Patent Application
20150178421
This invention relates to controlled building environments. More particularly, this invention relates to modeling, monitoring, commissioning, and adjusting heating, ventilation, and air conditioning systems and components in buildings.
Environments within office and other buildings are controlled, in part, using heating, ventilation, and air conditioning (HVAC) systems. When components of these HVAC systems are installed, they must be tested to ensure that they are functioning properly. For example, they must be tested to ensure that heating systems and components and cooling systems and components bring circulating air or fluids to their correct temperatures within specific time limits, that vents open on and close on command, and that fans circulate sufficient volumes of air within closed spaces.
Typically, this testing is performed manually. HVAC equipment installers measure the environmental data (e.g., temperature, humidity, air flow, and air flow rate). The installers use power and other meters to measure these data within building zones and visually check that the vents and equipment operate according to specifications. For a medium-sized office building this testing can take weeks; for a large office building, it can take months.
When the environmental and system performance data are eventually collected, the data can be used to model the HVAC units and their sub-components, though not efficiently. Most models are configuration specific. Among other things, the models depend on the types of HVAC components, their spatial locations within the zones, and the actions for controlling these components. For example, overhead air distribution systems use a different set of actuators than under-floor air distribution systems, both of which use a different set of actuators than water-based radiators for air conditioning. Because prior art models must take all of these factors into account, the methods for generating them are time consuming, computationally difficult and intensive, and error-prone.
Furthermore, these models are static. Once generated, they are not updated to reflect operating changes in HVAC components, such as due to age, damage, or current external weather or operating load. Nor are they updated to reflect changes in building occupancy, such as when additional staff move into or are relocated within a building.
In accordance with the principles of the invention, performance models of HVAC systems and sub-systems are modeled more efficiently and accurately. (To simplify the discussion that follows, references to “HVAC components” include HVAC systems and sub-systems.) In one embodiment, HVAC performance models are generated using power readings from the building's power meter, rather than requiring separate meters for each component in the building. These models are derived independently of the spatial locations of the components, the types of components, and the methods for controlling the components, and are thus derived faster and with fewer resources. Once these models are generated, they can be used for different purposes, such as automatic testing, to ensure that the HVAC components are working properly; adaptively updating the models; and generating reports detailing cost savings based on adjustments to the environmental conditions.
In a first aspect of the invention, a method models performance of an electro-mechanical component that controls an environment within one of multiple zones in a building. The method includes varying an input to the electro-mechanical component to generate associated outputs from the electro-mechanical component and generating a performance model of the electro-mechanical component based on the input, the associated outputs, and an energy consumption of the electro-mechanical component. In one embodiment, the energy consumption of the electro-mechanical component is determined from an energy consumption of the building while varying the input. In one embodiment, the method also includes measuring the energy consumption of the building.
Preferably, the performance model characterizes power consumed by the electro-mechanical component as a function of at least one of temperature and air flow rate. As some examples, the multiple electro-mechanical components include a fan, a chiller, a reheat valve, a packaged air-conditioning unit, or any combination thereof. In one embodiment, the output corresponds to steady-state and dynamic performance.
In one embodiment, the electro-mechanical component is one of multiple electro-mechanical components within the multiple zones in the building. The method also includes, while varying the input to the electro-mechanical component, maintaining outputs of remaining ones of the multiple electro-mechanical components at a preselected condition. In one embodiment, the preselected condition corresponds to a low-power state of the remaining ones of the electro-mechanical components, such that they draw minimal, if any, power. The energy consumption includes electrical consumption, gas consumption, or both. In one embodiment, the associated outputs correspond to air flows, air temperatures, rates of increase of air temperature, rates of increase of air flow, or any combination thereof.
In one embodiment, the performance model includes a nonlinear partial differential equation or an autoregression-moving-average model. In one embodiment, the nonlinear partial differential equation includes a Navier-Stokes equation and its linear approximation. In one embodiment, the performance model is generated from constrained least square, unconstrained least square, linear optimization, nonlinear optimization, Kalman filtering, or any combination thereof.
