METHOD AND SYSTEM FOR ESTIMATING OUTSIDE AIR TEMPERATURE

FIELD OF THE INVENTION

The disclosure generally relates to estimating temperature, and more specifically related to a method and a system for estimating outside air temperature (OAT) for a heating, ventilation, and air conditioning (HVAC) device.

BACKGROUND

A heating, ventilation, and air conditioning (HVAC) system moves air between indoor and outdoor areas and heats/cools residential and commercial buildings. The HVAC system keeps a user's environment warm and cozy in winter, and cool in summer. In some conventional HVAC systems, the user may manually set a temperature by using the conventional HVAC system's thermostat on a daily basis based on outside weather conditions and user's requirements. This manual setting between a cooling mode and a heating mode may result in HVAC equipment damage as well as energy waste.

Furthermore, some conventional HVAC systems lack the capability of measuring a local outdoor air temperature (OAT). As a result, the conventional HVAC systems are unable to maintain desired temperatures in residential and commercial buildings and must rely on other systems. In this case, the temperature is automatically set by obtaining local weather data from other systems (e.g., available online weather forecast agencies, a nearby weather station, a Zip code referenced, a cloud-based system of dedicated weather station, etc.), as shown in FIG. 1. These other systems cover large geographic areas, however, average temperatures over long distances result in inaccuracies in specific air temperatures around the device.

For example, to maintain desired temperatures at location 1, the conventional HVAC system at location 1 obtains local weather data from location 2 (other systems). The desired temperature at location 2 may be “Temperature level-X,” and such other systems may share this information with the conventional HVAC system at location 1. In a real-world scenario, locations 1 and 2 may be far apart (10 miles), and there is a possibility of a temperature difference between these other systems and HVAC systems. As a result, the conventional HVAC system at location 1 sets the desired temperature as “Temperature level-X,” despite the fact that it should be “Temperature level-1,” resulting in inaccuracies in specific air temperatures that cause an unpleasant user experience at location 1.

Thus, it is desired to address the above-mentioned disadvantages or at least provide a useful alternative for estimating the outside air temperature (OAT).

SUMMARY

This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the disclosure nor is it intended for determining the scope of the disclosure.

Disclosed herein is a method of estimating outside air temperature for a first device. The method includes receiving, from the first device, a request to obtain an estimate of outside air temperature at a first location of the first device. The method further includes identifying at least one heating, ventilation, and air conditioning (HVAC) device disposed within a predefined threshold distance from the first device, wherein each of the at least one HVAC device is equipped with an outside air temperature sensor. The method further includes obtaining one or more current temperature values associated with the at least one HVAC device, wherein each of the one or more current temperature values, associated with a respective HVAC device of the at least one HVAC device, is measured at a respective second location of the corresponding HVAC device. The method further includes estimating the outside air temperature for the first device based on the one or more current temperature values measured at the respective second location of each of the at least one HVAC device.

In one or more embodiments, the first device is an HVAC device without an outside air temperature sensor.

In one or more embodiments, the outside air temperature sensor is a thermistor.

In one or more embodiments, the method includes transmitting the estimated outside air temperature to the first device, wherein the estimated outside air temperature corresponds to a real-time temperature of the first location associated with the first device.

In one or more embodiments, obtaining the current temperature comprises transmitting, in real-time, a request to one or more of the at least one HVAC device to obtain a corresponding current temperature value of the one or more current temperature values. Also, obtaining the current temperature further comprises receiving the corresponding current temperature value from the one or more HVAC devices based on a real-time measurement of temperature using a respective outside air temperature sensor associated with each of the one or more HVAC devices.

In one or more embodiments, obtaining the current temperature values comprises obtaining the one or more current temperature values of the at least one HVAC device stored in a database, wherein the one or more current temperature values are received within a predefined period of time measured from a time of receiving the request.

In one or more embodiments, the method further includes receiving, over a predefined period of time, the one or more current temperature values measured at the respective second location of each of the at least one HVAC device, wherein the at least one HVAC device comprises a first HVAC device and a second HVAC device. The method further includes correlating the one or more current temperature values measured at respective second locations of the first HVAC device and the second HVAC device with respect to a predefined threshold value. The method further includes determining that the outside air temperature sensor associated with one of the first HVAC device and the second HVAC device is malfunctioning based on the output of the correlating step.

In one or more embodiments, the method further includes discarding the one or more current temperature values measured by the outside air temperature sensor of the HVAC device that is determined to be malfunctioning, wherein the estimating step comprises estimating the outside air temperature for the first device based on the one or more current temperature values measured at the second location of the HVAC device other than the HVAC device that is determined to be malfunctioning.

In one or more embodiments, identifying the at least one HVAC device comprises identifying two or more HVAC devices within the predefined distance of the first device. Also, obtaining the one or more current temperature values comprises obtaining two or more current temperature values associated with each identified HVAC device. The method further includes determining a weighted average of the obtained two or more current temperature values by applying a weight on the obtained two or more current temperature values associated with each identified HVAC device based on a distance of each identified HVAC device from the first device and calculating an average of the weighted temperature values. The method further includes estimating the outside air temperature for the first device based on the calculated weighted average.

In one or more embodiments, identifying the at least one HVAC device comprises identifying two or more HVAC devices within the predefined distance of the first device. Also, obtaining the one or more current temperature values, comprises obtaining two or more current temperature values associated with each identified HVAC device. The method further includes identifying a characteristic of each identified HVAC device and the first device by applying a machine learning (ML) model. The method further includes determining a weighted average of the obtained two or more current temperature values by assigning a weight to the obtained two or more current temperature values associated with each identified HVAC device based on the identified characteristic and determining the average of the weighted temperature values. The method further includes estimating the outside air temperature for the first device based on the determined weighted average.

In one or more embodiments, the method further includes determining whether at least one HVAC device is located within the predefined threshold distance from the first device, wherein each of the at least one HVAC device is equipped with an outside air temperature sensor. The method further includes obtaining one or more current temperature values from at least one non-HVAC device in response to determining that no HVAC device is located within the predefined threshold distance from the first device. The method further includes estimating the outside air temperature for the first device based on the one or more current temperature values measured at the non-HVAC device.

