Patent Application
20190293318
This application claims the benefit of Non-Provisional Chinese Application No. 201810249481.8 filed Mar. 23, 2018, which is incorporated herein by reference in its entirety.
The subject matter disclosed herein generally relates to heating, ventilation, and air conditioning (HVAC) systems, and more specifically to an apparatus and a method for monitoring a control system of an HVAC system.
Conventional HVAC systems are often designed with enough capacity allowance to maintain comfort in an enclosed area when operating at peak heating or cooling load conditions. However current systems are unable to predict when capacity may decrease to a point where the HVAC system is unable to maintain comfort in the enclosed areas.
According to one embodiment, a method of operating a heating, ventilation, and air conditioning (HVAC) analytics system is provided. The method including: obtaining HVAC data for an HVAC unit in electronic communication with the HVAC analytics system; obtaining an HVAC unit characteristic of the HVAC unit; determining an HVAC comfort performance index (CPI) in response to the HVAC data and the HVAC unit characteristic; determining an HVAC CPI degradation trend line in response to the HVAC comfort performance index; obtaining weather data for a geographical area where the HVAC system is located, the weather data including a predicted outside air temperature (OAT); and determining a point in time where the predicted OAT is equivalent to the HVAC CPI degradation trend line.
In addition to one or more of the features described above, or as an alternative, further embodiments may include: generating an HVAC performance report in response to the predicted OAT and the HVAC CPI degradation trend; and transmitting the HVAC performance report to a user device.
In addition to one or more of the features described above, or as an alternative, further embodiments may include: activating an alarm a selected time period in advance of the point in time.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the HVAC CPI is a comfort OAT limit.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the comfort OAT limit is calculated by determining an OAT value when an HVAC map of the HVAC system is equivalent to a trend line for a required capacity of the HVAC system as a function of the indoor air temperature (IAT) and the OAT or as a function of a difference between the IAT and the OAT.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the comfort OAT limit is calculated by determining an OAT value when an indoor air temperature rate (IATR) of zero is equivalent to a trend line representing an average IATR of the HVAC system as a function of the IAT and the OAT or as a function of a difference between the IAT and the OAT.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the HVAC data includes at least one of an IAT produced by the HVAC unit, an OAT of a geographical area where the HVAC unit is located, and a runtime of the HVAC unit.
According to another embodiment, a heating, ventilation, and air conditioning (HVAC) analytics system is provided. The HVAC system including: an HVAC unit configured to deliver conditioned air to a targeted area, and an HVAC analytics engine in electronic communication with the HVAC unit. The HVAC analytics engine including: a processor; a memory including computer-executable instructions that, when executed by the processor, cause the processor to perform operations, the operations including: obtaining HVAC data for the HVAC unit; obtaining an HVAC unit characteristic of the HVAC unit; determining an HVAC comfort performance index (CPI) in response to the HVAC data and the HVAC unit characteristic; determining an HVAC CPI degradation trend line in response to the HVAC comfort performance index; obtaining weather data for a geographical area where the HVAC unit is located, the weather data including a predicted outside air temperature (OAT); and determining a point in time where the predicted OAT is equivalent to the HVAC CPI degradation trend line.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the processor is further configured to perform the operations: generating an HVAC performance report in response to the predicted OAT and the HVAC CPI degradation trend; and transmitting the HVAC performance report to a user device.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the processor is further configured to perform the operations: activating an alarm a selected time period in advance of the point in time.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the HVAC CPI is a comfort OAT limit.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the comfort OAT limit is calculated by determining an OAT value when an HVAC map of the HVAC system is equivalent to a trend line for a required capacity of the HVAC system as a function of the indoor air temperature (IAT) and the OAT or as a function of a difference between the IAT and the OAT.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the comfort OAT limit is calculated by determining an OAT value when an indoor air temperature rate (IATR) of zero is equivalent to a trend line representing an average IATR of the HVAC system as a function of the IAT and the OAT or as a function of a difference between the IAT and the OAT.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the HVAC data includes at least one of an IAT produced by the HVAC unit, an OAT of a geographical area where the HVAC unit is located, and a runtime of the HVAC unit.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the HVAC analytics engine is separate and apart from the HVAC unit, and wherein the HVAC analytics engine is in electronic communication through a wireless communication network.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the HVAC analytics engine is embedded within at least one of the HVAC unit and a controller in communication with the HVAC unit.
