CN112119263A - Machine learning method for adjusting individual or common regions - Google Patents

Abstract

A method and system for adjusting an interior region is disclosed. One method comprises the following steps: retrieving environmental conditions regarding the interior region, wherein the environmental conditions include temperature, humidity, and air velocity; retrieving an outdoor environmental condition; generating a field of environmental conditions at a plurality of points within the interior region; estimating a user's garment insulation based on outdoor environmental conditions; calculating the thermal comfort level of the user to determine a predicted average vote value; and operating the heating, ventilation and air conditioning system based on the calculated thermal comfort.

Description

Machine learning method for adjusting individual or common regions

Technical Field

Exemplary embodiments relate to the field of electronic devices. In particular, the present disclosure relates to a method and system for integrating machine learning capabilities with adjustments regardless of whether those capabilities are possessed by individuals or groups.

Background

Thermal comfort in indoor locations is achieved through the use of heating, ventilation and air conditioning (HVAC) units placed throughout the indoor location. HVAC can be very expensive, accounting for up to 65% of the energy consumption of a building.

In the past, there have been many different ways of controlling thermal comfort and thus energy consumption. A very similar way of doing this is to manually control the air conditioning and heating units-switching them on and off as required, depending on whether the occupants of the building are comfortable or not. Later, a thermometer is added-if too high a temperature is sensed, the air conditioning system may be turned on, and if too low a temperature is sensed, the heating system may be turned on. It would be desirable to have a more efficient and accurate method of setting the thermal comfort level of an indoor area.

Disclosure of Invention

According to one embodiment, a method and system for conditioning an interior region is disclosed. One method comprises the following steps: retrieving environmental conditions regarding the interior region, wherein the environmental conditions include temperature, humidity, and air velocity; retrieving an outdoor environmental condition; generating a field of environmental conditions at a plurality of points within the interior region; calculating the thermal comfort level of the user to determine a predicted average vote value; and operating the heating, ventilation and air conditioning system based on the calculated thermal comfort.

In addition or as an alternative to one or more of the features described above, further embodiments may include: usage information about the interior region is retrieved.

In addition or as an alternative to the features described above, further embodiments may include: the user's garment insulation is estimated based on outdoor environmental conditions and usage information.

In addition or as an alternative to the features described above, further embodiments may include: receiving feedback from the user regarding the user's comfort level; and operating the heating, ventilation and air conditioning system based on the feedback.

In addition or as an alternative to the features described above, further embodiments may include: the feedback is stored in a thermal profile (profile) associated with the user.

In addition or as an alternative to the features described above, further embodiments may include: wherein calculating the thermal comfort level of the user comprises calculating the thermal comfort level using the thermal profile.

In addition or as an alternative to the features described above, further embodiments may include: wherein calculating the thermal comfort level of the user comprises calculating a predicted average vote value using a Fanger equation.

In addition or as an alternative to the features described above, further embodiments may include: wherein the predicted average vote value is on a continuous scale ranging from-3 to 3, wherein a value of-3 indicates a cold thermal condition and a value of 3 indicates a warm thermal condition.

In addition or as an alternative to the features described above, further embodiments may include: calculating a thermal comfort level for each of a plurality of users in the interior area; and calculating an average thermal comfort level for a plurality of users; wherein operating the heating, ventilation and air conditioning system is based on an average thermal comfort level of a plurality of users.

In addition or as an alternative to the features described above, further embodiments may include: calculating a predicted percentage of unsatisfied users based on a thermal comfort level of each of the plurality of users; wherein: operating the heating, ventilation and air conditioning system further comprises ensuring that the percentage of predicted unsatisfactory users is below a predetermined threshold.

Drawings

The following description should not be considered limiting in any way. Referring to the drawings, like elements are numbered alike:

FIG. 1 is a flow diagram illustrating operation of one or more embodiments;

FIG. 2 illustrates various equations used in one or more embodiments;

FIG. 3 is a block diagram of a computer system capable of executing one or more embodiments; and

FIG. 4 is a block diagram of an exemplary computer program product.

