or other data. The cold room controller may further be programmed to process the measured parameter using the Extended Kalman Filter to obtain the estimated unknown state of the cold room and the estimated unknown parameter of the cold room.
[0037] It will be apparent to those skilled in the art that various modifications and variations can be made in the disclosed model-based estimating systems and methods without departing from the scope of the disclosure. Embodiments other than those specifically disclosed herein will be apparent to those skilled in the art from consideration of the specification and practice of the systems and methods disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope of the disclosure being indicated by the following claims and their equivalents.
Claims
1. A method (20) for estimating a heating/cooling load of a zone within a building (10), comprising:
determining a measured parameter from the zone (22);
generating a reduced order thermodynamic model (25) of the zone (24);
generating an Extended Kalman Filter (27) based on the thermodynamic model (25) of the zone (26); and
processing the measured parameter using the Extended Kalman Filter (27) to estimate at least one unknown state of the zone (28).
2. The method of claim 1, in which the measured parameter is at least one air temperature within the zone.
3. The method of claim 1, in which the Extended Kalman Filter (27) includes a time update (30), in which the thermodynamic model (25) provides an estimated parameter and an estimated error covariance at a future time, and a measurement update (32), in which a Kalman gain is computed, the estimated parameter is updated with the measured parameter and the Kalman gain, and the estimated error covariance is updated with the Kalman gain.
4. The method of claim 1, in which the at least one unknown state is an estimated lumped load of the zone.
5. The method of claim 4, in which the estimated lumped load includes at least one of an estimated equipment load, an estimated lighting load, an estimated people load, an estimated infiltration load, and an estimated interzone mixing load.
6. The method of claim 4, in which the at least one unknown state further includes at least one estimated surface temperature associated with the zone.
7. The method of claim 4, further comprising developing a load profile (29) for the zone based on multiple estimated lumped loads provided over a period of time.
8. The method of claim 7, further comprising providing the load profile (29) as an input load profile to an energy simulation program.
9. The method of claim 1, further comprising providing an HVAC controller (51) having a memory for storing the reduced order thermodynamic model (25) and the Extended Kalman filter (27), and wherein the HVAC controller (51) is programmed to process the measured parameter using the Extended Kalman Filter (27) to estimate the at least one unknown state of the zone.
10. A method (60) for estimating a temperature in a cold room (52) of a refrigeration system (50), comprising:
determining a measured parameter from the cold room (62);
generating a reduced order thermodynamic model (25) of the cold room (52), the thermodynamic model (25) including at least one unknown parameter (64);
identifying the at least one unknown parameter of the thermodynamic model (25) using a system identification method and sensor measurement data (66);
generating an Extended Kalman Filter (27) based on the thermodynamic model (25) of the cold room (52) (68); and
processing the measured parameter using the Extended Kalman Filter (27) to obtain an estimated unknown state of the cold room (52) and to obtain an estimated unknown parameter of the cold room (52) (70).
11. The method of claim 10, in which the estimated unknown parameter of the cold room (52) comprises an estimated infiltration load.
12. The method of claim 10, in which the estimated unknown state of the cold room (52) comprises an estimated temperature of goods stored in the cold room (52).
13. The method of claim 10, further comprising generating a predicted cold room temperature using the thermodynamic model (25) and the estimated parameter of the cold room (52).
14. The method of claim 13, in which the predicted cold room temperature is compared to a measured cold room temperature to determine whether an alarm condition exists.
15. The method of claim 10, further comprising providing a cold room controller (51) having a memory for storing the reduced order thermodynamic model (25) of the cold room (52) and the Extended Kalman Filter (27), and wherein the cold room controller (51) is programmed to process the measured parameter using the Extended Kalman Filter (27) to obtain the estimated unknown state of the cold room (52) and the estimated unknown parameter of the cold room (52).
