Systems and Methods for Fuel Type Detection for Hydronic and Other Heating Systems

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Application No. 63/660,327, filed Jun. 14, 2024, the entirety of which is hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure is generally in the field of gas fired heating systems. For example, systems and methods are provided herein for detecting a fuel type in a gas fired heat exchanger system such as a hydronic system, a boiler, a water heater, an air handler, or other heating systems.

BACKGROUND

Hydronic systems such as boilers conveniently and efficiently heat fluids such as water for heating purposes and/or consumption in residential and commercial use. For example, hydronic systems may include a heat exchanger that consumes (e.g., burns) a fuel such as propane or natural gas to heat a water source. The heated water may then be circulated throughout residential or commercial structure to heat such spaces. For example, the hydronic system may include radiators for exchanging heat between the heated water and the surrounding environment.

Other heating systems such as water heaters, pool heaters, gas fired water heaters, gas furnaces, and other gas fired systems may use a similar technique in that such systems may use fuel and a heat exchanger to heat a fluid such as water or a refrigerant. The heated water and/or refrigerant may be used to exchange heat with the surrounding environment or the heated water may be used as a hot water supply. For systems such as gas furnaces, the fuel may be used to heat air for circulation throughout a residential or commercial structure.

While hydronic and other gas fired heating systems may be capable of consuming different types of fuel, the system must be adjusted based on the fuel type. For example, a boiler may be tuned to consume natural gas as well as propane. However, for efficiency and safety purposes, a technician is required to fine tune operation of the system including adjusting gas valve settings, fan speed settings, emergency shut down settings, and other settings depending on the fuel to be used. If the wrong fuel is input into the system, the efficiency of the system will be significantly impacted as the air-to-fuel ratio as a result of the valve and fan settings will be less than ideal. Additionally, inputting the wrong fuel into the system can result in unintended combustion and temperature profiles, which may result in malfunctions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an exemplary gas fired system for heating a structure as well as a remote server in communication with a controller of the gas fired system for providing fuel detection machine learning models.

FIG. 2 is a schematic illustration of a fuel detection machine learning model in the form of a recurrent neural network.

FIG. 3 illustrates a schematic block diagram of a controller of a gas fired system, a remote server, and a user device.

FIG. 4 illustrates a process flow for detecting a fuel type consumed by a gas fired system and adjusting operation of the gas fired system based on the detected fuel type.

FIG. 5 is a schematic block diagram of a gas fired system in accordance with one or more example embodiments of the disclosure.

DETAILED DESCRIPTION

Gas fired appliances such as a hydronic system, a boiler, water heater, furnace, and other fuel powered heating systems have been developed which are capable detecting a fuel type being consumed by the system and adjusting operation of the system based on the fuel type detected. For example, the gas fired appliance, which may be a hydronic or other heating system may have a controller capable of a running a machine learning model trained to detect a fuel type based on operational data corresponding to the gas fired appliance. For example, the inlet and/or outlet temperature of water in the gas fired system, a vent temperature, and/or a flame current may be used to determine the type of fuel (e.g., propane, natural gas, etc.).

Once the type of fuel is determined operation of the gas fired system or other heating system may be adjusted. For example, the system may be powered down, a gas valve may be adjusted to adjust fuel injected into the heat exchanger, or a fan (e.g., blower) speed (e.g., revolutions per minute) may be adjusted. In one example, the gas valve and the fan speed may be adjusted to achieve an optimal air-to-fuel ratio for the given fuel type.

Referring now to FIG. 1, an exemplary gas fired system is depicted for heating a structure as well as a remote server in communication with a controller of the gas fired system for providing fuel detection machine learning models. In one example, the gas fired system may be boiler though different types of gas fired (e.g., hydronic) systems may be used. While gas fired systems are described for illustrative purposes in FIGS. 1-5, it is understood that other fuel fired heating systems (e.g., pool heaters, heat pumps, water heaters, gas furnaces, etc.) may be used instead.