In one or more embodiments, identifying the at least one HVAC device comprises identifying two or more HVAC devices within the predefined distance of the first device. Also, obtaining the one or more current temperature values comprises obtaining two or more current temperature values associated with each identified HVAC device. The method further includes determining whether a user input is received to prioritize at least one HVAC device from the two or more identified HVAC devices to estimate the outside temperature for the first device. The method further includes determining a weighted average of the obtained two or more current temperature values by assigning a weight to the obtained two or more current temperature values associated with each identified HVAC device, wherein a weight assigned to the prioritized HVAC device is higher than another weight assigned to non-prioritized HVAC identified devices. The method further includes estimating the outside air temperature for the first device based on the determined weighted average.

In one or more embodiments, the method further includes receiving the user input at the first device to configure a temperature offset at the first location of the first device. The method further includes obtaining the one or more current temperature values associated with the at least one HVAC device. The method further includes estimating the outside air temperature for the first device based on the one or more obtained current temperature values and the temperature offset.

Also disclosed herein is a system of estimating the outside air temperature for the first device. The system may include an OAT module coupled with a processor, a memory, and a communicator. The OAT module may include a receiving module, an HVAC identifier, an obtaining module, and an estimating module. The receiving module may be configured to receive, from the first device, the request to obtain the estimate of outside air temperature at the first location of the first device. The HVAC identifier may be configured to identify the at least one HVAC device disposed within the predefined threshold distance from the first device, wherein each of the at least one HVAC device is equipped with the outside air temperature sensor. The obtaining module may be configured to obtain the one or more current temperature values associated with the at least one HVAC device, wherein each of the one or more current temperature values, associated with the respective HVAC device of the at least one HVAC device, is measured at the respective second location of the corresponding HVAC device. The estimating module may be configured to estimate the outside air temperature for the first device based on the one or more current temperature values measured at the respective second location of each of the at least one HVAC device.

In one or more embodiments, the system further comprises a transmitting module, which may be configured to transmit the estimated outside air temperature to the first device, wherein the estimated outside air temperature corresponds to a real-time temperature of the first location associated with the first device.

In one or more embodiments, wherein to obtain the current temperature, the obtaining module may be further configured to transmit, in real-time, a request to one or more of the at least one HVAC device to obtain a corresponding current temperature value of the one or more current temperature values. The obtaining module may be further configured to receive the corresponding current temperature value from the one or more HVAC devices based on a real-time measurement of temperature using a respective outside air temperature sensor associated with each of the one or more HVAC devices.

In one or more embodiments, wherein to obtain the one or more current temperature values, the obtaining module may be further configured to obtain the one or more current temperature values of the at least one HVAC device stored in a database, wherein the one or more current temperature values are received within a predefined period of time measured from a time of receiving the request.

In one or more embodiments, the system further comprises a processing module, which may be configured to receive, over a predefined period of time, the one or more current temperature values measured at the respective second location of each of the at least one HVAC device, wherein the at least one HVAC device comprises a first HVAC device and a second HVAC device. The processing module may be further configured to correlate the one or more current temperature values measured at respective second locations of the first HVAC device and the second HVAC device with respect to a predefined threshold value. The processing module may be further configured to determine that the outside air temperature sensor associated with one of the first HVAC device and the second HVAC device is malfunctioning based on the output of the correlation.

In one or more embodiments, the processing module may be further configured to discard the one or more current temperature values measured by the outside air temperature sensor of the HVAC device that is determined to be malfunctioning, wherein the estimation module is further configured to estimate the outside air temperature for the first device based on the one or more current temperature values measured at the second location of the HVAC device other than the HVAC device that is determined to be malfunctioning.

Also disclosed herein is a system for estimating the outside air temperature for the first device. The system may include at least one HVAC device and a server, wherein the server may be configured to receive, from the first device, the request to obtain an estimate of outside air temperature at the first location of the first device. Further, the server may be configured to identify the at least one HVAC device within the predefined threshold distance from the first device, wherein each of the at least one HVAC device is equipped with the outside air temperature sensor. Furthermore, the server may be configured to obtain one or more current temperature values measured at the second location of each of the at least one HVAC device. Furthermore, the server may be configured to estimate the outside air temperature for the first device based on the one or more current temperature values measured at the second location of each of the at least one HVAC device.

To further clarify the advantages and features of the methods, systems, and apparatuses, a more particular description of the methods, systems, and apparatuses will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered limiting of its scope. The disclosure will be described and explained with additional specificity and detail in the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

FIG. 1 illustrates a problem scenario for estimating outside air temperature (OAT) in conventional HVAC systems;

FIG. 2A is an example scenario illustrating a method for estimating an OAT value for a first device based on one or more current temperature values measured at a respective second location of each of the at least one HVAC device;

FIG. 2B illustrates a block diagram of a server for estimating the OAT value for the first device based on one or more current temperature values measured at a respective second location of each of the at least one HVAC device;

FIG. 3 is a flow diagram illustrating a method for estimating the OAT value for the first device based on the one or more current temperature values measured at the respective second location of each of the at least one HVAC device;

FIGS. 4A and 4B are sequence flow diagrams illustrating the method for estimating the OAT value for the first device based on the one or more current temperature values measured at the respective second location of each of the at least one HVAC device;

FIG. 5 is a flow diagram illustrating a method for estimating the OAT value for the first device by discarding the one or more current temperature values measured by the outside air temperature sensor of the HVAC device that has been determined to be malfunctioning;

FIG. 6 is a flow diagram illustrating a method for estimating the OAT value for the first device based on an average function when two or more HVAC devices within a predefined distance of the first device;

FIG. 7 is a flow diagram illustrating a method for estimating the OAT value for the first device based on a weighted average function when the two or more HVAC devices within the predefined distance of the first device;

FIG. 8 is a flow diagram illustrating a method for estimating the OAT value for the first device based on a weighted average function of various characteristics associated with the two or more HVAC devices within the predefined distance of the first device;

FIG. 9 is a flow diagram illustrating a method for estimating the OAT value for the first device using at least one non-HVAC device when no HVAC device is located within the predefined threshold distance from the first device;

FIG. 10 is a flow diagram illustrating a method for estimating the OAT value for the first device based on a user input when the two or more HVAC devices are within the predefined distance of the first device; and

FIG. 11 is a flow diagram illustrating a method for estimating the OAT value for the first device based on a temperature offset configuration at a first location of the first device.