Priority Applications (6)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DK11704901.5T DK2537071T3 (en) | 2010-02-15 | 2011-02-15 | SYSTEM AND PROCEDURE BASED ON A MODEL THAT MAKES ESTIMATE PARAMETERS AND CONDITIONS IN TEMPERATURE CONTROLLED ROOMS |
NO11704901A NO2537071T3 (en) | 2010-02-15 | 2011-02-15 | |
US13/515,213 US9037302B2 (en) | 2010-02-15 | 2011-02-15 | Model based system and method for estimating parameters and states in temperature controlled spaces |
ES11704901.5T ES2672222T3 (en) | 2010-02-15 | 2011-02-15 | Model-based system and method for estimating parameters and states in controlled temperature spaces |
CN201180009750.XA CN102934036B (en) | 2010-02-15 | 2011-02-15 | For estimating the system and method based on model of parameter in temperature controlled space and state |
EP11704901.5A EP2537071B1 (en) | 2010-02-15 | 2011-02-15 | Model based system and method for estimating parameters and states in temperature controlled spaces |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US30461310P | 2010-02-15 | 2010-02-15 | |
US61/304,613 | 2010-02-15 |
Publications (2)
Publication Number | Publication Date |
---|---|
WO2011100736A2 true WO2011100736A2 (en) | 2011-08-18 |
WO2011100736A3 WO2011100736A3 (en) | 2011-11-17 |
Family
ID=44147541
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2011/024847 WO2011100736A2 (en) | 2010-02-15 | 2011-02-15 | Model based system and method for estimating parameters and states in temperature controlled spaces |
Country Status (7)
Country | Link |
---|---|
US (1) | US9037302B2 (en) |
EP (1) | EP2537071B1 (en) |
CN (1) | CN102934036B (en) |
DK (1) | DK2537071T3 (en) |
ES (1) | ES2672222T3 (en) |
NO (1) | NO2537071T3 (en) |
WO (1) | WO2011100736A2 (en) |
Cited By (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103123461A (en) * | 2011-11-03 | 2013-05-29 | 丹福斯有限公司 | A method for setting parameters in a system, in particular a heating or cooling system, device to change parameters, and heating or cooling system |
WO2013166510A1 (en) * | 2012-05-04 | 2013-11-07 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets using an engineering-based energy asset model |
US8892264B2 (en) | 2009-10-23 | 2014-11-18 | Viridity Energy, Inc. | Methods, apparatus and systems for managing energy assets |
WO2015012832A1 (en) * | 2013-07-25 | 2015-01-29 | General Electric Company | Dynamic monitoring, diagnosis, and control of cooling tower systems |
US9098876B2 (en) | 2013-05-06 | 2015-08-04 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets based on a self-tuning energy asset model |
US9159042B2 (en) | 2009-10-23 | 2015-10-13 | Viridity Energy, Inc. | Facilitating revenue generation from data shifting by data centers |
US9159108B2 (en) | 2009-10-23 | 2015-10-13 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets |
US9171276B2 (en) | 2013-05-06 | 2015-10-27 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets using an engineering-based model |
CN105094168A (en) * | 2014-05-23 | 2015-11-25 | 西卡西伯特博士及屈恩有限及两合公司 | Method and apparatus for controlling the temperature of a calibration volume of a device for comparative calibration of temperature sensors |
US9335747B2 (en) | 2009-10-23 | 2016-05-10 | Viridity Energy, Inc. | System and method for energy management |
US9367825B2 (en) | 2009-10-23 | 2016-06-14 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets based on a self-tuning energy asset model |
CN106709227A (en) * | 2015-11-12 | 2017-05-24 | 中国石油化工股份有限公司 | Random seed number preprocessing, simplex postprocessing and distributed genetic lumped kinetics method |
EP3324316A1 (en) * | 2016-11-17 | 2018-05-23 | Kabushiki Kaisha Toshiba | Parameter estimation apparatus, air-conditioning system evaluation apparatus, parameter estimation method, and program |
US10025331B2 (en) | 2012-05-15 | 2018-07-17 | Passivsystems Limited | Predictive temperature management system controller |
US10126009B2 (en) | 2014-06-20 | 2018-11-13 | Honeywell International Inc. | HVAC zoning devices, systems, and methods |