As shown in FIG. 1, gas fired system 102 may be used to heat a structure 104. Gas fired system 102 may be any suitable boiler system or system that exchanges heat with water or other fluid for heating a residence or commercial structure. In the example shown in FIG. 1, the structure 104 is a residential structure. Other structures and configurations of zones are contemplated by this disclosure. Gas fired system 102 may include heat exchanger 110 which may burn a fuel from fuel source 106 to generate a flame for combustion to generate heat. The heat exchanger may exchange heat with a fluid such as water that may enter gas fired system 102 via fluid inlet 114 and may exit via outlet 112. For example, water may be circulated throughout residential structure 104 and may traverse heater exchanger 135 to exchange thermal energy between the heated water and the surrounding environment.

Fuel source 106 may be a fuel tank or fuel line that may be connected to gas fired system 102 to provide a fuel supply to heat exchanger 110. Fuel source 106 may provide natural gas, propane, biogas, hydrogen, or any other fuel for consumption by gas fired system 110. Gas fired system 102 may further include fan 118 which may provide an airflow for adjusting a fuel-to-air ratio for combustion of the fuel for heat exchange. Fan 118 may have an adjustable speed (e.g., revolutions per minute (RPM)) such that the airflow may be manipulated to adjust the air-to-fuel ratio.

Gas fired system 102 may further include fuel valve 117 which may be an adjustable value for adjusting an amount of fuel entering a combustion chamber of the heat exchanger. The fuel-to-air ratio for gas fired system 102 may be modified by adjusting the speed of fan 118 and/or the opening of fuel valve 117. For example, certain combustion or heat profiles or curves and efficiencies may be achieved for a given fuel with known combustion qualities by adjusting fuel valve 117 and/or the speed of fan 118. In one example, gas fired system 102 may be tuned for optimal efficiency and/or performance with one or more fan and fuel valves settings and gas fired system 102 may have one or more different settings for optimal efficiency and/or performance for natural gas.

Gas fired system 102 may further include vent 120 which may be a vent or flue for expelling exhaust gas from the combustion chamber of heat exchanger 110 as well as a safety feature which may include a sensor designed to cause the gas fired system to cease to operate and thus shut down (e.g., to prevent operational malfunctions). Gas fired system 102 may further include inlet sensor 114, outlet sensor 112, flame current sensor 128, vent sensor 126, fan sensor 125, and any other sensors or device for generating operational information about gas fired system 102. Controller 116 may control the operation of gas fired system 102 and may be any suitable computing device having a processor and memory.

Controller 116 may communicate with and/or receive information from inlet sensor 114, outlet sensor 112, flame current sensor 128, vent sensor 126, and/or fan sensor 125. Inlet sensor 114 may generate inlet temperatures which may be indicative of a temperature of the fluid (e.g., water) as it enters gas fired system 102. Outlet sensor 112 may generate outlet temperatures which may be indicative of a temperature of the fluid (e.g., water) as it leaves gas fired system 102. Flame current sensor 128 is a flame current value indicative of an intensity of combustion in the heat exchanger of the gas fired system. Vent sensor 126 may generate a vent temperature indicative of a temperature in vent 120. Fan sensor 125 may generate a fan speed reading indicative of a speed of the fan (e.g., RPMs). Controller 116 may also be programmed to know the altitude of the gas fired system, which may be included in the operational data processed by the machine learning model.

Controller 116 may communicate with remote server 108 via any well-known wireless communication technology (e.g., cellular, satellite, WiFi, etc.). Remote server 108 may send machine learning models trained to detect a type of fuel consumed by gas fired system 102. For example, remote server 108 may send controller 116 a recurrent neural network trained using operational data (e.g., inlet temperature, outlet temperature, vent temperature, fan speed, flame current data, etc.) known to correspond to a certain fuel type to train a model to detect the same fuel type using only such operational data.