In one embodiment, the method also includes receiving commands from a controller for varying the inputs and restoring a prior input to the electro-mechanical component when communication between the controller and electro mechanical component is interrupted. Preferably, the method also includes using a heartbeat initiated by the controller to detect that communication between the controller and the electro-mechanical component is interrupted. In one embodiment, the controller and the electro-mechanical component are communicatively coupled over the Internet or a corporate cloud.
Preferably, the commands are in an abstraction language and include checking whether operating conditions are met before varying the inputs. As one example, the operating conditions include determining that there is pressure in a duct before varying the damper in the zone.
In one embodiment, the method also includes determining performance models for each of the multiple electro-mechanical components within corresponding ones of the multiple zones, thereby generating multiple performance models and combining the multiple performance models to generate a performance model for the building.
In one embodiment, the method includes generating a report summarizing energy savings or cost savings for any one or more of the multiple electro-mechanical components based on a set of environmental settings.
The performance model is able to be used in a variety of ways, such as for model-based control, fault detection, system design, system and component testing, automatic PID gains tuning, or any combination thereof.
In a second aspect of the invention, a method characterizes performance of a building component. The method includes choosing a set of inputs and one output for the building component; selecting a set of steady-state operation points for each input and a duration at each of the steady-state operation points; and characterizing a performance of the component based on a log of the steady-state operation, historical performance data and data sheets for the component.
In a third aspect of the invention, a method adaptively updates a performance model for a heating, ventilation, and air-conditioning (HVAC) unit. The method includes determining a model characterizing performance of an HVAC unit; automatically, periodically driving the HVAC unit with inputs and measuring associated outputs from the HVAC unit; and using the inputs and associated outputs to update the performance model. Preferably, determining the model characterizing the performance of the HVAC unit is based on historical performance data for the HVAC unit.
In one embodiment, the HVAC unit is driven with inputs using commands in an abstraction language. The abstraction language translates a source command to drive the HVAC unit from a format not supported by the HVAC unit into one or more target commands in a format that is supported by the HVAC unit.
In one embodiment, the method also includes logically inserting an agent, comprising computer-executable instructions for step-testing and controlling the HVAC unit, within normal-operating computer-executable instructions for controlling the HVAC unit. Preferably, the agent includes a heart-beat monitor, for monitoring a connection between the HVAC unit and a platform.
In a fourth aspect of the invention, a method of adaptively managing a performance model for a heating, ventilation, and air-conditioning (HVAC) component includes (a) generating a descriptive model of the HVAC component, (b) generating an abstract of data and control mapping for the HVAC component, (c) calculating parameters for the model, (d) optimizing performance for the model based on pre-determined criteria, (e) simulating the optimization, (f) applying the optimization to the model, and (g) repeating steps (a) through (f) until a measured state of the HVAC component matches an expected state of the HVAC component.
In a fifth aspect of the invention, an electro-mechanical component controls an environment within a zone in a building. The component includes a thermal element for controlling a thermal environment in the zone; a sensor for measuring a characteristic of the thermal environment in the zone; and a controller that varies an input to the mechanical component to generate a corresponding output of the thermal element within the zone and generates a performance model for the electro-mechanical component based on energy consumption of the building components, the input, and the output. The controller includes a processor and a computer-readable medium containing computer-executable instructions that when executed by the processor varies an input to the mechanical component to generate a corresponding output of the thermal element within the zone and generates a performance model for the electro-mechanical component based on the input, the output, and an energy consumption of the building. The component forms part of a fan, a chiller, a reheat valve, a packaged air-conditioning unit, or any combination thereof.
The figures are used merely to illustrate embodiments of the invention and are not meant to be limiting in any way. Throughout the figures, the same label refers to the same or similar element.