Further, skilled artisans will appreciate those elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION OF FIGURES

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the disclosure relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the disclosure and are not intended to be restrictive thereof.

Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. Thus, appearances of the phrase “in an embodiment”, “in one embodiment”, “In one or more embodiments”, and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

The terms “comprise”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises . . . a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. The term “or” as used herein, refers to non-exclusive alternative embodiments or features in a list of such embodiments or features, unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

As is traditional in the field, embodiments may be described and illustrated in terms of blocks that carry out a described function or functions. These blocks, which may be referred to herein as units or modules or the like, are physically implemented by analog or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware and software. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like. The circuits constituting a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the disclosure. Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the disclosure.

The accompanying drawings are used to help easily understand various technical features and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the disclosure should be construed to extend to any alterations, equivalents, and substitutes in addition to those which are particularly set out in the accompanying drawings. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are generally only used to distinguish one element from another.

Referring now to the drawings, and more particularly to FIGS. 2A to 11, where similar reference characters denote corresponding features consistently throughout the figures, there are shown one or more embodiments.

FIG. 2A is an example scenario illustrating the method for estimating the OAT value for a first device 200A based on one or more current temperature values measured at a respective second location of each of at least one HVAC device (e.g., 200B, 200C, 200D . . . 200N, etc.).

In the example scenario, to maintain desired temperatures at location 1, the first device 200A sends the request to a server 100 to obtain the estimate of outside air temperature at a first location (i.e., Location-1) of the first device 200A, where the first device 200A is the HVAC device without the outside air temperature sensor. Upon receiving the request, the server 100 may identify the at least one HVAC device (i.e., 200B and 200C) within a predefined threshold distance (e.g., 2 miles) from the first device 200A, wherein each of the at least one HVAC device (e.g., 200B, 200C, etc.) is equipped with an outside air temperature sensor. The server 100 does not obtain the current temperature value from another system's Location-4 200D because the Location-4 is too far away (e.g., 10 miles) from the first device 200A. Then, the server 100 obtains the one or more current temperature values associated with the at least one HVAC device (i.e., 200B and 200C), wherein each of the one or more current temperature values, associated with the respective HVAC device of the at least one HVAC device (i.e., 200B and 200C), is measured at the respective second location (i.e., Location-2 and Location-3) of the corresponding HVAC device. Then, the server 100 estimates the outside air temperature for the first device 200A based on the one or more current temperature values measured at the respective second location of each of the at least one HVAC device (i.e., 200B and 200C). Then, the server 100 transmits the estimated outside air temperature to the first device 200A, wherein the estimated outside air temperature corresponds to the real-time temperature of the first location (Location-1) associated with the first device 200A and/or optimizes the network associated with the first device 200A, based on, for example, a nearby HVAC device (i.e., 200C). As a result, the first device 200A sets the desired temperature as “Temperature level-1” at the Location-1, resulting in accuracy in specific air temperatures and a pleasant user experience at the Location-1.

FIG. 2B illustrates a block diagram of the server 100 for estimating OAT value for the first device 200A (not shown in FIG. 2B) based on one or more current temperature values measured at a respective second location of each of one or more HVAC devices (e.g., 200B, 200C, . . . 200N). Examples of the server 100 may include, but are not limited to, a web server, a virtual machine (VM) server, an application server, a database server, and a cloud server. Examples of the first device 200A may include, but are not limited to, an HVAC device, a thermostat, a smartphone, a tablet computer, a personal digital assistance (PDA), an internet of things (IoT) device, and a wearable device.

In an embodiment, the server 100 comprises a system 101. The system 101 may include a memory 110, a processor 120, a communicator 130, and an OAT module 140.

In an embodiment, the memory 110 may store instructions to be executed by the processor 120 to estimate OAT for the first device 200A, as discussed throughout the disclosure. The memory 110 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory 110 may, in some examples, be considered a non-transitory storage medium. The term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transitory” should not be interpreted that the memory 110 is non-movable. In some examples, the memory 110 can be configured to store larger amounts of information than the memory. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). The memory 110 can be an internal storage unit, or it can be an external storage unit of the server 100, a cloud storage, or any other type of external storage.

The processor 120 may communicate with the memory 110, the communicator 130, and the OAT module 140. The processor 120 may be configured to execute instructions stored in the memory 110 and to perform various processes for estimating the OAT for the first device 200A, as discussed throughout the disclosure. The processor 120 may include one or a plurality of processors, may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an Artificial intelligence (AI) dedicated processor such as a neural processing unit (NPU).

The communicator 130 may be configured for communicating internally between internal hardware components and with external devices (e.g., HVAC device 200B, first device 200A, etc.) via one or more networks (e.g., Radio technology, etc.). The communicator 130 may include an electronic circuit specific to a standard that enables wired or wireless communication.

The OAT module 140 may be implemented by processing circuitry such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like.

In an embodiment, the OAT module 140 may include a receiving module 141, an HVAC identifier 142, an obtaining module 143, an estimating module 144, a transmitting module 145, and a processing module 146.

The receiving module 141 may receive a request from the communicator 130 to obtain an estimate of outside air temperature (OAT) at a first location of the first device 200A. The request may include, for example, “please provide the current temperature at the following coordinates, latitude (23.0225xx) and longitude (72.571xxx)”. In another example, the request may be encoded in any form to be understood by the server as a trigger to obtain temperature at the first location of the first device. The first device 200A is a heating, ventilation, and air conditioning (HVAC) device without an outside air temperature sensor (non-OAT sensor).

Upon receiving the request to obtain the estimate of the OAT at the first location of the first device 200A, the HVAC identifier 142 may identify at least one HVAC device (e.g., 200B and 200C) within a predefined threshold distance (e.g., 2 miles) from the first device 200A, wherein each of the at least one HVAC device (e.g., 200B and 200C) is equipped with an outside air temperature sensor (OAT sensor). Examples of the OAT sensor may include, but are not limited to, a thermocouple, a resistance temperature detector (RTD), a thermistor, and a semiconductor based integrated circuits (IC).

