Patent Application
20230288086
The invention relates generally to control of heat pumps and, more specifically, to defrost control in heat pumps.
Under certain operating conditions, heat pump systems may slowly accumulate frost on outdoor coils. The frost may insulate the heat exchanger and may inhibit its performance as the frost layer grows. A defrost cycle is generally needed to remove the frost and restore system performance. However, heating may be interrupted during the defrost cycle, which may cause the seasonal performance (HSPF) to decrease.
Aspects of the disclosure relate to methods, apparatuses, and/or systems for defrost control in heat pumps.
In some embodiments, a system for defrost control in a heat pump comprises at least one processor; and memory storing instructions executable by the at least one processor, the instructions when executed cause the system to: determine a heat load forecast; determine a frost growth forecast based on the determined heat load forecast; determine one or more control parameters of the heat pump based on the determined frost growth forecast.
In some embodiments, the control parameters of the heat pump include defrost control parameters.
In some embodiments, the defrost control parameters comprise adjusting one or more of a schedule, duration, frequency, and heat source of a defrost cycle of the heat pump.
In some embodiments, the system may be configured to detect a frost growth condition in the determined frost growth forecast, and wherein the control parameters are determined based on the detected condition.
In some embodiments, the system may be configured to determine a duration of the detected frost condition, wherein the control parameters are based on the determined duration of the frost condition.
In some embodiments, the control parameters comprise pre-heating instructions based on the detected frost condition.
In some embodiments, the system may be configured to obtain a weather forecast, and wherein the heat load forecast is determined based on the weather forecast.
In some embodiments, the system may be configured to obtain a occupation forecast, and wherein the heat load forecast is determined based on the occupation forecast.
In some embodiments, the system may be configured to generate a recommendation for the determined control parameters for the heat pump; and responsive to a user accepting the recommendation, applying the determined control parameters.
In some embodiments, the heat pump is configured to operate in a building, the building having one or more subsystems, and wherein the instructions further cause the system to determine one or more control parameters of the building subsystems based on determined frost growth forecast.
In some embodiments, a method being implemented in a system comprising at least one processor, and memory storing instructions. The method comprises: determining a heat load forecast; determining a frost growth forecast based on the determined heat load forecast; and determining one or more control parameters of the heat pump based on the determined frost growth forecast.
In some embodiments, a non-transitory computer-readable storage medium storing program instructions computer-executable to implement: determining a heat load forecast; determining a frost growth forecast based on the determined heat load forecast; and determining one or more control parameters of the heat pump based on the determined frost growth forecast.
Various other aspects, features, and advantages of the invention will be apparent through the detailed description of the invention and the drawings attached hereto. It is also to be understood that both the foregoing general description and the following detailed description are examples and not restrictive of the scope of the invention.
In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be appreciated, however, by those having skill in the art that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other cases, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the invention.
The present disclosure, in accordance with some embodiments, describes a system 100 for defrost control in heat pumps. In some embodiments, system 100 may be configured to determine one or more control parameters of one or more components of the heat pump (e.g., compressor, fans, etc.). In some embodiments, system 100 may include predictive models configured for determining the heat pump control parameters. In some embodiments, the predictive models may be configured to determine frost growth forecast (e.g., based on heat load information, weather information, occupation information, thermostat settings, system configuration information, heat pump components condition, sensor information, and/or other information) to determine an optimal defrost schedule, defrost duration, defrost frequency, system setpoints, use of auxiliary heating sources, or other control parameters. In some embodiments, system 100 may allow for controlling the heat pump operations to function in different operating modes to avoid frost formation, minimize duration, minimize frequency of defrost cycles, and/or other operating modes that may help optimize the defrost operations of the heat pump and save energy (for example energy generally used to provide other heating alternatives during the defrost cycle of the heat pump). For example, the operating modes may include a predictive mode, where the system uses the forecasts and the predictive models to determine optimum control methods for defrosting the heat pump (e.g., by controlling operations of the heat pump and operations of other subsystems of the building). This may help minimize the duration and frequency of defrost cycles without impacting the thermal comfort in the building. The operating modes, in some embodiments, may include a preventive mode, where the control parameters are configured to avoid, delay, and/or minimize frost formation in the heat pump. In these cases, the system may use the forecasts and the predictive models to determine control methods that may help prevent or delay frost formation (passive defrosting).