Under operation of the control platform 120, the thermal environment in a selected zone (e.g., zone 1) is driven to predetermined set points (e.g., temperature=65° F., air flow rate=10 m3/min) using a corresponding HVAC (e.g., HVAC1), and the thermal environment is measured using the sensor in the selected zone (e.g., sensor 1). During this process, the remaining HVACs (e.g., HVAC2-5) are maintained in a low-power state (e.g., an OFF state) in which they draw little power. In this way, characterization of the HVACi components and the corresponding zone will reflect only the corresponding component and/or zone.
The remaining components are sequentially modeled in the same way. For example, the characteristics of the HVAC2 component is modeled while HVAC1 and HVAC3-5 are in a low power state, etc. In a large system, sufficiently decoupled zones may be modeled in parallel.
The control platform 120 is able to characterize steady-state and dynamic responses of the HVAC components in the zones 1-5. Some examples of these characteristics include power consumed (and thus cost) as a function of thermal characteristics, temperature output into a zone as a function of air flow and temperature input into the zone, change in temperature within a zone as a function of air flow into the zone, etc. After reading this disclosure, those skilled in the art will recognize other models that can be generated and used in accordance with the principles of the invention. Furthermore, while
Some examples of HVAC components include packaged air-conditioning units, chillers, fans, and re-heat valves. Zones include partitioned offices, non-partitioned spaces that have independent thermal characteristics, or any other elements of a “building fabric.”
Preferably, to inconvenience the occupants of the group of zones 100 as little as possible, the thermal environments of the zones 1-5 are modeled when the group of zones 100 is relatively unoccupied, such as at night, on the weekends, or during holidays.
While
As explained above, the BMS 105 exchanges data and commands with components in the building 101. In some embodiments, functionally, the interface between the two appears as a cloud network, shown schematically as cloud network 102.
The control platform 250 includes several functional layers 260-264. The application layer 260 includes a suite of services. The integration layer 261 includes a tools sub-layer 261A and an infrastructure and management sub-layer 261 B. The foundation layer 262 includes a first sub-layer 262A and a second sub-layer 262B. The first sub-layer 262A includes communications, models, and data, and the second sub-layer 262B includes connectivity, security, components, system, and historical data.
The tools sub-layer 261A includes a usage-report generator, summarizing power consumption for different environment settings, such as described below. The sub-layer 262A includes performance models, such as described herein, data, including current environmental data recently received from the agents 210A, 215A, and 220A, and historical data, including environmental data and entire building data, previously received from the agents 201A, 215A, and 220A.
While
Performance models generated in accordance with the principles of the invention include steady-state and dynamic performance models, which are generated without requiring additional sensors. HVAC components modeled in accordance with embodiments of the invention include packaged air-conditioning units, chillers, fans, re-heat valves, thermal spaces, and thermal zones. The component behavior characteristics include the response time of the heating sub-system controller by a zone re-heat valve. In one embodiment, these characteristics are generated using only a temperature sensor in the zone and the power meter for the entire building, though in other embodiments, additional sensors or power meters can be used. Steady-state performance includes characteristics, such as thermal resistance, thermal capacitance, and component efficiency. Dynamic performance can be determined for characteristics such as output as a function of the temperature of air entering a zone, the temperature of air leaving a zone, and ambient air temperature in the zone.
The component performance models derived from the methods can be used in a number of model-based technologies, such as model-based control, fault-detection, model-based system design, and automatic PID gains tuning, just to name a few examples.