Upon identifying the at least one HVAC device (e.g., 200B and 200C) within the predefined threshold distance from the first device 200A, the obtaining module 143 may transmit, in real-time, a request to one or more of the at least one HVAC device (e.g., 200B and 200C) to obtain a corresponding current temperature value of the one or more current temperature values (e.g., 36.1 Celsius (C), 37.2 C, etc.) associated with the at least one HVAC device (e.g., 200B and 200C), wherein each of the one or more current temperature values, associated with a respective HVAC device of the at least one HVAC device (e.g., 200B and 200C), is measured at a respective second location of the corresponding HVAC device (e.g., 200B and 200C). Furthermore, the obtaining module 143 may receive the corresponding current temperature value from the one or more HVAC devices (e.g., 200B and 200C) based on a real-time measurement of temperature using a respective OAT sensor associated with each of the one or more HVAC devices (e.g., 200B and 200C). In one embodiment, the obtaining module 143 may obtain the one or more current temperature values of the at least one HVAC device (e.g., 200B and 200C) stored in a database of the server 100, wherein the one or more current temperature values are received within a predefined period of time measured (e.g., 1 minute) from a time of receiving the request.

Upon receiving a response to the request from the one or more of the at least one HVAC device (e.g., 200B and 200C), the estimating module 144 may estimate the OAT for the first device 200A based on the one or more current temperature values measured at the respective second location of each of the at least one HVAC device (e.g., 200B and 200C) by utilizing the obtaining module 143 and an artificial intelligence (AI) module (not shown) of the server 100. The transmitting module 145 may transmit the estimated OAT to the first device 200A, wherein the estimated OAT corresponds to a real-time temperature of the first location associated with the first device 200A.

In an embodiment, upon transmitting, in real-time, the request to one or more of the at least one HVAC device (e.g., 200B and 200C) to obtain the corresponding current temperature value, the processing module 146 may receive, over a predefined period of time (e.g., 2 minutes), the one or more current temperature values measured at the respective second location of each of the at least one HVAC device (e.g., 200B and 200C), wherein the at least one HVAC device (e.g., 200B and 200C) comprises a first HVAC device (e.g., 200B) and a second HVAC device (e.g., 200C). The processing module 146 may correlate the one or more current temperature values measured at respective second locations of the first HVAC device (e.g., 200B) and the second HVAC device (e.g., 200C) with respect to a predefined threshold value. The processing module 146 may determine that the OAT sensor associated with one of the first HVAC device (e.g., 200B) and the second HVAC device (e.g., 200C) is malfunctioning based on the correlation. For example, the server 100 may receive one or more current temperature values associated with two HVAC devices (e.g., 200B and 200C) for one hour, where the two HVAC devices (e.g., 200B and 200C) are close to each other. After one hour, the server 100 may apply the ML model to one or more received current temperature values and notice that one of two HVAC devices (e.g., 200B and 200C) has more fluctuation in the one or more received current temperature values in every 5-minute duration. Based on the observed fluctuation, the server 100 determines that one of the HVAC devices (e.g., 200C) has a malfunctioning issue, such as the OAT sensor of one of the HVAC devices (e.g., 200C) not functioning properly.

Further, the processing module 146 may discard the one or more current temperature values measured by the OAT sensor of the HVAC device (e.g., 200C) that is determined to be malfunctioning. The estimation of the OAT for the first device 200A is based on the one or more current temperature values measured at the second location of the HVAC device (e.g., 200B) other than the HVAC device (e.g., 200C) that is determined to be malfunctioning.

In one or more embodiments, the HVAC identifier 142 may identify the two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, as illustrated in FIG. 2A. Upon identifying the two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, the obtaining module 143 may obtain the two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C). Then, the estimating module 144 may determine a weighted average of the obtained two or more current temperature values by applying a weight on the obtained two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C) based on a distance of each identified HVAC device (e.g., 200B and 200C) from the first device 200A and calculate an average of the weighted temperature values, and estimates the OAT for the first device 200A based on the calculated weighted average. For example, the server 100 may detect two HVAC devices (200B and 200C, for example) within the predefined distance of the first device 200A. Whereas the first HVAC device 200B is 50 meters away from the first device 200A and the second HVAC device 200C is 500 meters away from the first device 200A. In this scenario, the server 100 may receive one or more current temperature values associated with the first HVAC device 200B, nearby HVAC device, for one hour. After one hour, the server 100 may apply the ML model to one or more received current temperature values to determine an average value of the one or more received current temperature values. Based on the average value, the server 100 estimates the OAT for the first device 200A.

In one or more embodiments, the HVAC identifier 142 may identify two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, as illustrated in FIG. 2A. Upon identifying two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, the obtaining module 143 may obtain two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C). Then, the receiving module 141 may identify a characteristic (e.g., sun shade) of each identified HVAC device (e.g., 200B and 200C) and the first device 200A by applying a machine learning (ML) model. Such characteristics may be pre-stored at the server at the time of installation of the HVAC devices. Alternatively, such characteristics may be provided by a user associated with each HVAC device. Then, the estimating module 144 may determine a weighted average of the obtained two or more current temperature values by assigning a weight to the obtained two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C) based on the identified characteristic and determine the average of the weighted temperature values, and estimates the OAT for the first device 200A based on the determined weighted average. For example, the ML model is used to identify one or more HVAC devices (e.g., 200B and 200C) that are in the sun or shade by comparing temperatures on sunny days(s) to temperatures on cloudy days(s). The determination of cloudy days may be provided, at the server, through one or more third party systems, such as a weather station. One or more HVAC devices (e.g., 200A) located in the sun may use one or more temperature values from nearby one or more HVAC devices (e.g., 200B) located in the sun. One or more HVAC devices (e.g., 200A) located in the shade will use one or more temperature values from nearby one or more HVAC devices (e.g., 200C) located in the shade. If one or more HVAC devices (e.g., 200A) requesting a temperature are located in the sun, then nearby one or more HVAC devices (e.g., 200B) also located in the sun are more heavily weighted in the weighted average.

In one or more embodiments, the HVAC identifier 142 may determine whether the at least one HVAC device (e.g., 200B and 200C) is located within the predefined threshold distance from the first device 200A, wherein each of the at least one HVAC device (e.g., 200B and 200C) is equipped with the OAT sensor. In an embodiment, the obtaining module 143 may obtain one or more current temperature values from at least one non-HVAC device (e.g., weather station) in response to determining that no HVAC device (e.g., 200B and 200C) is located within the predefined threshold distance from the first device 200A. Specifically, the estimating module 144 may estimate the OAT for the first device 200A based on the one or more current temperature values measured at the non-HVAC device.