According to another embodiment, a computer program product tangibly embodied on a computer readable medium, the computer program product including instructions that, when executed by a processor, cause the processor to perform operations comprising: obtaining heating, ventilation, and air conditioning (HVAC) data for an HVAC unit in electronic communication with an HVAC analytics system; obtaining an HVAC unit characteristic of the HVAC unit; determining an HVAC comfort performance index (CPI) in response to the HVAC data and the HVAC unit characteristic; determining an HVAC CPI degradation trend line in response to the HVAC comfort performance index; obtaining weather data for a geographical area where the HVAC system is located, the weather data including a predicted outside air temperature (OAT); and determining a point in time where the predicted OAT is equivalent to the HVAC CPI degradation trend line.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the operations further include: generating an HVAC performance report in response to the predicted OAT and the HVAC CPI degradation trend; and transmitting the HVAC performance report to a user device.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the operations further include: activating an alarm a selected time period in advance of the point in time.
In addition to one or more of the features described above, or as an alternative, further embodiments may include that the HVAC CPI is a comfort OAT limit.
Technical effects of embodiments of the present disclosure include utilizing predicting capacity loss of HVAC unit in response to upcoming weather forecasts.
The foregoing features and elements may be combined in various combinations without exclusivity, unless expressly indicated otherwise. These features and elements as well as the operation thereof will become more apparent in light of the following description and the accompanying drawings. It should be understood, however, that the following description and drawings are intended to be illustrative and explanatory in nature and non-limiting.
The subject matter which is regarded as the disclosure is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of the disclosure are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:
The following descriptions should not be considered limiting in any way. With reference to the accompanying drawings, like elements are numbered alike:
A detailed description of one or more embodiments of the disclosed apparatus and method are presented herein by way of exemplification and not limitation with reference to the Figures.
Conventional HVAC control systems typically monitor only the temperature of one or more rooms in a building or house to operate an HVAC unit according to a target temperature set point value set by the user. However, various unknown system faults can cause degradation of the actual HVAC performance.
Early fault detection of an HVAC system in advance of when homeowners begin to notice a comfort issue can provide value to homeowners and dealer service persons. Generally, homeowners may not be aware of performance issues with their HVAC system during mild weather seasons. HVAC systems may already be performing poorly without the homeowner's knowledge due to a variety of HVAC issues including but not limited to a refrigerant leak, improperly sized equipment, house envelope leakage, etc. The comfort issues may arise once the HVAC issues get worse and/or peak load conditions exist (hot summer and/or cold winter). Once peak load conditions exist, homeowners may have difficulty having their HVAC unit serviced due to an increased number of HVAC dealer/contractor service calls.
Various non-limiting embodiments of the disclosure provide an HVAC analytics engine configured to automatically analyze historical HVAC operational data and detect HVAC faults in advance of any comfort issue and then report the HVAC faults to a servicing dealer. The HVAC analytics engine analyzes historical HVAC operational data and interacts with a dealer (and/or homeowner), to support the dealer's recommendation for service and provide more effective and productive servicing of the equipment. The fault detection system can provide real time information of HVAC system performance and generate alerts when the performance degradation occurs. All of the above could help dealers provide quick response to the homeowner, even before the homeowner makes a service call.