Controller 116 may further be in communication with user device 137, which may include a processor and memory, and may communicate wireless with controller 116 via any suitable wireless communication technology. User device 137 may include a touch screen and/or buttons and may be used to select settings (e.g., set points) for gas fired system 102.

Referring now to FIG. 2, a fuel detection machine learning model in the form of a recurrent neural network is illustrated. For example, machine learning model 200 may be a recurrent neural network and may capture historical information from prior inputs into machine learning model. Alternatively, machine learning model 200 may be a feed-forward network or any other type of suitable neural network.

Inputs 202 may be input into machine learning model 200 and processed or otherwise analyzed by machine learning model 200. Inputs 202 may include one or more types of operational data and/or may include data indicative of operation of the gas fired system not shown in FIG. 2. Inputs 202 may be operational data and may be received by the controller from one or more sensors of the gas fired system, may be generated by one or more signals in the gas fired system, may be received from the user device, and/or may be generated and/or calculated based on one or more of the foregoing.

As shown in FIG. 2, input layer 202 may include inlet temperature 210 which may be indicative of a temperature at a fluid inlet of a gas fired system, outlet temperature 212 which may be indicative of temperature at a fluid outlet of the gas fired system, a difference between the inlet and outlet temperatures, fan speed setpoint 216 which may be indicative of fan setting (e.g., fan speed setting), fan speed feedback 218 which may be indicative of the actual or measured speed at which the fan is moving, vent temperature 220, which may be indicative of the temperature in the vent, flame current 222, which may be indicative of a degree of intensity of combustion, oxygen value 228, which may indicative of a level or percentage of oxygen, calculated heat output 228, which may be a calculated value of heat generated by the gas fired system, and/or operating status 230, which may indicative operational settings (e.g., system modes, cycles, set points, or the like). Input layer 202 may include fewer or greater number of operational parameters in input layer 202 and/or may include operational parameters different from those illustrated in FIG. 2. Including several input parameters (e.g., input parameters 210-230) significantly improves accuracy of outputs 232 and 234 as compared to a single input parameter or only a select number of parameters from sensors.

As shown in FIG. 2, machine learning model 200 may include hidden layers 204, which may include multiple hidden layers. For example, three hidden layers may be used. Hidden layers 204 may be used to account for prior inputs in addition to the current inputs. Finally, machine learning model 202 may include output layer 206, which may include output 232 and output 234. Output 232 may be indicative of a likelihood of a presence or a confidence in the presence of a first fuel type. Output 234 may be indicative of a likelihood of a presence or a confidence in the presence of a second fuel type. While the model illustrated in FIG. 2 generates outputs for only two fuel types, it is understood that machine learning model 200 could alternatively generate outputs for more than two fuel types (e.g., three, four, five, six, etc.). In one example, outputs 232 and 234 may be a numeral value between 0 and 100 or between 0 and 1. Outputs 232 and 234 may be integer values or decimal values.

Referring now to FIG. 3, a schematic block diagram of a controller of a gas fired system, a remote server, and a user device is depicted. Controller 302 may be the same as or similar to controller 116 of FIG. 1. Controller 302 may include several modules for controlling operation of the gas fired system and detecting a fuel type consumed by the gas fired system. Controller 302 may include an implementation module for overseeing tasks and operations performed by other modules of controller 302. Sensors module may communicate with implementation module and may determine and/or generate sensor data (e.g., temperature data, speed data, status data, etc.).

Fuel determination module 316 may oversee execution of the machine learning module and may also communicate with implementation module. Communication module 312 may permit controller 302 to communicate with user device controller 306 and remote server controller 304. For example, communication module may communicate with remote server controller 304 to receive the machine learning model from remote server controller 304. Systems operations module 316 may control operation of the components of the gas fired system (e.g., heat exchanger, fuel valve, fan, etc.). For example, operations module 316 may send instructions to the fuel valve to cause the fuel valve to open or close. Fuel type settings 317 may maintain optimal settings for operating components of the gas fired system for each fuel type. For example, certain fan speed and fuel valve settings may be maintained for achieving an optimal or desired air-to-fuel ratio for each fuel type.