In one or more embodiments, the HVAC identifier 142 may identify the two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, as illustrated in FIG. 2A. Upon identifying the two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, the obtaining module 143 may obtain the two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C). Then, the receiving module 141 may determine whether a user input is received to prioritize at least one HVAC device (e.g., 200B) from the two or more identified HVAC devices (e.g., 200B and 200C) to estimate the outside temperature for the first device 200A. Then, the estimating module 144 may determine a weighted average of the obtained two or more current temperature values by assigning a weight to the obtained two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C), wherein a weight assigned to the prioritized HVAC device (e.g., 200B) is higher than another weight assigned to non-prioritized HVAC identified devices. Upon determining the weighted average, the estimating module 144 may estimate the OAT for the first device 200A based on the determined weighted average. For example, the server 100 may detect two HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A. The server 100 may receive one or more current temperature values associated with two detected HVAC devices (e.g., 200B and 200C). Further, the server 100 may receive priority information/feedback/user's priority/history of selection associated with the two HVAC devices (e.g., 200B and 200C) from the user of the first device 200A. Based on the received priority information/feedback/user's priority/history of selection, the server may apply the ML model on the received one or more current temperature values and detect that the first HVAC device 200B has a priority “0.7” and the second HVAC device 200B has a priority “0.5”. As a result, the server 100 may estimate the OAT for the first device 200A based on the second HVAC device 200B.

In one or more embodiments, outlier temperatures that differ by more than a percentage point can be excluded from the averaging.

In one or more embodiments, the receiving module 141 may receive the user input at the first device 200A to configure a temperature offset at the first location of the first device 200A. Upon receiving the user input, the obtaining module 143 may obtain the one or more current temperature values associated with the at least one HVAC device (e.g., 200B and 200C). Then, the estimating module 144 may estimate the OAT for the first device 200A based on the one or more obtained current temperature values and the temperature offset. For example, if a thermostat is set to turn on at 72° (in Fahrenheit mode) in heat mode at the first location and has a temperature offset of 2, the first device 200A may activate a furnace when the room temperature reaches 71° and shut it down when it reaches 73°. For cooling at 72° with the temperature offset of 2, an air conditioner will activate at 73° and shut down at 71°.

A function associated with various hardware components of the server 100 may be implemented by the ML model/AI engine (not shown), the AI engine may be implemented through the non-volatile memory, the volatile memory, and the processor 120. One or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or AI model stored in the non-volatile memory and the volatile memory. Training or learning is used to provide the predefined operating rule or AI model. Here, being provided through learning means that a predefined operating rule or AI model of the desired characteristic is created by applying a learning algorithm to a set of learning data. The learning may be performed in a device itself in which AI according to an embodiment is performed, and/or may be implemented through a separate server/system. The learning algorithm is a method for training a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device (e.g., the first device 200A) to decide or predict the authenticity of the user. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

The AI engine may consist of a plurality of neural network layers. Each layer has a plurality of weight values and performs a layer operation through a calculation of a previous layer and an operation of a plurality of weights. Examples of neural networks include, but are not limited to, convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), restricted Boltzmann Machine (RBM), deep belief network (DBN), bidirectional recurrent deep neural network (BRDNN), generative adversarial networks (GAN), and deep Q-networks.

Although FIG. 2B shows various hardware components of the server 100, but it is to be understood that other embodiments are not limited thereon. In other embodiments, the server 100 may include any number of components. Further, the labels or names of the components are used only for illustrative purpose and does not limit the scope of the invention. One or more components can be combined to perform the same or substantially similar functions to estimate the OAT for the first device 200A.

One embodiment comprises a thermostat connected to the at least one HVAC device (e.g., 200B), a local network connecting the thermostat to a larger network such as the Internet, one or more additional thermostats connected to the network, and other HVAC device (e.g., 200C), and the server 100 in bi-directional communication with the thermostats. In one embodiment, the server 100 may record the one or more current temperature values sensed by each thermostat versus time, as well as the signals sent by the thermostats to the at least one HVAC device (e.g., 200B and 200C) to which they are connected. In one embodiment, the server 100 may also collect OAT and humidity data from the respective second location of each of the at least one HVAC device (e.g., 200B and 200C) served by the connected at least one HVAC device (e.g., 200A).

In one embodiment, the at least one HVAC device (e.g., 200B and 200C) with the OAT sensor may regularly measure the one or more current temperature values at the respective second location and send the regularly measure one or more current temperature values to the server 100.

FIG. 3 is a flow diagram illustrating a method 300 for estimating the OAT value for the first device 200A based on the one or more current temperature values measured at the respective second location of each of the at least one HVAC device (e.g., 200B and 200C). Steps (301 to 304) are performed by the server 100 to estimate the OAT for the first device 200A.

At step 301, the method 300 includes receiving the request from the first device 200A to obtain the estimate of outside air temperature at the first location of the first device 200A. In one embodiment, the first device 200A is the HVAC device without the OAT sensor. In one embodiment, the OAT sensor is the thermistor.

At step 302, the method 300 includes identifying the at least one HVAC device (e.g., 200B and 200C) disposed within the predefined threshold distance from the first device 200A, where each of the at least one HVAC device (e.g., 200B and 200C) is equipped with the OAT sensor. In one embodiment, the method 300 includes identifying the at least one HVAC device comprising identifying two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A. In one embodiment, the method 300 includes determining whether at least one HVAC device (e.g., 200B and 200C) is located within the predefined threshold distance from the first device 200A, wherein each of the at least one HVAC device (e.g., 200B and 200C) is equipped with the outside air temperature sensor.