In some embodiments, heat pump 104 may be configured to heat and/or cool a climate-controlled space (e.g., a building, home, or other spaces). In some embodiments, heat pump 104 may be used in commercial or residential settings. In some embodiments, the heat pump 104 be configured to use a refrigerant to carry thermal energy between a relatively hotter side of a circulation loop to a relatively cooler side of the circulation loop. Compression of the refrigerant occurs at the hotter side of the loop, where one or more compressors raises the temperature of the refrigerant. Evaporation of the refrigerant occurs at the cooler side of the loop, where the refrigerant is allowed to expand, thus resulting in a temperature drop. Thermal energy is added to the refrigerant on one side of the loop and extracted from the refrigerant on the other side, due to the temperature differences between the refrigerant and the indoor and outdoor mediums, respectively, to make use of the outdoor mediums as either a thermal energy source or a thermal energy sink. In some embodiments, heat pump 104 may include one or more valves for selectively directing the refrigerant through indoor and outdoor heat exchangers so that the indoor heat exchanger is on the hot side of the refrigerant circulation loop for heating and on the cool side for cooling. In some embodiments, the heat pump may include one or more fans for circulating air over the heat exchangers. In some embodiments, frost may form on a coil of the heat pump as a result of certain operation condition (e.g., cold, humidity, etc.). Heat pump 104 may be configured to operate a defrost cycle to remove the frost. In some embodiments, the heat pump may be controlled such that the defrost cycle of the heat pump may be avoided or adjusted based on frost growth forecast, weather forecast, heat load forecast, occupancy forecast, sensor information, and/or other information.
In some embodiments, one or more sensors 102 be configured to generate output signals related to the operations of the heat pump. In some embodiments, one or more sensors 102 may be located proximate to one or more of the heat pump components (within or outside the building). For example, in some embodiments, the heat pump sensors may include one or more of pressure sensors, outdoor temperature sensors, refrigerant temperature sensors, flow sensors, humidity sensors, position sensors, and/or other sensors related to the operations of the heat pump. For example, in some embodiments, the heat pump sensors may be configured to detect frost creation (e.g., on evaporator, coil, or other components of heat pump 104) by sensing the refrigerant temperature, the ambient air temperature, a change in the differential pressure drop across the evaporator coil, etc.
In some embodiments, one or more sensors 102 may be configured to generate output signals related to operations of other subsystems of the building (e.g., thermostats, lighting, refrigeration, water treatment, air treatment, fire systems, elevators, security, access control, appliances, server/computer systems, charging stations, and/or other subsystems). For example, the sensors may include one or more of temperature sensors, pressure sensors, humidity sensors, air flow sensors, light sensors, sound sensors, air quality sensors, water quality sensors, gas particles detectors (e.g., CO2, O2, CO, etc.), smoke detectors, energy consumption sensors (or meters), energy generation sensors (or meters), fluid level sensors, fluid flow sensors, optical sensors, motion sensors, occupancy sensors, image sensors, accelerometers, gyroscopes, or other sensors for measuring parameters related to the functioning or performance the subsystems.
Defrost control system 110 may include a heat load module 120, a weather module 130, a forecasting module 150, control module 180, and/or other components. In some embodiments, defrost control system 110 may include computing resources such as processors and memory devices for storing instructions (e.g., computing system 400 described herein below with reference to
Heat load module 120 may be configured to obtain information related to the heat pump heat load. In some embodiments, the heat load information may be pre-determined based on the building (e.g., at the time of installation). In some embodiments, the heat load may be dynamically calculated (or adjusted) based on heat demand of the building. For example, an initial heat load may be determined, at the time of installation, based on the building (e.g., type, size, insulation, etc.). The initial heat load may be dynamically adjusted based on weather, occupancy, heat demand, energy information (source, cost, demand, consumption, etc.), and/or other information related to the building. In some embodiments, the building information may be obtained from a building information module (not shown here), may be received from one or more sensors 102, or from other components of system 100. In some embodiments, the building information may be received from other subsystems of the building (e.g., thermostats, lighting, refrigeration, water treatment, air treatment, fire systems, elevators, security, access control, appliances, server/computer systems, charging stations, and/or other subsystems). In some embodiments, in smart buildings for example, one or more of the subsystems may be managed by one control system, in these cases, the heat load module 120 may obtain the building information from the building control system. In some embodiments, one or more operations of the present disclosure may be executed by the building control system. It's to be noted that these examples of building systems are for illustration purposes only, other information subsystems (or devices) that may affect heat load (or heat demand) may be considered and are compatible with the present disclosure.