EP3584666A1 (en) * | 2013-04-19 | 2019-12-25 | Google LLC | Controlling an hvac system in association with a demand-response event |
US10581862B2 (en) | 2013-03-15 | 2020-03-03 | Google Llc | Utility portals for managing demand-response events |
US10718539B2 (en) | 2013-03-15 | 2020-07-21 | Google Llc | Controlling an HVAC system in association with a demand-response event |
US11282150B2 (en) | 2013-03-15 | 2022-03-22 | Google Llc | Systems, apparatus and methods for managing demand-response programs and events |
Families Citing this family (24)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP5093378B2 (en) * | 2011-05-12 | 2012-12-12 | ダイキン工業株式会社 | Ventilation system |
US9568204B2 (en) * | 2013-01-31 | 2017-02-14 | Johnson Controls Technology Company | Systems and methods for rapid disturbance detection and response |
US9852481B1 (en) * | 2013-03-13 | 2017-12-26 | Johnson Controls Technology Company | Systems and methods for cascaded model predictive control |
US9436179B1 (en) | 2013-03-13 | 2016-09-06 | Johnson Controls Technology Company | Systems and methods for energy cost optimization in a building system |
US9235657B1 (en) * | 2013-03-13 | 2016-01-12 | Johnson Controls Technology Company | System identification and model development |
DE102013106806A1 (en) * | 2013-06-28 | 2014-12-31 | Berlinovo Immobilien Gesellschaft mbH | METHOD FOR REGULATING THE CLIMATE IN A BUILDING USING AT LEAST ONE HOUSE OR PROCESS PLANT |
GB2520293B (en) * | 2013-11-14 | 2018-02-07 | Passivsystems Ltd | Improvements in and relating to temperature controlled systems |
US10871756B2 (en) * | 2014-08-26 | 2020-12-22 | Johnson Solid State, Llc | Temperature control system and methods for operating same |
US10274915B2 (en) | 2014-10-22 | 2019-04-30 | Carrier Corporation | Scalable cyber-physical structure management |
WO2016076946A2 (en) * | 2014-11-12 | 2016-05-19 | Carrier Corporation | Automated functional tests for diagnostics and control |
US10332026B2 (en) | 2014-11-26 | 2019-06-25 | International Business Machines Corporation | Building thermal control techniques |
CN104809333B (en) * | 2015-04-03 | 2017-08-29 | 百度在线网络技术(北京)有限公司 | Capacity prediction methods and system based on Kalman filter |
WO2017004286A1 (en) * | 2015-07-02 | 2017-01-05 | Pacecontrols Llc | Method, controllers, and systems for energy control and savings estimation of duty cycled hvac&r equipment |
CN105184094B (en) * | 2015-09-23 | 2018-06-19 | 华南理工大学建筑设计研究院 | A kind of building periphery Temperature prediction method |
CN106709226A (en) * | 2015-11-12 | 2017-05-24 | 中国石油化工股份有限公司 | Distribution heredity lumping kinetic method with random function preprocessing and least square postprocessing |
US9978114B2 (en) | 2015-12-31 | 2018-05-22 | General Electric Company | Systems and methods for optimizing graphics processing for rapid large data visualization |
US10619879B2 (en) | 2018-03-21 | 2020-04-14 | Mitsubishi Electric Research Laboratories, Inc. | System and method for controlling operations of air-conditioning system |
US11210591B2 (en) * | 2019-01-04 | 2021-12-28 | Johnson Controls Tyco IP Holdings LLP | Building control system with automated Kalman filter parameter initiation and system identification |
CN110285532B (en) * | 2019-07-04 | 2021-07-30 | 中国工商银行股份有限公司 | Machine room air conditioner control method, device and system based on artificial intelligence |
CN110567132B (en) * | 2019-09-29 | 2021-01-08 | 珠海格力电器股份有限公司 | Regional control method, device and system and air conditioning system |
CN110925974B (en) * | 2019-12-09 | 2021-08-03 | 广东美的暖通设备有限公司 | Air conditioner and control method and control device for output parameters of air conditioner |
US11359950B2 (en) | 2019-12-10 | 2022-06-14 | Johnson Controls Tyco IP Holdings LLP | Reduced length valve assembly with ultrasonic flow sensor |
US11639804B2 (en) | 2019-12-13 | 2023-05-02 | Trane International Inc. | Automated testing of HVAC devices |