At step 303, the method 300 includes obtaining one or more current temperature values associated with the at least one HVAC device (e.g., 200B and 200C), wherein each of the one or more current temperature values, associated with the respective HVAC device of the at least one HVAC device (e.g., 200B and 200C), is measured at the respective second location of the corresponding HVAC device. In one embodiment, the method 300 includes transmitting, in real-time, the request to one or more of the at least one HVAC device (e.g., 200B and 200C) to obtain a corresponding current temperature value of the one or more current temperature values. In one embodiment, the method 300 includes receiving the corresponding current temperature value from the one or more HVAC devices (e.g., 200B and 200C) based on the real-time measurement of temperature using the respective OAT sensor associated with each of the one or more HVAC devices (e.g., 200B and 200C). In one embodiment, the method 300 includes obtaining the one or more current temperature values of the at least one HVAC device (e.g., 200B and 200C) stored in the database, wherein the one or more current temperature values are received within the predefined period of time measured from the time of receiving the request. In one embodiment, the method 300 includes obtaining the one or more current temperature values, which comprises obtaining two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C). In one embodiment, the method 300 includes obtaining one or more current temperature values from at least one non-HVAC device (e.g., 200B and 200C) in response to determining that no HVAC device is located within the predefined threshold distance from the first device 200A.

At step 304, the method 300 includes estimating the outside air temperature for the first device 200A based on the one or more current temperature values measured at the respective second location of each of the at least one HVAC device (e.g., 200B and 200C). In one embodiment, the method 300 further includes transmitting the estimated outside air temperature to the first device 200A, wherein the estimated outside air temperature corresponds to a real-time temperature of the first location associated with the first device 200A. In one embodiment, the method 300 further includes receiving, over the predefined period of time, the one or more current temperature values measured at the respective second location of each of the at least one HVAC device (e.g., 200B and 200C), wherein the at least one HVAC device (e.g., 200B and 200C) comprises the first HVAC device (e.g., 200B) and the second HVAC device (e.g., 200C). In one embodiment, the method 300 further includes correlating the one or more current temperature values measured at respective second locations of the first HVAC device (e.g., 200B) and the second HVAC device (e.g., 200C) with respect to the predefined threshold value. In one embodiment, the method 300 further includes determining that the OAT sensor associated with one of the first HVAC device (e.g., 200B) and the second HVAC device (e.g., 200C) is malfunctioning based on the output of the correlating step. In one embodiment, the method 300 further includes discarding the one or more current temperature values measured by the outside air temperature sensor of the HVAC device (e.g., 200C) that is determined to be malfunctioning. Wherein the estimating step comprises estimating the OAT for the first device based on the one or more current temperature values measured at the second location of the HVAC device (e.g., 200B) other than the HVAC device (e.g., 200C) that is determined to be malfunctioning.

In one embodiment, the method 300 includes determining an average of the obtained two or more current temperature values and estimating the outside air temperature for the first device 200A based on the determined average.

In one embodiment, the method 300 includes determining a weighted average of the obtained two or more current temperature values by applying a weight on the obtained two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C) based on a distance of each identified HVAC device (e.g., 200B and 200C) from the first device 200A and calculating the average of the weighted temperature values, and estimating the outside air temperature for the first device 200A based on the calculated weighted average.

In one embodiment, the method 300 includes identifying a characteristic of each identified HVAC device (e.g., 200B and 200C) and the first device 200A by applying a machine learning (ML) model, determining a weighted average of the obtained two or more current temperature values by assigning a weight to the obtained two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C) based on the identified characteristic and determining the average of the weighted temperature values, and estimating the outside air temperature for the first device 200A based on the determined weighted average.

In one embodiment, the method 300 includes estimating the outside air temperature for the first device 200A based on the one or more current temperature values measured at the non-HVAC device.

In one embodiment, the method 300 includes determining whether the user input is received to prioritize at least one HVAC device (e.g., 200B and 200C) from the two or more identified HVAC devices to estimate the outside temperature for the first device 200A, determining a weighted average of the obtained two or more current temperature values by assigning a weight to the obtained two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C), wherein a weight assigned to the prioritized HVAC device (e.g., 200B) is higher than another weight assigned to non-prioritized HVAC identified devices (e.g., 200C), and estimating the outside air temperature for the first device based on the determined weighted average.

In one embodiment, the method 300 includes receiving a user input at the first device 200A to configure a temperature offset at the first location of the first device 200A, obtaining the one or more current temperature values associated with the at least one HVAC device (e.g., 200B and 200C), and estimating the outside air temperature for the first device 200A based on the one or more obtained current temperature values and the temperature offset.

FIGS. 4A and 4B are sequence flow diagrams illustrating the method 400 for estimating the OAT value for the first device 200A based on the one or more current temperature values measured at the respective second location of each of the at least one HVAC device (e.g., 200B and 200C). A system 4000 includes the first device 200A, the server 100, the HVAC device(s) (i.e., 200B, 200C, 200N, etc.).

At step 401, the first device 200A may send the request to the server 100 to obtain the estimate of the OAT value at the first location of the first device 200A, which relates to step 301 of FIG. 3. At step 402A, the server 100 may identify the HVAC device(s) (i.e., 200B and 200C) within the predefined threshold distance from the first device 200A, wherein each of the HVAC device(s) (i.e., 200B and 200C) is equipped with the OAT sensor, which relates to step 302 of FIG. 3. At step 402B, the server 100 may identify the HVAC device(s) (i.e., 200N) as far away from the first device 200A or equip with a non-OAT sensor, which relates to step 302 of FIG. 3. At step 403A, the server 100 may send the request to the HVAC device 200B to obtain the current temperature value, which relates to step 303 of FIG. 3. At step 403B, the server 100 may send the request to the HVAC device 200C to obtain the current temperature value, which relates to step 303 of FIG. 3.

At step 404A, the HVAC device 200B may send the current temperature value in response to receive the request from the server 100, which relates to step 303 of FIG. 3. At step 404B, the HVAC device 200C may send the current temperature value in response to receive the request from the server 100, which relates to step 303 of FIG. 3. At step 405, the server 100 may store the obtained one or more current temperature values of the at least one HVAC device (i.e., 200B and 200C) in the database of the server 100, which relates to step 303 of FIG. 3. At step 406, the server 100 may utilize the AI engine to determine an optimal/most suitable current temperature value for the first device 200A from the obtained one or more current temperature values of the at least one HVAC device (i.e., 200B and 200C), which relates to step 304 of FIG. 3. At step 407, the server 100 may send the optimal/most suitable current temperature value to the first device 200A, which relates to step 304 of FIG. 3. At steps 408-409, the first device 200A displays the optimal/most suitable current temperature value and optimizes the network associated with the first device 200A based on the optimal/most suitable current temperature value, which relates to step 304 of FIG. 3.