With reference now to
The HVAC unit 202 is in electronic communication with a computing system 100. The computing system 100 can be installed in the HVAC system 201 or wirelessly connected to the HVAC system through the computing network 206 while being installed on a separate server 212 or a user device 210. The computing system 100 includes a memory 102 and an electronic hardware processor or controller 106. The memory 102 stores various instructions algorithms which are executable by the controller 106. The memory 102 can also store set operating schedules, HVAC unit characteristics 354, and historical HVAC data 352 obtained from HVAC unit 202 (see
The HVAC unit 202 is in electronic communication with the controller 106 such as, for example, a digital thermostat. Although one controller 106 is illustrated, it should be appreciated that multiple controllers can be located remotely from one another. Each controller 106 can control the HVAC unit 202. The controller 106 can perform various functions including, but not limited to, switching on and off the HVAC unit 202, selecting a mode (e.g., heating mode, cooling mode, etc.) of the HVAC unit 202, setting a desired room temperature at which to operate the HVAC unit 202, and setting operating schedules at which to operate the HVAC unit 202. The controller 106 is also in electronic communication with one or more sensors (not shown) configured to detect and monitor various environmental conditions such as, for example, room temperatures and humidity. In this manner, the controller 106 can actively control the HVAC unit 202 to achieve and/or maintain a room temperature set point value and/or set according to an operating schedule. The controller 106 is also configured to monitor operation of the HVAC unit 202. In this manner, the controller 106 can generate operation HVAC data 352 (see
The controller 106 may electrically communicate with the memory 102 via one or more input/output (I/O) devices 108. In some embodiments, the I/O device(s) 108 may include one or more of a keyboard or keypad, a touchscreen or touch panel, a display screen, a microphone, a speaker, a mouse, a button, a remote control, a joystick, a printer, a telephone or mobile device (e.g., a smartphone), sensors such as temperature, pressure and occupancy, etc. The I/O device(s) 108 may be configured to provide an interface such as a thermostat interface, for example, to allow a user to interact with the computing system 100.
The computing system 100 further includes a network interface 110 capable of communication with a network 206. The network 206 can be implemented as a local on-site data network, a computer network, a telephone network, a cloud computing network, etc. The network interface 110 includes any communication device (e.g., a modem, wireless network adapter, etc.) that operates according to a network protocol (e.g., Wi-Fi, Ethernet, satellite, cable communications, etc.) which establishes a wired and/or wireless communication with the network 206. The network 206 may be in electronic communication with one or more electronic user devices 210 and various servers 212 to transmit and receive data. For example, weather data 370 (see
The user devices 210 include, but are not limited to, a desktop computer, a laptop computer, and a mobile device (e.g., a cell phone, smartphone, smart wearable device, etc.). The user device 210 also includes a display unit, which can display HVAC performance reports 320 (see
Turning now to
In at least one embodiment, the HVAC system 201 sends HVAC Data 352 and HVAC unit characteristics 354 to the HVAC comfort failure forecasting engine 306. The HVAC unit characteristics 354 include the type of HVAC unit 202, the performance rating data of the HVAC unit 202 (e.g., the performance rating maximum rated output performance per units of energy consumed), target area (i.e. room(s)) to be heated/cooled, the number of total HVAC units 202 per targeted area, cooling capacity, heating capacity, and a geographical location of the HVAC system 201. The HVAC unit characteristics 354 may also include updated HVAC equipment information, which can indicate whether a new HVAC unit 202 has been installed in the HVAC system 201.
The HVAC comfort failure forecasting engine 306 includes an HVAC data processing module 310, an HVAC data analytics module 312, an HVAC performance index learning module 314, and a discomfort time predicted module 316. Any one of the HVAC data processing module 310, the HVAC data analytics module 312, the HVAC performance index learning module 314, and the discomfort time predicted module 316 can be constructed as an electronic hardware controller that includes memory and a processor configured to execute algorithms and/or computer-readable program instructions stored in the memory.
The HVAC data processing module 310 is configured to pre-process the raw HVAC data 352 from the controller 106 with the purpose to extract the essence (i.e. useful information) from data and remove the dross (i.e. data noise and useless information data). The raw HVAC data 352 may include HVAC information such as, for example, outdoor air temperature (OAT), indoor air temperature (TAT), HVAC set point, user inputs, HVAC unit running time, set temperature per hour, and actual room temperature per hour. The HVAC data processing module 310 is configured to process the HVAC data 352 in order to average out data noise to create average data such as temperature difference between IAT and OAT, average required capacity, to name a couple of non-limiting examples. The HVAC comfort failure forecasting engine 306 may perform a loop from the HVAC data processing module 310 to the HVAC data analytics module 312, the HVAC performance index learning module 314, and the discomfort time predicted module 316, as seen in
The HVAC data analytics module 312 is configured to determine (i.e. learn) system behavior in response to the HVAC data 352 from the HVAC data processing module 310 and the HVAC unit characteristics 354. The HVAC data analytics module 312 calculates an HVAC comfort performance index (CPI), such as, for example, a comfort OAT limit 418 (i.e., maximum OAT in cooling mode and minimum OAT in heating mode) beyond which the HVAC system 201 will have comfort issues for an individual within the targeted area.