In some embodiments, the heat load information determined by heat load module may include heat load forecast. In some embodiments, the heat load forecast may be determined based on current and/or historic heat load information. In some embodiments, the heat load forecast may be determined by one or more predictive models. In some embodiments, the heat load forecast may be determined based on other forecast information (e.g., weather, occupancy, heat demand, energy, and/or other forecasts related to the building). In some embodiments, the heat load forecast, and/or the building forecasts may be determined by forecast module 150 (described herein below).
Weather module 130 may be configured to obtain weather information. In some embodiments, the weather information may include current, historic, or future (e.g., including weather forecasts, and historic weather forecasts) weather information. In some embodiments, the weather information may be obtained from one or more sensors (e.g., sensors 102), weather forecast APIs, user input via a user interface, or from other components within or outside of system 100.
Forecasting module 150 may be configured to determine a frost growth forecast. In some embodiments, the frost growth forecast may be determined based on one or more of the heat load information, weather information, occupation information, thermostat settings, system configuration information, heat pump components condition, sensor information, or other information. In some embodiments, forecasting module 150 may be configured to determine frost growth forecast over time. For example, forecasting module 150 may be configured to generate the forecasts for a given time (e.g., specific time of day) or a given period of time or moving time horizon (e.g., number of hours, days, weeks, months, or other time periods). In some embodiments, the forecasting module 150 may be configured to determine a frost growth rate forecast (e.g., the growth of frost in the heat pump as function of time) based on the determined forecasts over time.
In some embodiments, forecasting module 150 may include a machine learning system for training one or more machine learning models to determine/predict the frost growth forecast. In some embodiments, the machine learning system may include predictive models configured to determine the frost growth forecast. In some embodiments, the predictive models may use one or more data samples (current or historical) to determine the frost growth forecast (e.g., data samples obtained from heat load module 120, weather module 130, or from other components within or outside system 100). In some embodiments, the machine learning system may be configured to use one or more of supervised learning, semi-supervised, unsupervised learning, reinforcement learning, and/or other machine learning techniques. In some embodiments, the machine learning models may include decision trees, support vector machines, nearest neighbors models, autoregression analysis, Bayesian networks, random forest learning, dimensionality reduction algorithms, boosting algorithms, artificial neural networks (e.g., fully connected neural networks, deep convolutional neural networks, or recurrent neural networks), deep learning, and/or other machine learning models. These are representative machine learning models and it is to be understood that models not specifically called out here are within the spirit of the current invention.
As explained above, forecast module 150 may be configured to generate other forecasts. For example, forecasting module 150 may be configured to predict/forecast the weather information (e.g., based on historic and/or current weather information). The forecast module 150 may be configured to predict/forecast the building occupancy (e.g., based on scheduling information, sensor information, and/or historic information). In some embodiments, forecast module 150 may determine the heat load forecast based on one or more of the weather forecast and/or the occupation information. It's to be noted that the forecast module 150 may use other information to determine the heat load forecast, the frost growth forecast, or other forecasts. For example, forecast module 150 may be configured to obtain (or determine or forecast) information related to the energy load of the building. In some embodiments, the energy information may include energy generation sources, energy consumption, energy demand, energy distribution, energy cost, or other energy related information. In some embodiments, forecast module 150 may be configured to determine the energy forecast based on one or more of the weather information, the heat load information, the frost.
Control module 180 may be configured to determine one or more control parameters of the heat pump. In some embodiments, the determined control parameters may include defrost control parameters. For example, control module 180 may determine a schedule, duration, and frequency of the defrost cycle. In some embodiments, control module may be configured to determine a heat source for the defrost cycle (e.g., compressor heat, condenser heat, heat from thermal energy storage device, and/or from other integrated systems). In some embodiments, the heat pump control parameters may be configured to minimize the duration and frequency of defrost cycles. For example, control module 180 may be configured to identify times when passive defrosting may be used without impacting thermal comfort in the building. For example, control module 180 may be configured to identify times when the use of the compressor may not be needed to defrost the coil of the heat pump. This may help optimize the defrost operations of the heat pump and save energy (for example energy generally used to provide other heating alternatives during the defrost cycle of the heat pump).
It's to be noted that the example described in
In some embodiments, one or more components of system 100 may communicate directly through one or more dedicated communication links. In some embodiments system 100 may include a network 190 connecting one or more components of system 100. In some embodiments, network 190 may be any type of network configured to provide communications between components of system 100. For example, network may be any type of wired or wireless network (including infrastructure) that provides communications, exchanges information, and/or facilitates the exchange of information, such as the Internet, near field communication (NFC), optical code scanner, cellular network, a public switched telephone network (“PSTN”), text messaging systems (e.g., SMS, MMS), frequency (RF) link, Bluetooth®, Wi-Fi, a private data network, a virtual private network, a Wi-Fi network, a LAN or WAN network, or other suitable connections that enables the sending and receiving of information between the components of system 100. It will be appreciated that this is not intended to be limiting and that the scope of this disclosure includes implementations in which the client one or more components of system 100 are operatively linked via some other communication media.