FIG. 5 is a flow diagram illustrating a method 500 for estimating the OAT value for the first device 200A by discarding the one or more current temperature values measured by the outside air temperature sensor of the HVAC device that has been determined to be malfunctioning. Steps 501 to 504 are performed by the server 100 to estimate the OAT for the first device 200A. Further, the steps 501 to 504 may be performed as sub-steps of step 304 of FIG. 3 while estimating the outside air temperature for the first device.

At step 501, the method 500 includes receiving, over the predefined period of time, the one or more current temperature values measured at the respective second location of each of the at least one HVAC device (e.g., 200B, 200C, 200N, etc.), wherein the at least one HVAC device comprises the first HVAC device (e.g., 200B) and the second HVAC device (e.g., 200C).

At step 502, the method 500 includes correlating the one or more current temperature values measured at respective second locations of the first HVAC device (e.g., 200B) and the second HVAC device (e.g., 200C) with respect to the predefined threshold value.

At step 503, the method 500 includes determining that the OAT sensor associated with one of the first HVAC device (e.g., 200B) and the second HVAC device (e.g., 200C) is malfunctioning based on the output of the correlating step.

At step 504, the method 500 includes discarding the one or more current temperature values measured by the outside air temperature sensor of the HVAC device (e.g., 200C) that is determined to be malfunctioning, where the estimating step comprises estimating the OAT for the first device 200A based on the one or more current temperature values measured at the second location of the HVAC device (e.g., 200B) other than the HVAC device (e.g., 200C) that is determined to be malfunctioning.

An exemplary use case scenario considering two identified HVAC devices, i.e., device A and device B, within the predefined distance of the first HVAC device for which outside air temperature is to be estimated, is discussed hereinafter. Based on the output of the correlating step, the method 500 includes determining whether the OAT sensor associated with either device A or device B is malfunctioning. For instance, if the correlation analysis suggests that the temperature values from the device A are consistently above or below the predefined threshold value, it may indicate a malfunctioning OAT sensor associated with the device A. Further, the method 500 includes discarding the current temperature values measured by the device A's OAT sensor. Finally, the method 500 includes estimating the OAT for the first HVAC device based on the current temperature values measured at the device B (the HVAC device other than the one with the malfunctioning sensor). The estimation allows for obtaining an approximate value of the OAT for the first HVAC device, despite the malfunctioning sensor.

FIG. 6 is a flow diagram illustrating a method 600 for estimating the OAT value for the first device 200A based on an average function when two or more HVAC devices (e.g., 200B and 200C) are within the predefined distance of the first device 200A. Steps 601 to 604 are performed by the server 100 to estimate the OAT for the first device 200A.

At step 601, the method 600 includes identifying the at least one HVAC device comprises identifying two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, which relates to step 302 of FIG. 3.

At step 602, the method 600 includes obtaining the one or more current temperature values comprises obtaining two or more current temperature values associated with each identified HVAC device (i.e., 200B and 200C), which relates to step 303 of FIG. 3.

At step 603, the method 600 includes determining an average of the obtained two or more current temperature values, which relates to step 304 of FIG. 3.

At step 604, the method 600 includes estimating the OAT for the first device 200A based on the determined average, which relates to step 304 of FIG. 3.

FIG. 7 is a flow diagram illustrating a method 700 for estimating the OAT value for the first device 200A based on a weighted average function when the two or more HVAC devices (e.g., 200B and 200C) are within the predefined distance of the first device 200A. Steps 701 to 704 are performed by the server 100 to estimate the OAT for the first device 200A.

At step 701, the method 700 includes identifying the at least one HVAC device comprises identifying two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, which relates to step 302 of FIG. 3.

At step 702, the method 700 includes obtaining the one or more current temperature values comprises obtaining two or more current temperature values associated with each identified HVAC device (i.e., 200B and 200C), which relates to step 303 of FIG. 3.

At step 703, the method 700 includes determining the weighted average of the obtained two or more current temperature values by applying the weight on the obtained two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C) based on a distance of each identified HVAC device (e.g., 200B and 200C) from the first device 200A and calculating the average of the weighted temperature values, which relates to step 304 of FIG. 3.

At step 704, the method 700 includes estimating the OAT for the first device 200A based on the calculated weighted average, which relates to step 304 of FIG. 3.

FIG. 8 is a flow diagram illustrating a method 800 for estimating the OAT value for the first device 200A based on a weighted average function of various characteristics associated with the two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A. Steps 801 to 805 are performed by the server 100 to estimate the OAT for the first device 200A.

At step 801, the method 800 includes identifying the at least one HVAC device comprises identifying two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, which relates to step 302 of FIG. 3.

At step 802, the method 800 includes obtaining the one or more current temperature values comprises obtaining two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C), which relates to step 303 of FIG. 3.

At step 803, the method 800 includes identifying the characteristic of each identified HVAC device (e.g., 200B and 200C) and the first device 200A by applying a machine learning (ML) model, which relates to step 304 of FIG. 3.

At step 804, the method 800 includes determining a weighted average of the obtained two or more current temperature values by assigning the weight to the obtained two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C) based on the identified characteristic and determining the average of the weighted temperature values, which relates to step 304 of FIG. 3.

At step 805, the method 800 includes estimating the OAT for the first device 200A based on the determined weighted average, which relates to step 304 of FIG. 3.

An exemplary use case scenario considering two identified HVAC devices, i.e., device A and device B, within the predefined distance of the first HVAC device for which outside air temperature is to be estimated, is discussed hereinafter. The method 800 includes obtaining one or more current temperature values associated with each identified HVAC device, i.e., the device A and the device B. The method 800 then includes identifying the characteristic of each identified HVAC device and the first HVAC device by applying the ML model. These characteristics could be features like whether the HVAC devices are in the sun or shade. The ML model is used to analyze the temperatures on sunny days and compare them to temperatures on cloudy days to identify the characteristics of the HVAC devices. The characteristics may be pre-stored on the server during installation or provided by the user associated with each HVAC device. Then, the method 800 includes determining the weighted average of the obtained temperature values. The weights assigned to the temperature values associated with each identified HVAC device are based on the identified characteristics. For example, if the first HVAC device requesting the temperature is located in the sun, the nearby identified HVAC devices also located in the sun will be more heavily weighted than the identified HVAC devices located in the shade, in the weighted average calculation. The same applies to the scenario when the first HVAC device is located in the shade, then the nearby identified HVAC devices in the shade will have a higher weight. Finally, the method 800 includes estimating the OAT for the first HVAC device based on the determined weighted average of the temperature values of the identified HVAC devices.