Remote server controller 304 may include communication module for communicating with controller 302 as well as fuel detection module 326. Fuel detection module 326 may maintain a robust machine learning model that was trained using operational data known to correspond to a certain fuel type and verified using different operational data known to correspond to that certain fuel type. The model may be trained to take into account performance changes over time as the gas fired system ages and/or experiences wear and tear. Further, data from various of gas fired systems that are operational and in use may be used to train the model to improve accuracy. In one example, training data may include operational data from different equipment models, years, ages, locations, and the like. As remote server controller 304 may have greater computing capability (e.g., memory, processor speed, etc.) the model maintained on remote server controller 304 may be a more robust version of the model shared with controller 302. For example, the model on remote server 304 may be shrunken down for execution on controller 302. Techniques for shrinking the model may include model pruning (e.g., removing inputs and/or weights with little impact), quantization (e.g., truncating data and/or using integers), knowledge distillation (e.g., recreating simpler inner layers of model), embedded optimized code (e.g., using direct memory manipulation), use of optimized libraries (e.g., libraries optimized for certain hardware), and the like. The model may be reduced such that it may be run on a thread under the main application program for operating the gas fired system.

User device controller 306 may include communication module 302 for communicating with controller 302 of the gas fired system and may further include system settings module. System settings module may oversee input received from a user of the gas fired system and may generate operational settings, instructions or commands to be processed by controller 302 and which may be indicative of desired temperatures, setpoints, or the like.

Referring now to FIG. 4, a process flow for detecting a fuel type consumed by a gas fired system and adjusting operation of the gas fired system based on the detected fuel type is depicted. While example embodiments of the disclosure may be described in the context of a controller (e.g., controller 116 of FIG. 1), it should be appreciated that the disclosure is more broadly applicable to various types of computing devices as well as a controller in combination with a computing device, such as a server. Some or all of the blocks of the process flows in this disclosure may be performed in a distributed manner across any number of devices. The operations of process flow 400 may be optional and may be performed in a different order.

At block 402, computer-executable instructions stored on a memory of a device, such as a controller, may be executed to determine a fuel detection model and/or an updated fuel detection model. This may the shrunken or reduced model provided by the remote server. The remote server may periodically provide the model and/or the controller may periodically inquire whether an updated model is available.

At block 404, computer-executable instructions stored on a memory of a device, such as a controller, may be executed to determine sensor data and/or request sensor data. The sensor data may include inlet temperature of the fluid, outlet temperature of the fluid, vent temperature, flame current, fan speed, operational status, or any other operational information that may be sensed by the gas fired system.

At block 406, computer-executable instructions stored on a memory of a device, such as a controller, may be executed to determine calculate input data based on the sensor data. Such input data may include temperature differentials, speed differentials, current differentials, calculated heat values, fluid flow rates, and the like. At block 408, computer-executable instructions stored on a memory of a device, such as a controller, may be executed to analyze and/or process the sensor data, settings data corresponding to operation of the gas fired system, and/or calculated input data, each being operational data, using the fuel detection model determined at block 402. Alternatively, a remote server in communication with the controller may execute the machine learning model to analyze and/or process the sensor data, settings data and/or calculated input data. In this example, the controller may send such data to the remote server and may receive from the remote server predictions of a fuel type (e.g., based on fuel confidence levels). Based on the fuel type predictions, the controller can adjust system operations as described at block 418.