US11644212B2 (en) * | 2020-11-12 | 2023-05-09 | International Business Machines Corporation | Monitoring and optimizing HVAC system |
Family Cites Families (21)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4649711A (en) | 1985-09-03 | 1987-03-17 | Carrier Corporation | Apparatus and method for infrared optical electronic qualitative analysis of a fluid independent of the temperature thereof |
US4829779A (en) * | 1987-12-15 | 1989-05-16 | Hussmann Corporation | Interface adapter for interfacing a remote controller with commercial refrigeration and environmental control systems |
JP4461550B2 (en) * | 2000-02-16 | 2010-05-12 | ダイキン工業株式会社 | Air conditioning load prediction method and apparatus |
US6892546B2 (en) * | 2001-05-03 | 2005-05-17 | Emerson Retail Services, Inc. | System for remote refrigeration monitoring and diagnostics |
US6981385B2 (en) | 2001-08-22 | 2006-01-03 | Delaware Capital Formation, Inc. | Refrigeration system |
US6863222B2 (en) * | 2002-09-04 | 2005-03-08 | Statrak Llc | System and method for freight refrigeration power control |
KR20040064452A (en) * | 2003-01-13 | 2004-07-19 | 엘지전자 주식회사 | Multi-type air conditioner for cooling/heating the same time |
CA2419647A1 (en) * | 2003-02-21 | 2004-08-21 | Jean-Pierre Gingras | Walk-in cooler control and monitoring system |
US7775452B2 (en) * | 2004-01-07 | 2010-08-17 | Carrier Corporation | Serial communicating HVAC system |
US20060026975A1 (en) | 2004-02-11 | 2006-02-09 | John Bunch | Wireless system for preventing condensation on refrigerator doors and frames |
US7152415B2 (en) | 2004-03-18 | 2006-12-26 | Carrier Commercial Refrigeration, Inc. | Refrigerated compartment with controller to place refrigeration system in sleep-mode |
US7905100B2 (en) * | 2004-12-16 | 2011-03-15 | Danfoss A/S | Method for controlling temperature in a refrigeration system |
US7881889B2 (en) * | 2005-12-21 | 2011-02-01 | Barclay Kenneth B | Method and apparatus for determining energy savings by using a baseline energy use model that incorporates an artificial intelligence algorithm |
US9261299B2 (en) | 2006-09-22 | 2016-02-16 | Siemens Industry, Inc. | Distributed microsystems-based control method and apparatus for commercial refrigeration |
EP2079970A1 (en) | 2006-10-31 | 2009-07-22 | Carrier Corporation | Detection of refrigerant release in co2 refrigerant systems |
US20080148751A1 (en) * | 2006-12-12 | 2008-06-26 | Timothy Dean Swofford | Method of controlling multiple refrigeration devices |
EP2012069A1 (en) * | 2007-06-04 | 2009-01-07 | RHOSS S.p.A. | Method for regulating the delivery temperature of a service fluid in output from a refrigerating machine |
US8393169B2 (en) * | 2007-09-19 | 2013-03-12 | Emerson Climate Technologies, Inc. | Refrigeration monitoring system and method |
JP2009139028A (en) | 2007-12-07 | 2009-06-25 | Sanyo Electric Co Ltd | Control device and control method for control device |
JP2009210161A (en) * | 2008-02-29 | 2009-09-17 | Sanyo Electric Co Ltd | Equipment control system, control device, and control program |
US20130245847A1 (en) * | 2009-10-23 | 2013-09-19 | Alain P. Steven | Facilitating revenue generation from wholesale electricity markets using an enineering-based energy asset model |
-
2011
- 2011-02-15 CN CN201180009750.XA patent/CN102934036B/en not_active Expired - Fee Related
- 2011-02-15 NO NO11704901A patent/NO2537071T3/no unknown
- 2011-02-15 WO PCT/US2011/024847 patent/WO2011100736A2/en active Application Filing
- 2011-02-15 US US13/515,213 patent/US9037302B2/en active Active
- 2011-02-15 EP EP11704901.5A patent/EP2537071B1/en not_active Not-in-force
- 2011-02-15 DK DK11704901.5T patent/DK2537071T3/en active
- 2011-02-15 ES ES11704901.5T patent/ES2672222T3/en active Active
Cited By (33)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9335747B2 (en) | 2009-10-23 | 2016-05-10 | Viridity Energy, Inc. | System and method for energy management |
US8892264B2 (en) | 2009-10-23 | 2014-11-18 | Viridity Energy, Inc. | Methods, apparatus and systems for managing energy assets |