FIG. 9 is a flow diagram illustrating a method 900 for estimating the OAT value for the first device 200A using at least one non-HVAC device when no HVAC device (e.g., 200N) is located within the predefined threshold distance from the first device 200A. Steps 901 to 903 are performed by the server 100 to estimate the OAT for the first device 200A.

At step 901, the method 900 includes determining whether at least one HVAC device (e.g., 200B and 200C) is located within the predefined threshold distance from the first device 200A, wherein each of the at least one HVAC device (e.g., 200B and 200C) is equipped with the OAT sensor, which relates to step 302 of FIG. 3.

At step 902, the method 900 includes obtaining one or more current temperature values from at least one non-HVAC device in response to determining that no HVAC device (e.g., 200B, 200C . . . 200N) is located within the predefined threshold distance from the first device 200A, which relates to step 303 of FIG. 3.

At step 903, the method 900 includes estimating the OAT for the first device 200A based on the one or more current temperature values measured at the non-HVAC device (e.g., whether station), which relates to step 304 of FIG. 3.

An exemplary use case scenario considering no HVAC devices being identified within the predefined distance of the first HVAC device for which outside air temperature is to be estimated, is discussed hereinafter. In this example, the method 900 obtains one or more current temperature values from the non-HVAC device(s), such as weather stations. The disclosed method 900 provides an effective means to estimate the OAT for the first HVAC device 200A, even in cases where no HVAC device is available within the predefined threshold distance.

FIG. 10 is a flow diagram illustrating a method 1000 for estimating the OAT value for the first device 200A based on the user input when the two or more HVAC devices (e.g., 200B and 200C) are within the predefined distance of the first device 200A. Steps 1001 to 1005 are performed by the server 100 to estimate the OAT for the first device 200A.

At step 1001, the method 1000 includes identifying the at least one HVAC device comprises identifying two or more HVAC devices (e.g., 200B and 200C) within the predefined distance of the first device 200A, which relates to step 302 of FIG. 3.

At step 1002, the method 1000 includes obtaining the one or more current temperature values comprises obtaining two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C), which relates to step 303 of FIG. 3.

At step 1003, the method 1000 includes determining whether the user input is received to prioritize at least one HVAC device from the two or more identified HVAC devices (e.g., 200B and 200C) to estimate the outside temperature for the first device 200A, which relates to step 304 of FIG. 3.

At step 1004, the method 1000 includes determining a weighted average of the obtained two or more current temperature values by assigning a weight to the obtained two or more current temperature values associated with each identified HVAC device (e.g., 200B and 200C), wherein a weight assigned to the prioritized HVAC device (e.g., 200B) is higher than another weight assigned to non-prioritized HVAC identified devices, which relates to step 304 of FIG. 3.

At step 1005, the method 1000 includes estimating the OAT for the first device 200A based on the determined weighted average, which relates to step 304 of FIG. 3.

FIG. 11 is a flow diagram illustrating a method 1100 for estimating the OAT value for the first device 200A based on a temperature offset configuration at the first location of the first device 200A. Steps 1101 to 1103 are performed by the server 100 to estimate the OAT for the first device 200A.

At step 1101, the method 1100 includes receiving the user input at the first device 200A to configure a temperature offset at the first location of the first device 200A, which relates to step 301 of FIG. 3.

At step 1102, the method 1100 includes obtaining the one or more current temperature values associated with the at least one HVAC device (e.g., 200B and 200C), which relates to step 303 of FIG. 3.

At step 1103, the method 1100 includes estimating the OAT for the first device 200A based on the one or more obtained current temperature values and the temperature offset, which relates to step 304 of FIG. 3.

For example, the user may interact with a thermostat, and provide input to configure the temperature offset +2 degrees Fahrenheit on the thermostat. The method 1100 then includes receiving the user input and recording the configured temperature offset. The method 1100 then collects current temperature values associated with at least one HVAC device. For instance, there may be two HVAC devices nearby, device A and device B. The method 1100 includes obtaining the current temperature values from both devices. The device A may report a current temperature, for example, 70 degrees Fahrenheit. The device B may report a current temperature, for example, 72 degrees Fahrenheit. The method 1100 may include combining the obtained current temperature values with the configured temperature offset to estimate the OAT for the first device. For device A, based on the offset of +2 degrees Fahrenheit, an adjusted temperature value of 72 degrees Fahrenheit may be obtained. Similarly, for device B, based on the offset of +2 degrees Fahrenheit, an adjusted temperature value of 74 degrees Fahrenheit may be obtained. The method 1100 then includes estimating the OAT for the first device based on the adjusted temperature values. In this example, the estimated OAT for the first device may be the average of the adjusted temperatures: (72 degrees Fahrenheit+74 degrees Fahrenheit)/2=73 degrees Fahrenheit.

The various actions, acts, blocks, steps, or the like in the flow/sequence diagrams may be performed in the order presented, in a different order, or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.

Unlike existing methods and systems, the disclosed method 300 and/or the system 101 enables the first device 200A (without the OAT sensor) to accurately estimate the outside air temperature by the server 100, whereas existing methods and systems obtain local weather data from other systems (e.g., available online weather forecast agencies, a nearby weather station, a Zip code referenced, a cloud-based system of dedicated weather station, etc.). By removing/not including the OAT sensor and utilizing the above-mentioned quality of the first device 200A, there will be cost savings for the first device 200A. In addition, the disclosed method 300/system 101 monetizes the estimated outside air temperature and/or one or more current temperature values measured at the respective second location.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one ordinary skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.

While specific language has been used to describe the subject matter, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method to implement the inventive concept as taught herein. The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment.

The embodiments disclosed herein can be implemented using at least one hardware device and performing network management functions to control the elements.

The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phrasecology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described herein.

METHOD AND SYSTEM FOR ESTIMATING OUTSIDE AIR TEMPERATURE

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