As shown in
The second method 420 plots the HVAC data 352 for an indoor air temperature rate (IATR) 422 of the HVAC system 201 versus IAT-OAT 414 of the HVAC system 201 and then determines a trend line 426 for IATR 422 versus IAT-OAT 414 of the HVAC system 201. The IATR 422 may be a daily average, a bi-daily average, or any other segmented average amount. The trend line 426 represents the average IATR 422 as a function of indoor-outdoor temperature difference (i.e. IAT-OAT 414). The IAT and OAT may be used in place of IAT-OAT 414, thus trend line 426 may represent the IATR 422 as a function of IAT and OAT. The comfort OAT limit 418 is calculated by assuming the IATR is zero (i.e. available HVAC capacity is equal to thermal load on the targeted area and HVAC system 201 will be unable to pull down/up the IAT) and then the comfort OAT limit 418 is the OAT value at an intersection point 427 of the trend line 426 and an IATR of zero. In an embodiment, the comfort OAT limit 418 of the HVAC system 201 is computed in response to the IATR.
The comfort OAT limit 418 may be calculated by the HVAC data analytics module 312 for a selected time period 434 at a selected time increment, such as, for example, every week, every month, every year, . . . etc. The comfort OAT limit 418 for the selected time period 434 is then transmitted to the HVAC performance index learning module 314. The HVAC performance index learning module 314 is configured to calculate an HVAC CPI degradation trend line 438 in response to the comfort OAT limit 418 for the selected time period 434 by correlating the monthly comfort OAT limit 418 with time. The HVAC CPI degradation trend line 438 projects the comfort OAT limit 418 into the future.
The HVAC CPI degradation trend line 438 is transmitted to the discomfort time prediction module 316. The discomfort time prediction module 316 is also configured to receive weather data 370 from an external server 212. The weather data 370 may include past OAT data 372 and predicted OAT data 374 for the geographical area where the HVAC system 201 is located. The predicted OAT data 374 may be based upon predicted weather forecasts and/or past OAT data 372 recorded. In another embodiment, the discomfort time prediction module 316 may determine the predicted OAT data 374 in response to the past OAT data.
The discomfort time prediction module 316 is configured to plot the HVAC CPI degradation trend line 438 against the predicted OAT 374. The intersection point 437 of the HVAC CPI degradation trend line 438 and the predicted OAT 374 is a point in time at which the HVAC system 201 is no longer able to maintain comfort within the targeted area due to of lack-of-capacity due to gradual performance degradation such as refrigerant leakage, etc.
The discomfort time predicted module 316 is also configured to generate one or more HVAC performance reports 320 depicting the intersection point 437. The discomfort time predicted module 316 also generates and transmits the HVAC performance reports 320 to the user device 210. A display unit of the user device 210 may display HVAC performance reports 320. The user device 210 also includes a display unit which can display HVAC performance reports 320. The user device may belong to a dealer/repairman of the HVAC system 201 and/or an owner of the HVAC system 201. Advantageously, the HVAC performance reports 320 may help an HVAC repairman catch and fixed issues causing reduced capacity in the HVAC system 201 prior to the owner of the HVAC system 201 feeling any discomfort associated with the issue. An alert may be generated to draw attention to the predicting capacity loss of the HVAC system 201. The alert may be activated a selected time period in advance of the intersection point 437. For example, a repairman may be alerted in April that an HVAC system 201 is showing an HVAC CPI degradation trend is showing that capacity of the HVAC system 201 will be reduced to a point that the HVAC system 201 will not be able to handle the predicted elevated temperatures in August.
Referring now also to
At block 502, HVAC data 352 of the HVAC system 201 is obtained. The HVAC data 352 can be obtained from the HVAC controller 106, and can be communicated to the HVAC comfort failure forecasting engine 306 in real-time, and/or can be delivered in response to a data request sent by the HVAC comfort failure forecasting engine 306.