Pwr=fpu, k(flowtotal, TS, Tmixed) (1)
In this example, the sub-components of the packaged unit 300 are also modeled. When modeling some components, certain “constraints” or “prerequisites” must be met for accurate modeling. As one example, to prevent a heater from overheating, the heater will not be turned ON until an associated fan is turned ON. Other constraints are based on the limitations of mathematical modeling. In this example, constraints for the fan 310 model include (1) power is required only when the fan is ON, and (2) the model fits the nth order polynomial: Pwrfan=poly(mtot). Constraints for the compressor (301 and 302) model include (1) maximum power is required for each compressor and each compressor's status signal, (2) the compressor's power is estimated by fitting the scheduled polynomial models: Pwrcomp=slope*max(TS−Tmixed), and slope=poly(Tmixed, mtot), and (3) the poly( ) function is required to fit bin slopes as a function of inputs (TS and Tmixed) in given input ranges.
T
zi
=f
zone((flowi, Tsi) (2)
(m)dTz/dt=dQ/dt+cp(dms/dt)(Ts−Tz) (3)
The equation is derived from two coupled states, Tmass and Tzone, derived from equations (4) and (5):
adT
z
/dt=dQ/dt+b(dms/dt)(Ts−Tz)+γ(Tmass−Tz) (4)
T
mass
=c+kT
amb(t−δ) (5)
Parameter identifiers (PIDs), for both historical and real-time data, can also be modeled in accordance with the principles of the invention. For a PID, the Tmass parameters are given by equation (6):
T
mass
=c+kT
amb(t−δ) (6)
In this example, during a period of no airflow and no transients, Tmass=Tzone. The term δ is estimated from the time between peaks in Tamb and Tzone. The terms c and k are estimated from least squares. The term k is classified as a function of Tamb max−Tamb min over 24 hours. In this example, higher order dynamic models (linear and nonlinear optimal experimental design (OED)) are also tested. The Tz parameter is determined from equation (7):
mKc
p
dTz/dt=dQ/dt+b(dms/dt)(Ts−T)+γ(Tmass−Tz) (7)
where cp is the heat capacity of air, determined from equations (8) and (9):
(mc)dTz/dt=dQ/dt+cp(dms/dt)(Ts−Tz) (8)
m=rho*volume (9)
where, for example, the height of a zone=9 feet. The terms m and b are the zone size. The term y is the time constant to reach Tmass. The term γ is estimated when the system turns OFF (e.g., at the end of the day), from the time it reaches Tz
The real-time PID is characterized using equations (10) and (11):
T
mass
=c+kT
amb(t−δ) (10)
(a)dTz/dt=dQ/dt+b(dms/dt)(Ts−Tz)+γ(Tmass−Tz) (11)
Preferably, these equations are re-estimated every day, dQ/dt is forecast, and are re-scheduled and re-learned. Preferably, higher-order linear and nonlinear OED are also tested.
These examples of modeling components is merely illustrative. After reading this disclosure, those skilled in the art will recognize other modeling methods and associated equations and other components that can be modeled in accordance with the principles of the invention.
In accordance with one embodiment of the invention, performance models are used to perform automated “commissioning” of commercial buildings. During these step-tests, HVAC components are driven with pre-determined signals and the outputs are measured to ensure that the components are operating properly, as intended by the building designers, engineers, or contractors. Because this commissioning is performed automatically and can be triggered remotely, it can be performed on short notice, for reduced costs, and with increase accuracy. In one embodiment, auto-commissioning uses a platform, such as described above (e.g., 120 or 250).
Next, in the step 715, an abstraction system, with settings rollback, is used to obtain the desired operation. As explained in more detail below, the settings rollback are values that the inputs are returned to in case of a communication error during the auto-commissioning. The values are also used to return the system to its normal operating configuration. The abstraction system is able to solve an optimization problem to generate the steady-state request. As one example, if it is desired that the fan in this example, but neither compressor, is to be turned ON, the T_supply set point must be specified to ensure that the compressors do not turn ON. As another example, a command cannot be given to start the fan at minimum flow since, generally, no such command exists. Instead, to turn ON the fan, the abstraction layer is able to set the fan at minimum total flow. The abstraction layer will then determine that, to generate this output, all the zones must be to set to minimum flow. As also explained below, the abstraction layer thus translates the original “source” command to a “target” command to turn he flow in all the zones to minimum flow.