US9367052B2 (en) | 2009-10-23 | 2016-06-14 | Viridity Energy, Inc. | Managing energy assets associated with transport operations |
US9159042B2 (en) | 2009-10-23 | 2015-10-13 | Viridity Energy, Inc. | Facilitating revenue generation from data shifting by data centers |
US9159108B2 (en) | 2009-10-23 | 2015-10-13 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets |
US9367825B2 (en) | 2009-10-23 | 2016-06-14 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets based on a self-tuning energy asset model |
CN103123461A (en) * | 2011-11-03 | 2013-05-29 | 丹福斯有限公司 | A method for setting parameters in a system, in particular a heating or cooling system, device to change parameters, and heating or cooling system |
WO2013166510A1 (en) * | 2012-05-04 | 2013-11-07 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets using an engineering-based energy asset model |
US10025331B2 (en) | 2012-05-15 | 2018-07-17 | Passivsystems Limited | Predictive temperature management system controller |
US11282150B2 (en) | 2013-03-15 | 2022-03-22 | Google Llc | Systems, apparatus and methods for managing demand-response programs and events |
US11739968B2 (en) | 2013-03-15 | 2023-08-29 | Google Llc | Controlling an HVAC system using an optimal setpoint schedule during a demand-response event |
US11308508B2 (en) | 2013-03-15 | 2022-04-19 | Google Llc | Utility portals for managing demand-response events |
US10718539B2 (en) | 2013-03-15 | 2020-07-21 | Google Llc | Controlling an HVAC system in association with a demand-response event |
US10581862B2 (en) | 2013-03-15 | 2020-03-03 | Google Llc | Utility portals for managing demand-response events |
EP3584666A1 (en) * | 2013-04-19 | 2019-12-25 | Google LLC | Controlling an hvac system in association with a demand-response event |
EP3961342A1 (en) * | 2013-04-19 | 2022-03-02 | Google LLC | Controlling an hvac system in association with a demand-response event |
US9171276B2 (en) | 2013-05-06 | 2015-10-27 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets using an engineering-based model |
US9098876B2 (en) | 2013-05-06 | 2015-08-04 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets based on a self-tuning energy asset model |
KR20160034413A (en) * | 2013-07-25 | 2016-03-29 | 제네럴 일렉트릭 컴퍼니 | Dynamic monitoring, diagnosis, and control of cooling tower systems |
WO2015012832A1 (en) * | 2013-07-25 | 2015-01-29 | General Electric Company | Dynamic monitoring, diagnosis, and control of cooling tower systems |
US10041736B2 (en) | 2013-07-25 | 2018-08-07 | Bl Technologies, Inc. | Dynamic monitoring, diagnosis, and control of cooling tower systems |
KR102189649B1 (en) * | 2013-07-25 | 2020-12-11 | 비엘 테크놀러지스 인크. | Dynamic monitoring, diagnosis, and control of cooling tower systems |
CN105094168A (en) * | 2014-05-23 | 2015-11-25 | 西卡西伯特博士及屈恩有限及两合公司 | Method and apparatus for controlling the temperature of a calibration volume of a device for comparative calibration of temperature sensors |
US9970829B2 (en) | 2014-05-23 | 2018-05-15 | SIKA Dr. Sieber & ëhn GmbH & Co. KG | Method and apparatus for controlling the temperature of a calibration volume of a device for comparative calibration of temperature sensors |
EP2947439A1 (en) * | 2014-05-23 | 2015-11-25 | SIKA Dr.Siebert & Kühn GmbH & Co. KG. | Method and device for controlling the temperature of the calibrating volume of a device for comparative calibrating of temperature sensors |
US10242129B2 (en) | 2014-06-20 | 2019-03-26 | Ademco Inc. | HVAC zoning devices, systems, and methods |
US10151502B2 (en) | 2014-06-20 | 2018-12-11 | Honeywell International Inc. | HVAC zoning devices, systems, and methods |
US10126009B2 (en) | 2014-06-20 | 2018-11-13 | Honeywell International Inc. | HVAC zoning devices, systems, and methods |
US10915669B2 (en) | 2014-06-20 | 2021-02-09 | Ademco Inc. | HVAC zoning devices, systems, and methods |
US11692730B2 (en) | 2014-06-20 | 2023-07-04 | Ademco Inc. | HVAC zoning devices, systems, and methods |