At block 410, computer-executable instructions stored on a memory of a device, such as a controller, may be executed to determine fuel confidence values based on the outputs of the machine learning model. For example, the machine learning model may output a likelihood value of a presence of a certain type of fuel or a confidence level that a certain type of fuel is being consumed by the gas fired system. At decision 412, the presence of a given fuel type may be determined by comparing the likelihood value or confidence level to a threshold value. For example, the threshold value may be 0.95 or 95% and if the confidence level or likelihood value is the same as or exceeds the threshold value, the fuel may be determined to be present (e.g., to be currently consumed by the gas fired system).

At decision 414, computer-executable instructions stored on a memory of a device, such as a controller, may be executed to consider whether the fuel type has changed. To make this determination the fuel type determined to be present may be compared against a last known fuel type determine to be present. If the fuel type has not changed, the block 402 may be reinitiated. If the fuel types are different, then fuel type may be determined to have changed. The last known fuel type may be saved locally on the controller. If the fuel type has changed, the new fuel type may be saved as the current fuel type.

At block 416, computer-executable instructions stored on a memory of a device, such as a controller, may be executed to determine the settings for the new fuel type. For example, for each fuel type the controller may maintain fuel valve settings, fan settings, and other operational settings to achieve optimal or desired fuel-to-air ratios and/or efficiency of the gas fired system.

At block 418, computer-executable instructions stored on a memory of a device, such as a controller, may be executed to adjust operation of the gas fired system based on the operational settings determined at block 416. For example, the fan speed may be adjusted and/or the gas valve may be adjusted to increase or reduce the fuel flow into the combustion chamber. Alternatively, the gas fired system may be immediately shut down for safety purposes. The adjustments made at block 418 may depend on the air-to-fuel mixture of the fuel type determined at block 412. In one example, the fan speed may be increased for a fuel type with a lean mixture or may be reduced for fuel type having a rich mixture. For example, propane may be considered a richer mixture than natural gas and natural gas may be considered a leaner mixture than propane. In another example, a fuel valve may be caused to be opened by the controller for a rich mixture and caused to be closed for a lean mixture. In yet another example, the fan speed may be increased and the fuel valve may be closed for a fuel type with a lean mixture and/or the fan speed may be decreased and the fuel valve may be opened for a fuel type with a rich mixture.

At block 420, computer-executable instructions stored on a memory of a device, such as a controller, may be executed to generate an alert and/or cause the user device to present an alert regarding the new fuel type or changes to operational settings. For example, the controller may cause the user device to generate an alert that a new fuel type was detected and certain settings of the gas fired system were automatically adjusted for the new fuel type. Alternatively, if the gas fired system is shut down at block 418, the alert may inform the user that the fuel type changed and the system was powered off.

FIG. 5 is a schematic block diagram of controller 500, in accordance with one or more example embodiments of the disclosure. Controller 500 may be the same as controller 116 of FIG. 1. While the schematic block diagram is described with respect to controller 500, it is understood that other controllers, servers, and/or computing devices may additionally or alternatively be used.

Controller 500 may be configured to communicate with one or more servers, computing devices, controllers, user devices, other systems, or the like. Controller 500 may be configured to communicate via one or more networks. Such network(s) may include, but are not limited to, any one or more different types of communications networks such as, for example, cable networks, public networks (e.g., the Internet), private networks (e.g., frame-relay networks), wireless networks, cellular networks, telephone networks (e.g., a public switched telephone network), or any other suitable private or public packet-switched or circuit-switched networks.

In an illustrative configuration, controller 500 may include one or more processors 502, one or more memory devices 504 (also referred to herein as memory 504), one or more input/output (I/O) interface(s) 506, one or more network interface(s) 508, one or more transceiver(s) 510, one or more antenna(s) 534, and data storage 520. The controller 500 may further include one or more bus(es) 518 that functionally couple various components of the controller 500.

The bus(es) 518 may include at least one of a system bus, a memory bus, an address bus, or a message bus, and may permit exchange of information (e.g., data (including computer-executable code), signaling, etc.) between various components of the controller 500. The bus(es) 518 may include, without limitation, a memory bus or a memory controller, a peripheral bus, an accelerated graphics port, and so forth. The bus(es) 518 may be associated with any suitable bus architecture.