After the step 715, in the step 720, it is ensured that the entire operation can be obtained during one or more pre-determined time periods, such as late at night when the building is unoccupied, on weekends, or during holidays. These time periods can be automatically learned from the data, or provided as input, such as from the building manager, an automated schedule, or the commissioning agent.
After the step 720, in the step 725, the components are characterized using, for example, the log of the modified operations, the building's historical data, and component data sheets. In the step 725, the component performance characterization includes choosing a model and a technique to characterize the model. Examples of these models include differential equations, auto-regression-moving-average mode (ARMAX). Examples of techniques include constrained and unconstrained least square, linear and non-linear optimization, and Kalman filtering. The technique uses building historical data to remove measured effects, which do not depend on the modified operation. This includes, for example, instance power meter, packaged unit consumption, and lighting consumption, as just a few examples. The component data sheets are used as an initial-guess for the identification technique.
Together, the performance models of the HVAC components are able to be combined to characterize an interconnected system of building components.
In one embodiment, the functional description of components, whether user driven or data driven, is developed using a graphical user interface (GUI). Using the methods described above, constraints on system operation (e.g., minimum and maximum values) are established. These functional descriptions, with the constraints, with generic models (e.g., empty or populated with default data) are used to automatically generate step-test procedures to isolate components to determine both static and dynamic performance characteristics. In this way, these performance models are able to used for advanced system control.
In one embodiment, the feedback in the step 815 is provided to automated commission users. Some examples of user feedback include information about the malfunctioning building components and actionable information on how to resolve the malfunction. The new tests that are optionally executed in the step 815 use one or more of the log of the step-test operations, building historical data, component data sheets, and component models.
As one example, a building model auto-commissioned using the process 800 is used to access thermal coupling and the time to reach steady-state. This information is then used to decide which thermal zones to step test in parallel and for how long, with the goal of reducing total testing time while minimizing coupling effects.
In one embodiment, a platform includes one or more processors and computer-readable media storing computer-executable instructions that when executed by the processor perform the steps 700 and 800. It will be appreciated that the platform can be a single platform or a distributed one.
It will also be appreciated that the steps 700 and 800 are merely illustrative of one embodiment of the invention. After reading this disclosure, those skilled in the art will recognize other modifications in accordance with the principles of the invention. For example, in other embodiments, some of the steps 700 and 800 are deleted, other steps are added, and some steps are performed in different orders.
In accordance with the principles of the invention, performance models for multiple HVAC components are generated by using power measured using a single power meter (e.g., electrical, gas, or a combination of both) for the entire building. In accordance with different embodiments, utility data are used to identify the time frame with minimum based load variation (e.g., when no one is in the building). As one example, step tests are performed on a fan during the time frame (active fan, inactive fan). For each period of inactivity, the base load is estimated. The estimated base load is then used to detrend the power meter reading. This data is then used for fitting the polynomial models.
In accordance with the principles of the invention, HVAC models are able to be managed and adaptively controlled. An HVAC optimization system in accordance with one embodiment of the invention continuously gathers data about the operation of the underlying system and compares real system behavior with expected behavior based on model simulations and heuristics. This allows the system to alert an operator when physical faults in the system or condition changes in the system invalidate the current optimization model. The adaptive system can then be remodeled and re-optimized according to the new conditions present in the underlying system.
The model abstraction module 920 receives as input measurements and controls of the modeled system and maps the data and control signals to points in the descriptive model. The model stores enough data to parameterize the model. The model abstraction module 920 also partitions the data by explicit or implicit changes in the set of conditions of the system being measured. These changes can result from system wear, new parameters for local control of the system, or replacement of the lower-level control programs or equipment. As some examples, partitioning criteria include the time of day, the time of year, occupied status of the serviced space, and particular uses of the serviced space. The model abstraction module 920 is able to apply and roll back settings to the underlying control system.