At block 504, HVAC unit characteristics 354 of the HVAC system 201 is obtained. The HVAC unit characteristics 354 can be obtained from the HVAC controller 106, and can be communicated to the HVAC comfort failure forecasting engine 306 in real-time, and/or can be delivered in response to a data request sent by the HVAC comfort failure forecasting engine 306. In another embodiment, the HVAC unit characteristics 354 can be obtained from a separate server 212 (e.g. the server 212 is configured to store the HVAC unit characteristics 354 for each HVAC system 201), and can be communicated to the HVAC comfort failure forecasting engine 306 in real-time, and/or can be delivered in response to a data request sent by the HVAC comfort failure forecasting engine 306.
At block 506, an HVAC CPI is determined for a selected time period at a selected time increment. The HVAC CPI may be a comfort OAT limit 418 (i.e., maximum OAT in cooling mode and minimum OAT in heating mode) beyond which the HVAC system 201 will have comfort issues for an individual within the targeted area. As discussed above, two methods may be used to determine comfort OAT limits 418 including a first method 410 and a second method 420.
At block 508, the HVAC performance index learning module 314 is configured to calculate an HVAC CPI degradation trend line 438 in response to the comfort OAT limit 418 for the selected time period 434 by correlating the monthly comfort OAT limit 418 with time. The HVAC CPI degradation trend line 438 projects the comfort OAT limit 418 into the future. The HVAC CPI degradation trend line 438 is transmitted to the discomfort time prediction module 316.
At block 510, the discomfort time prediction module 316 obtains weather data 370 from an external server 212. The weather data 370 includes a past OAT data 372 and a predicted OAT 374 for the geographical area where the HVAC system 201 is located. The predicted OAT 374 may be based upon predicted weather forecasts and/or past OAT data 372 recorded.
At block 512, a discomfort time prediction module 316 determines a point in time (i.e. intersection 437) where the predicted OAT 374 is equivalent to the HVAC CPI degradation trend line 438. This point in time (i.e. intersection point 437 of the HVAC CPI degradation trend line 438 and the predicted OAT 374) is a time at which the HVAC system 201 is no longer able to maintain comfort within the targeted area due to of lack-of-capacity due to gradual performance degradation such as refrigerant leakage, etc.
At block 514, one or more HVAC performance reports 320 are generated in response to the HVAC CPI degradation trend line 438. The HVAC performance reports 320 include various analytical data predicting performance of the HVAC system 201 over a period of time. At block 516, the HVAC performance reports 320 are transmitted to a user device 210 in electronic communication with the computing network 206. The reports can be displayed via the user device 210 such that a user (e.g. dealer, maintainer, or homeowner) is able to monitor the operating performance of the HVAC system 201.
While the above description has described the flow process of
As used herein, the term “module” or “unit” can refer to an application specific integrated circuit (ASIC), an electronic circuit, a microprocessor, a computer processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, a microcontroller including various inputs and outputs, and/or other suitable components that provide the described functionality. The module is configured to execute various algorithms, transforms, and/or logical processes to generate one or more signals of controlling a component or system. When implemented in software, a module can be embodied in memory as a non-transitory machine-readable storage medium readable by a processing circuit (e.g., a microprocessor) and storing instructions for execution by the processing circuit for performing a method. A controller refers to an electronic hardware controller including a storage unit capable of storing algorithms, logic or computer executable instruction, and that contains the circuitry necessary to interpret and execute instructions.
As described above, embodiments can be in the form of processor-implemented processes and devices for practicing those processes, such as a processor. Embodiments can also be in the form of computer program code containing instructions embodied in tangible media, such as network cloud storage, SD cards, flash drives, floppy diskettes, CD ROMs, hard drives, or any other computer-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes a device for practicing the embodiments. Embodiments can also be in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into an executed by a computer, the computer becomes a device for practicing the embodiments. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.
The term “about” is intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” can include a range of ±8% or 5%, or 2% of a given value.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof.
While the present disclosure has been described with reference to an exemplary embodiment or embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from the essential scope thereof. Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this present disclosure, but that the present disclosure will include all embodiments falling within the scope of the claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 201810249481.8 | Mar 2018 | CN | national |