The memory 504 may include volatile memory (memory that maintains its state when supplied with power) such as random access memory (RAM) and/or non-volatile memory (memory that maintains its state even when not supplied with power) such as read-only memory (ROM), flash memory, ferroelectric RAM (FRAM), and so forth. Persistent data storage, as that term is used herein, may include non-volatile memory. In various implementations, the memory 404 may include multiple different types of memory such as various types of static random access memory (SRAM), various types of dynamic random access memory (DRAM), various types of unalterable ROM, and/or writeable variants of ROM such as electrically erasable programmable read-only memory (EEPROM), flash memory, and so forth.

The data storage 520 may include removable storage and/or non-removable storage including, but not limited to, magnetic storage, optical disk storage, and/or tape storage. The data storage 520 may provide non-volatile storage of computer-executable instructions and other data. The memory 504 and the data storage 520, removable and/or non-removable, are examples of computer-readable storage media (CRSM) as that term is used herein. The data storage 420 may store computer-executable code, instructions, or the like that may be loadable into the memory 504 and executable by the processor(s) 502 to cause the processor(s) 502 to perform or initiate various operations. The data storage 520 may additionally store data that may be copied to memory 504 for use by the processor(s) 502 during the execution of the computer-executable instructions. Moreover, output data generated as a result of execution of the computer-executable instructions by the processor(s) 502 may be stored initially in memory 504, and may ultimately be copied to data storage 520 for non-volatile storage.

The data storage 520 may store one or more operating systems (O/S) 522; one or more optional database management systems (DBMS) 524; and one or more program module(s), applications, engines, computer-executable code, scripts, or the like such as, for example, one or more implementation modules 526, system operation modules 527, communication modules 528, fuel determination module 530, and sensor module 532. Some or all of these modules may be sub-modules. Any of the components depicted as being stored in data storage 520 may include any combination of software, firmware, and/or hardware. The software and/or firmware may include computer-executable code, instructions, or the like that may be loaded into the memory 504 for execution by one or more of the processor(s) 502. Any of the components depicted as being stored in data storage 520 may support functionality described in reference to correspondingly named components earlier in this disclosure.

Referring now to other illustrative components depicted as being stored in the data storage 520, the O/S 522 may be loaded from the data storage 520 into the memory 504 and may provide an interface between other application software executing on the controller 500 and hardware resources of the controller 500. More specifically, the O/S 522 may include a set of computer-executable instructions for managing hardware resources of the controller 500 and for providing common services to other application programs (e.g., managing memory allocation among various application programs). In certain example embodiments, the O/S 522 may control execution of the other program module(s) to for content rendering. The O/S 522 may include any operating system now known or which may be developed in the future including, but not limited to, any server operating system, any mainframe operating system, or any other proprietary or non-proprietary operating system.

The optional DBMS 524 may be loaded into the memory 504 and may support functionality for accessing, retrieving, storing, and/or manipulating data stored in the memory 404 and/or data stored in the data storage 520. The DBMS 524 may use any of a variety of database models (e.g., relational model, object model, etc.) and may support any of a variety of query languages. The DBMS 524 may access data represented in one or more data schemas and stored in any suitable data repository including, but not limited to, databases (e.g., relational, object-oriented, etc.), file systems, flat files, distributed datastores in which data is stored on more than one node of a computer network, peer-to-peer network datastores, or the like.

The optional input/output (I/O) interface(s) 506 may facilitate the receipt of input information by the controller 500 from one or more I/O devices as well as the output of information from the controller 500 to the one or more I/O devices. The I/O devices may include any of a variety of components such as a display or display screen having a touch surface or touchscreen; an audio output device for producing sound, such as a speaker; an audio capture device, such as a microphone; an image and/or video capture device, such as a camera; and so forth. Any of these components may be integrated into the controller 500 or may be separate.