CN106709227A (en) * | 2015-11-12 | 2017-05-24 | 中国石油化工股份有限公司 | Random seed number preprocessing, simplex postprocessing and distributed genetic lumped kinetics method |
EP3324316A1 (en) * | 2016-11-17 | 2018-05-23 | Kabushiki Kaisha Toshiba | Parameter estimation apparatus, air-conditioning system evaluation apparatus, parameter estimation method, and program |
US11574102B2 (en) | 2016-11-17 | 2023-02-07 | Kabushiki Kaisha Toshiba | Parameter estimation apparatus, air-conditioning system evaluation apparatus, parameter estimation method, and non-transitory computer readable medium |
Also Published As
Publication number | Publication date |
---|---|
US20120330465A1 (en) | 2012-12-27 |
US9037302B2 (en) | 2015-05-19 |
EP2537071A2 (en) | 2012-12-26 |
ES2672222T3 (en) | 2018-06-13 |
WO2011100736A3 (en) | 2011-11-17 |
CN102934036B (en) | 2016-04-27 |
NO2537071T3 (en) | 2018-10-20 |
CN102934036A (en) | 2013-02-13 |
EP2537071B1 (en) | 2018-05-23 |
DK2537071T3 (en) | 2018-06-25 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
EP2537071B1 (en) | Model based system and method for estimating parameters and states in temperature controlled spaces | |
US10107513B2 (en) | Thermodynamic modeling for enclosures | |
US9664400B2 (en) | Automated technique of measuring room air change rates in HVAC system | |
Yoon et al. | Impacts of HVACR temperature sensor offsets on building energy performance and occupant thermal comfort | |
JP5943255B2 (en) | Energy management device and energy management system | |
US9639072B2 (en) | Temperature gradient reduction using building model and HVAC blower | |
US20150041550A1 (en) | Air conditioning controlling device and method | |
DK2612124T3 (en) | DETERMINING THE HEATING COFFEE EFFICIENT OF A LOCATION | |
US20100286843A1 (en) | Air conditioning system control | |
CN107110539A (en) | The abnormality determination method of the control device of air-conditioning system, air-conditioning system and air-conditioning system | |
WO2020179088A1 (en) | Air conditioning management device, air conditioning management system, air conditioning management method, and program | |
UA121205C2 (en) | Method and device for determining the heat loss coefficient of a room | |
JP2009150640A (en) | Method of measuring cooling capacity of air conditioning system using package type air conditioner | |
US20110257926A1 (en) | Method for the analysis of the thermal behaviour of a structure and associated system | |
JP4879814B2 (en) | Method for estimating state in closed space, and device for monitoring temperature state of thermostat using the method | |
Woradechjumroen et al. | Virtual partition surface temperature sensor based on linear parametric model | |
JP2016114327A (en) | Sensor diagnostic device and sensor diagnostic method | |
O'Neill et al. | Model-based estimation of cold room temperatures in a supermarket refrigeration system | |
JP2004234302A (en) | Process management device | |
Omarov et al. | Fuzzy-PID based self-adjusted indoor temperature control for ensuring thermal comfort in sport complexes | |
JP2016056973A (en) | Air conditioning control device, air conditioning control method and air conditioning control program | |
WO2017175406A1 (en) | Air-conditioner blowout temperature estimation device and program | |
JP4434905B2 (en) | Process management device | |
JP2015090232A (en) | Air conditioning system, and program | |
JP2006343031A (en) | Air conditioner, and its control method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
WWE | Wipo information: entry into national phase |
Ref document number: 201180009750.X Country of ref document: CN |
|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 11704901 Country of ref document: EP Kind code of ref document: A2 |
|
WWE | Wipo information: entry into national phase |
Ref document number: 13515213 Country of ref document: US |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2011704901 Country of ref document: EP |