It should be appreciated that the illustrated components are depicted as discrete functional blocks, but embodiments are not limited to systems in which the functionality described herein is organized as illustrated. The functionality provided by each of the components may be provided by software or hardware modules that are differently organized than is presently depicted, for example such software or hardware may be intermingled, conjoined, replicated, broken up, distributed (e.g., within a data center or geographically), or otherwise differently organized. The functionality described herein may be provided by one or more processors of one or more computers executing code stored on a tangible, non-transitory, machine readable medium.
In some embodiments, the methods may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The processing devices may include one or more devices executing some or all of the operations of the methods in response to instructions stored electronically on an electronic storage medium. The processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of the method.
At an operation 302 of method 300, heat load forecast may be determined. In some embodiments, operation 302 may be performed by a forecast module the same as or similar to forecast module 150 (shown in
At an operation 304 of method 300, a frost growth forecast may be determined. In some embodiments, the frost growth forecast may be determined based on the heat load forecast. In some embodiments, operation 304 may be performed by a forecast module the same as or similar to forecast module 150 (shown in
At an operation 306 of method 300, control parameters of the heat pump may be determined. In some embodiments, the control parameters of the heat pump may be determined based on the frost growth forecast. In some embodiments, operation 306 may be performed by a control module the same as or similar to control module 180 (shown in
Embodiments of one or more techniques of the present disclosure as described herein may be executed on one or more computer systems, which may interact with various other devices. One such computer system 400 is illustrated by
In the illustrated embodiment, computer system 400 includes one or more processors 410 coupled to a system memory 420 via an input/output (I/O) interface 430. Computer system 400 further includes a network interface 440 coupled to I/O interface 430, and one or more input/output devices 450, such as cursor control device 460, keyboard 470, and display(s) 480. In some embodiments, it is contemplated that embodiments may be implemented using a single instance of computer system 400, while in other embodiments multiple such systems, or multiple nodes making up computer system 400, may be configured to host different portions or instances of embodiments. For example, in one embodiment some elements may be implemented via one or more nodes of computer system 400 that are distinct from those nodes implementing other elements.
In various embodiments, computer system 400 may be a uniprocessor system including one processor 410, or a multiprocessor system including several processors 410 (e.g., two, four, eight, or another suitable number). Processors 410 may be any suitable processor capable of executing instructions, and may include one or more semiconductor(s) and/or transistors (e.g., electronic integrated circuits (ICs)). In such a context, processor-executable instructions may be electronically executable instructions. For example, in various embodiments, processors 410 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 410 may commonly, but not necessarily, implement the same ISA.
In some embodiments, at least one processor 410 may be a graphics processing unit. A graphics processing unit or GPU may be considered a dedicated graphics-rendering device for a personal computer, workstation, game console or other computing or electronic device. Modern GPUs may be very efficient at manipulating and displaying computer graphics, and their highly parallel structure may make them more effective than typical CPUs for a range of complex graphical algorithms. For example, a graphics processor may implement a number of graphics primitive operations in a way that makes executing them much faster than drawing directly to the screen with a host central processing unit (CPU). In various embodiments, the image processing methods disclosed herein may, at least in part, be implemented by program instructions configured for execution on one of, or parallel execution on two or more of, such GPUs. The GPU(s) may implement one or more application programmer interfaces (APIs) that permit programmers to invoke the functionality of the GPU(s). Suitable GPUs may be commercially available from vendors such as NVIDIA Corporation, ATI Technologies (AMD), and others. In some embodiments, one or more computers may include multiple processors operating in parallel. A processor may be a central processing unit (CPU) or a special-purpose computing device, such as graphical processing unit (GPU), an integrated circuit or on-chip system, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a complex programmable logic device (CPLD), or application-specific integrated circuits.
System memory 420 may be configured to store program instructions and/or data accessible by processor 410. In various embodiments, system memory 420 may be implemented using any suitable memory technology, such as static random-access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory. In the illustrated embodiment, program instructions and data implementing desired functions, such as those described in this disclosure, are shown stored within system memory 420 as program instructions 425 and data storage 435, respectively. In other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memory 420 or computer system 400. Generally speaking, a computer-accessible medium may include storage media or memory media such as magnetic or optical media, e.g., disk or CD/DVD-ROM coupled to computer system 400 via I/O interface 430. Program instructions and data stored via a computer-accessible medium may be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link, such as may be implemented via network interface 440.