The controller 500 may further include one or more network interface(s) 508 via which the con controller 500 may communicate with any of a variety of other systems, platforms, networks, devices, and so forth. The network interface(s) 508 may enable communication, for example, with one or more wireless routers, one or more host servers, one or more web servers, and the like via one or more of networks.

The antenna(s) 534 may include any suitable type of antenna depending, for example, on the communications protocols used to transmit or receive signals via the antenna(s) 534. Non-limiting examples of suitable antennas may include directional antennas, non-directional antennas, dipole antennas, folded dipole antennas, patch antennas, multiple-input multiple-output (MIMO) antennas, or the like. The antenna(s) 534 may be communicatively coupled to one or more transceivers 510 or radio components to which or from which signals may be transmitted or received. Antenna(s) 534 may include, without limitation, a cellular antenna for transmitting or receiving signals to/from a cellular network infrastructure, an antenna for transmitting or receiving Wi-Fi signals to/from an access point (AP), a Global Navigation Satellite System (GNSS) antenna for receiving GNSS signals from a GNSS satellite, a Bluetooth antenna for transmitting or receiving Bluetooth signals including BLE signals, a Near Field Communication (NFC) antenna for transmitting or receiving NFC signals, a 900 MHz antenna, and so forth.

The transceiver(s) 510 may include any suitable radio component(s) for, in cooperation with the antenna(s) 534, transmitting or receiving radio frequency (RF) signals in the bandwidth and/or channels corresponding to the communications protocols utilized by the controller 500 to communicate with other devices. The transceiver(s) 510 may include hardware, software, and/or firmware for modulating, transmitting, or receiving-potentially in cooperation with any of antenna(s) 534—communications signals according to any of the communications protocols discussed above including, but not limited to, one or more Wi-Fi and/or Wi-Fi direct protocols, as standardized by the IEEE 802.11 standards, one or more non-Wi-Fi protocols, or one or more cellular communications protocols or standards. The transceiver(s) 510 may further include hardware, firmware, or software for receiving GNSS signals. The transceiver(s) 410 may include any known receiver and baseband suitable for communicating via the communications protocols utilized by the controller 500. The transceiver(s) 510 may further include a low noise amplifier (LNA), additional signal amplifiers, an analog-to-digital (A/D) converter, one or more buffers, a digital baseband, or the like.

Referring now to functionality supported by the various program module(s) depicted in FIG. 5, the implementation module(s) 526 may include computer-executable instructions, code, or the like that responsive to execution by one or more of the processor(s) 502 may perform functions including, but not limited to, overseeing coordination, communication, and interaction between one or more modules and computer executable instructions in data storage 520.

The system operation module(s) 527 may include computer-executable instructions, code, or the like that responsive to execution by one or more of the processor(s) 502 may perform functions including, but not limited to, controlling the operation of various components of the gas fired system including the fuel valve and/or the fan speed.

The communication module(s) 528 may include computer-executable instructions, code, or the like that responsive to execution by one or more of the processor(s) 402 may perform functions including, but not limited to, communicating with one or remote servers for receiving the machine learning model and/or more user devices for receiving setpoints and/or temperature settings.

The fuel determination module(s) 530 may include computer-executable instructions, code, or the like that responsive to execution by one or more of the processor(s) 502 may oversee operation of the heat pump and may perform functions including, but not limited to, executing one or more machine learning models using operational data and comparing outputs of the models to threshold values.

The sensor module(s) 532 may include computer-executable instructions, code, or the like that responsive to execution by one or more of the processor(s) 502 may oversee generation for operational data including sensor data, calculated values, operating information (e.g., status and/or modes), and the like.