The model and abstraction module 920 is also able to map a series of settings to an abstract setting. For example, when the optimization engine wishes to set an airflow in the underlying system, but the underlying system doesn't offer access to the airflow control, the model is able to manipulate other settings, such as damper position or set point temperature to accomplish the desired action in the underlying system. One example would be to limit the electrical power use of the system.
The model parameter calculator 925 calculates model parameters using statistical analysis of collected data from each relevant partition of conditions. The optimization module 930 optimizes the condition of the system against a desired set of criteria or exercises the system in some way. As some examples, the criteria include a combination of minimized energy usage, minimized utility costs, and acceptable environmental conditions within the served building. The outputs of the optimization module 930 are periodic and control adjustments to the underlying control system. In one embodiment, the optimization module 930 runs once a minute and the model parameters are recalculated one a week.
The modeled system simulator 935 evaluates the model with a calculated set of parameters. The output of the modeled system simulator 935 is compared to the actual performance of the system and variants. The modeled system simulator 935 is able to run on the underlying system or on a separate system.
The alert module 940 alters operators and remodels, or re-optimizes, the underlying system when the results of the simulation diverge by a pre-determined amount from expected results. In some cases, the descriptive model is able to be changed, such as when new equipment is added to the underlying system or when low-level executable software is added to the underlying system.
In different embodiments, the optimization performed by the optimization system 910 occurs either in the control system itself or in an overlay control system that uses the original control system for measurement and parameter adjustment. In the case of an overlay control system, some or a majority of control decisions for HVAC equipment can remain in the controllers of that equipment, with the optimization system adjusting the parameters of the local control systems.
In one embodiment, a platform includes one or more processors and computer-readable media storing computer-executable instructions that when executed by the processor perform the steps 1000. It will be appreciated that the platform can be a single platform or a distributed one.
It will also be appreciated that the steps 1000 are merely illustrative of one embodiment of the invention. After reading this disclosure, those skilled in the art will recognize other modifications in accordance with the principles of the invention. For example, in other embodiments, some of the steps 1000 are deleted, other steps are added, and some steps are performed in different orders.
As described above, some embodiments of the invention include an abstraction layer that, among other things, (1) translates generic commands for controlling HVAC components into commands understandable by the systems that control the HVAC components and (2) ensures that any prerequisites are met before the components are adjusted based on the commands.
As one example, a high-level code fragment is written to loop through multiple packages, and for each component in a package actually connected to the system, if the measured air flow rate is above a CONSTANT value, particular commands are performed:
In this example, these particular commands (SET VALUES and ADJUST COMPONENT INPUT) are to be performed by the components. These generic, high-level commands are translated into specific commands recognizable by a controller and executable by the component. Moreover, these commands are only executed when certain prerequisites are satisfied.
As one example, when turning ON a fan during step testing, the abstract layer must first determine the prerequisite: an “occupancy” variable must be set to OCCUPIED, since during normal operations the fan controller will only turn ON the fan when a zone is occupied and the temperature variable is set to the desired setting. Rather than using a separate “FAN ON” command, which the controller does not recognize, the abstract layer indirectly turns the fan ON by setting the occupancy flag ON and setting the temperature variable to the desired setting. In other words, in this example, the abstraction layer translates the “source” command “TURN ON FAN” to the “target” commands:
SET OCCUPANCY ON
SET TEMPERATURE 20
Generally, the target commands are low-level commands specific to the HVAC component being controlled and are thus in a format different from that of the high-level source commands.
As another example, when a high-level command is to increase the air flow to a zone, such as an office, the abstraction layer inserts the prerequisite of testing whether the dampers are open before starting a fan, thereby ensuring that the increase in air pressure does not damage ducts. In this example, the abstraction layer translates the source command AIRFLOW=100 to the target commands:
READ DAMPER_STATUS
IF DAMPER_STATUS CLOSED THEN SET DAMPER_STATUS OPEN
SET AIR FLOW 100
Referring to
In one embodiment, a platform includes one or more processors and computer-readable media storing computer-executable instructions that when executed by the processor perform the steps 1100. It will be appreciated that the platform can be a single platform or a distributed one.