In one embodiment, I/O interface 430 may be configured to coordinate I/O traffic between processor 410, system memory 420, and any peripheral devices in the device, including network interface 440 or other peripheral interfaces, such as input/output devices 450. In some embodiments, I/O interface 430 may perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory 420) into a format suitable for use by another component (e.g., processor 410). In some embodiments, I/O interface 430 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I/O interface 430 may be split into two or more separate components, such as a north bridge and a south bridge, for example. In addition, in some embodiments some or all of the functionality of I/O interface 430, such as an interface to system memory 420, may be incorporated directly into processor 410.
Network interface 440 may be configured to allow data to be exchanged between computer system 400 and other devices attached to a network, such as other computer systems, or between nodes of computer system 400. In various embodiments, network interface 440 may support communication via wired or wireless general data networks, such as any suitable type of Ethernet network, for example, via telecommunications/telephony networks such as analog voice networks or digital fiber communications networks; via storage area networks such as Fibre Channel SANs, or via any other suitable type of network and/or protocol.
Input/output devices 450 may, in some embodiments, include one or more display terminals, cursor control devices (e.g., mouse), keyboards, keypads, touchpads, touchscreens, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or retrieving data by one or more computer system 400. Multiple input/output devices 450 may be present in computer system 400 or may be distributed on various nodes of computer system 400. In some embodiments, similar input/output devices may be separate from computer system 400 and may interact with one or more nodes of computer system 400 through a wired or wireless connection, such as over network interface 440.
Those skilled in the art will appreciate that computer system 400 is merely illustrative and is not intended to limit the scope of the present disclosure. In particular, computer system 400 may also be connected to other devices that are not illustrated, or instead may operate as a stand-alone system. In addition, the functionality provided by the illustrated components may in some embodiments be combined in fewer components or distributed in additional components. Similarly, in some embodiments, the functionality of some of the illustrated components may not be provided and/or other additional functionality may be available.
It should be understood that the description and the drawings are not intended to limit the invention to the particular form disclosed, but to the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. Further modifications and alternative embodiments of various aspects of the invention will be apparent to those skilled in the art in view of this description. Accordingly, this description and the drawings are to be construed as illustrative only and are for the purpose of teaching those skilled in the art the general manner of carrying out the invention. It is to be understood that the forms of the invention shown and described herein are to be taken as examples of embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed or omitted, and certain features of the invention may be utilized independently, all as would be apparent to one skilled in the art after having the benefit of this description of the invention. Changes may be made in the elements described herein without departing from the spirit and scope of the invention as described in the following claims. Headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description.
As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). The words “include”, “including”, and “includes” and the like mean including, but not limited to. As used throughout this application, the singular forms “a,” “an,” and “the” include plural referents unless the content explicitly indicates otherwise. Thus, for example, reference to “an element” or “a element” includes a combination of two or more elements, notwithstanding use of other terms and phrases for one or more elements, such as “one or more.” The term “or” is, unless indicated otherwise, non-exclusive, i.e., encompassing both “and” and “or.” Terms describing conditional relationships, e.g., “in response to X, Y,” “upon X, Y,”, “if X, Y,” “when X, Y,” and the like, encompass causal relationships in which the antecedent is a necessary causal condition, the antecedent is a sufficient causal condition, or the antecedent is a contributory causal condition of the consequent, e.g., “state X occurs upon condition Y obtaining” is generic to “X occurs solely upon Y” and “X occurs upon Y and Z.” Such conditional relationships are not limited to consequences that instantly follow the antecedent obtaining, as some consequences may be delayed, and in conditional statements, antecedents are connected to their consequents, e.g., the antecedent is relevant to the likelihood of the consequent occurring. Further, unless otherwise indicated, statements that one value or action is “based on” another condition or value encompass both instances in which the condition or value is the sole factor and instances in which the condition or value is one factor among a plurality of factors. Unless otherwise indicated, statements that “each” instance of some collection have some property should not be read to exclude cases where some otherwise identical or similar members of a larger collection do not have the property, i.e., each does not necessarily mean each and every. Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining” or the like refer to actions or processes of a specific apparatus, such as a special purpose computer or a similar special purpose electronic processing/computing device.
The application claims the benefit of U.S. Provisional Application No. 63/269,267 filed Mar. 14, 2022, the contents of which are hereby incorporated in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63269267 | Mar 2022 | US |