Although specific embodiments of the disclosure have been described, one of ordinary skill in the art will recognize that numerous other modifications and alternative embodiments are within the scope of the disclosure. For example, any of the functionality and/or processing capabilities described with respect to a particular device or component may be performed by any other device or component. Further, while various illustrative implementations and architectures have been described in accordance with embodiments of the disclosure, one of ordinary skill in the art will appreciate that numerous other modifications to the illustrative implementations and architectures described herein are also within the scope of this disclosure.

Certain aspects of the disclosure are described above with reference to block and flow diagrams of systems, methods, apparatuses, and/or computer program products according to example embodiments. It will be understood that one or more blocks of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and the flow diagrams, respectively, may be implemented by execution of computer-executable program instructions. Likewise, some blocks of the block diagrams and flow diagrams may not necessarily need to be performed in the order presented, or may not necessarily need to be performed at all, according to some embodiments. Further, additional components and/or operations beyond those depicted in blocks of the block and/or flow diagrams may be present in certain embodiments.

Accordingly, blocks of the block diagrams and flow diagrams support combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, may be implemented by special-purpose, hardware-based computer systems that perform the specified functions, elements or steps, or combinations of special-purpose hardware and computer instructions.

Program module(s), applications, or the like disclosed herein may include one or more software components, including, for example, software objects, methods, data structures, or the like. Each such software component may include computer-executable instructions that, responsive to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.

A software component may be coded in any of a variety of programming languages. An illustrative programming language may be a lower-level programming language such as an assembly language associated with a particular hardware architecture and/or operating system platform. A software component comprising assembly language instructions may require conversion into executable machine code by an assembler prior to execution by the hardware architecture and/or platform.

Another example programming language may be a higher-level programming language that may be portable across multiple architectures. A software component comprising higher-level programming language instructions may require conversion to an intermediate representation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to, a macro language, a shell or command language, a job control language, a script language, a database query or search language, or a report writing language. In one or more example embodiments, a software component comprising instructions in one of the foregoing examples of programming languages may be executed directly by an operating system or other software component without having to be first transformed into another form.

A software component may be stored as a file or other data storage construct. Software components of a similar type or functionally related may be stored together such as, for example, in a particular directory, folder, or library. Software components may be static (e.g., pre-established or fixed) or dynamic (e.g., created or modified at the time of execution).

Software components may invoke or be invoked by other software components through any of a wide variety of mechanisms. Invoked or invoking software components may comprise other custom-developed application software, operating system functionality (e.g., device drivers, data storage (e.g., file management) routines, other common routines, and services, etc.), or third-party software components (e.g., middleware, encryption, or other security software, database management software, file transfer or other network communication software, mathematical or statistical software, image processing software, and format translation software).

Software components associated with a particular solution or system may reside and be executed on a single platform or may be distributed across multiple platforms. The multiple platforms may be associated with more than one hardware vendor, underlying chip technology, or operating system. Furthermore, software components associated with a particular solution or system may be initially written in one or more programming languages, but may invoke software components written in another programming language.

Computer-executable program instructions may be loaded onto a special-purpose computer or other particular machine, a processor, or other programmable data processing apparatus to produce a particular machine, such that execution of the instructions on the computer, processor, or other programmable data processing apparatus causes one or more functions or operations specified in the flow diagrams to be performed. These computer program instructions may also be stored in a CRSM that upon execution may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement one or more functions or operations specified in the flow diagrams. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process.

Additional types of CRSM that may be present in any of the devices described herein may include, but are not limited to, programmable random access memory (PRAM), SRAM, DRAM, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the information and which can be accessed. Combinations of any of the above are also included within the scope of CRSM. Alternatively, computer-readable communication media (CRCM) may include computer-readable instructions, program module(s), or other data transmitted within a data signal, such as a carrier wave, or other transmission. However, as used herein, CRSM does not include CRCM.

Although embodiments have been described in language specific to structural features and/or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments. Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments could include, while other embodiments do not include, certain features, elements, and/or steps. Thus, such conditional language is not generally intended to imply that features, elements, and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and/or steps are included or are to be performed in any particular embodiment.