It will also be appreciated that the steps 1100 are merely illustrative of one embodiment of the invention. After reading this disclosure, those skilled in the art will recognize other modifications consistent with the principles of the invention. For example, in other embodiments, some of the steps 1100 are deleted, other steps are added, and some steps are performed in different orders.
Using the abstraction layer in accordance with the invention, programmers are able to write portable programs that drive and test HVAC components, without knowing the details (e.g., prerequisites) of these components.
Embodiments of the invention include many computationally intensive functions, such as determining performance characteristics of the HVAC components, performing auto-commissioning, translating source commands into the target commands for driving the HVAC components using an abstraction layer, adaptively managing HVAC components, and generating reports summarizing utility savings, to name only a few such functions. In some embodiments, these functions are performed on the HVAC components. In other embodiments, because these components do not have the processing capabilities to perform these functions at all or efficiently, these functions are performed on a remote platform. In these other embodiments, software agents are downloaded to the buildings to control the HVAC components. The agents drive HVAC components in pre-determined manners and transmit data to the remote platform for processing.
In operation, the software agents are automatically injected into the program of the lower-level controller for the HVAC components. An automated program searches for patterns indicating control points in the lower-level controller code and injects a fragment of new code that implements both a hook and a “heartbeat” into the code. The hook allows a supervisory system to override the values of the control points in the lower level controller. In HVAC systems, the hook can be visible through the BACNET protocol. Preferably, the hook has some method of storing the original overridden value. In one embodiment, the heartbeat is a monotonically increasing value that is also received from the supervisory system. If the heartbeat does not increase within a set period of time, the hook must override heartbeat with the original overridden value. In BACNET, the new value can be set to a higher priority than the existing value. If the heartbeat fails to trigger, the lower priority value will be restored and the state of the HVAC component is “rolled back” to its previous state.
As one example, a heating component on a packaging unit is tested during an auto-commissioning test or during adaptive management of an HVAC model. The heating component is set at 65° F. before the agent begins the auto-commissioning process. When the agent begins the auto-commissioning process, such as at night, it first saves the current temperature setting (the overridden value) and begins the heartbeat monitor. The agent then initializes the temperature to 90° F., increasing it to particular set points during the auto-commissioning process, and transmits measured data to a remote platform. If, during auto-commissioning, the agent determines that communication between it and the remote platform has terminated, the agent resets (rolls back) the temperature of the heating component to its overridden value (65° F.), stops the auto-commissioning process, and returns control of the thermal component to its normal operating code. In this way, the terminated communication between the agent and the remote platform does not leave the building in an unexpected state.
In accordance with embodiments of the invention, reports that allow users to determine utility savings by adjusting environmental condition constraints can be generated. From a report, for example, a user may see that lowering the temperature in a particular zone by 1° F. for one hour during lunchtime, when the zone is lightly unoccupied, will result in a energy savings of about $150 each month, and lowering the temperature by 2° F., will result in savings of about $200 each month. The user can then balance cost versus comfort to determine an energy plan.
In operation, a centralized platform incorporates Building Management Systems (BMSs), weather station, utility price data, both historical and real time, to create predictive models of the utility costs attributable to individual components of the HVAC system. This allows for numerical optimization of the whole building utility cost using environmental zone conditions as constraints. The optimization process identifies the financial cost to meet the load in each zone and the effect of relaxing the environmental condition constraints. This granular information can be presented to the user to make informed decisions when changing zone set points. The system automatically writes the most optimal settings to the BMS periodically, such as every 5 minutes, though other time periods can also be used.
The financial-cost calculator 1220 calculates the financial cost of a quantity of energy consumed at a given time based on the utility rate tariff schedule of the facility. The system optimizer 1230 optimizes the operation of the system for minimized utility costs within environmental condition constraints set by the user. The output of the model is periodic control adjustments to the underlying control system. The GUI 1240 displays to the user quantitative data predicting the effect of a range of changes to the environmental condition constraints.
It will be appreciated that the report 1300 is merely illustrative. In accordance with the principles of the invention, many different reports can be generated, including ones containing different information in different formats, as selected by a user.
In operation, a system models environmental characteristics of zones in buildings, such as by using Equations 1-11 above or similar equations. In one embodiment, the modeling is performed using a single power meter. During the modeling process, HVAC components are exercised using an abstraction language that hides the component-specific workings as well a command prerequisites from the programmers, allowing the testing software to be both compact and portable. The process includes inserting software agents into the normal operating software for the components. Advantageously, the agents monitor the connection between the components and modeling platform. If the connection is broken, the inputs to the components are rolled back, to their pre-testing configurations. Using these models, the HVAC components can be adaptively managed and reports about component efficiency can be generated and environment settings set to reduce costs.
While the description above gives examples of HVAC components that can be modeled in accordance with the principles of the invention, it will be appreciated that any type of electro-mechanical component is able to be modeled, including, but not limited to, other HVAC components such as sensors (e.g., temperature, flow, pressure, humidity, etc), actuators, variable speed drives for motor speed control (also called variable frequency drives), fans, dampers, air-side economizers, pumps, valves, reheat valves, pre-heat valves, heating valves, chilled water valves, automatic isolation valves, automatic shut-off valves, chillers, air-cooled chillers, water-cooled chillers, cooling towers, fluid coolers, dry coolers, water-side economizers, hot-water boilers, steam boilers, furnaces, humidifiers, desiccant dehumidifiers, evaporative coolers, direct evaporative coolers, indirect evaporative coolers, heating coils, cooling coils, pre-heat coils, air-to-water heat exchangers, water-to-water heat exchangers, radiant heating equipment, radiant cooling equipment, underfloor air-distribution equipment, baseboard heaters (convectors) baseboard radiators, unitary air-conditioning equipment (packaged units), heat pumps, air-source heat pumps, water-source heat pumps, ground-source heat pumps, water-cooled AC units, self-contained water-cooled DX units, variable air volume (VAV) cooling-only terminal units, variable air volume (VAV) reheat terminal units (with either electric heat or hot-water heating coil), dual-duct variable air volume (DDVAV) terminal units, fan-powered VAV terminal units, series fan-powered VAV terminal units (with and without heating coil) for electric or hot-water heating, and parallel fan-powered VAV terminal units (with and without heating coil) for electric or hot-water heating.
While many of the examples above describe HVAC systems, it will be appreciated that the principles of the invention are able to be used in other systems. For example, many other building components can be auto commissioned in accordance with principles of the invention, including, not only HVAC components and their control systems, but also plumbing components, electrical systems, first and life safety systems, building envelopes, co-generation units, utility plants, sustainable systems, lighting components, wastewater units, control units, and building security units, to name only a few examples.
It will be readily apparent to one skilled in the art that various other modifications may be made to the embodiments without departing from the spirit and scope of the invention as defined by the appended claims.
This application claims priority under 35 U.S.C. §119(e) of the co-pending U.S. provisional patent application Ser. No. 61/919,547, filed Dec. 20, 2013, and titled “System, Method and Platform for Characterizing In-Situ Building and System Component and Sub-component Performance by Using Generic Performance Data, Utility-Meter Data, and Automatic Step Testing,” and the co-pending U.S. provisional patent application Ser. No. 62/022,126, filed Jul. 8, 2014, and titled “System, Method and Platform for Automated Commissioning in Commercial Buildings,” both of which are hereby incorporated by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| 61919547 | Dec 2013 | US | |
| 62022126 | Jul 2014 | US |