Detailed Description

A detailed description of one or more embodiments of the disclosed apparatus and methods is presented herein by way of illustration, and not limitation, with reference to the figures.

The term "about" is intended to include a degree of error associated with measuring a particular quantity based on equipment available at the time of filing the present application.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof.

As described above, indoor locations (such as office buildings, warehouses, residences, apartments, educational buildings, retail locations, and the like) typically have heating, ventilation, and air conditioning (HVAC) systems to help maintain the indoor location at a desired temperature. While manual controls exist to turn the HVAC system on and off, most modern HVAC systems are controlled by thermostats. Thermostats have temperature sensors at one or more locations. The thermostat may switch portions of the HVAC system on and off based on the temperature sensed at the location.

In one or more embodiments, machine learning methods and systems may be used to monitor and learn a thermal comfort level of an occupant. One or more embodiments may use voting techniques to determine the comfort level of each occupant in a group of occupants. Thereafter, the thermal profile may be updated based on the received feedback.

The thermal profile is used to determine whether the user will be comfortable under a range of interior climate conditions. In some embodiments, this may be measured using a thermal comfort algorithm. The thermal profile includes the temperature of the room. In some embodiments, the thermal profile may further include: aspects of the room such as relative humidity, air speed, and radiation (average radiation temperature); and characteristics of the user, such as the metabolic rate of the user and the clothing worn by the user. While aspects about the room may be measured using one or more of a variety of different sensors, characteristics of the user may be estimated.

Estimating the characteristics of the user may utilize a variety of information known about the user, the time of day, and the user's possible tasks. Metabolic rates have been estimated for a number of different tasks. For example, the national institutes of health have published tables providing various metabolic equivalent values for various activities, where 1 Met equals 58.15 watts per square meter of body surface. Since adults have a surface area of 1.7 square meters on average, 1 Met equates to approximately 100 watts of heat loss. For example, sleep produces 0.92 Met, food preparation produces 2.16 Met, basketball playing produces 8.00 Met, and homework produces 1.66 Met.

Based on the building layout, the estimated MET value for the user may be estimated based on activities predicted to occur in the room in which the user is located. The meeting room will have a lower MET value than the basketball hall, but will have a higher MET value than the bedroom.

The garment worn may be estimated based on the season, time of day, outdoor weather conditions, and the intended use of the interior space. For example, people wearing business formal wear garments that have a different thermal insulation than people located in exercise facilities. During cold weather, people located near the entrance of the hotel wear additional layers of clothing they wear on the outside, but people located in the hotel room often take off their coats. Garments may be classified by their insulative value. The insulation value can be measured in units of Clo, where 1 Clo equals 0.155 m²DEG C/W. For a typical piece of clothing, there is a Clo value that is readily available. The Clo value for a person will then be the Clo value for each garment they are wearing. A person wearing shorts, shirts, socks, and shoes may have a Clo value of 0.38, while a person wearing casual work wear may have a Clo value of 0.91.

As stated above, the remaining data points may be determined via a sensor. The data can then be used in an equation to determine the thermal comfort of the user. Details of exemplary equations will be set forth below. The manner in which the equations are used will be discussed in conjunction with the method 100.

With respect to FIG. 1, a method 100 is presented illustrating the operation of one or more embodiments. The method 100 is merely exemplary and is not limited to the embodiments presented herein. The method 100 may be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the procedures, processes, and/or activities of method 100 may be performed in the order presented. In other embodiments, one or more of the procedures, processes, and/or activities of method 100 may be combined, skipped, or performed in a different order. In some embodiments, the method 100 may be performed by the system 200. The method 100 details a training algorithm that may be used to determine the thermal comfort of a customer. The internal environmental conditions are retrieved from various sensors located in or near the room to be conditioned (block 102). These conditions may include temperature (e.g., of objects located in the room to be conditioned), humidity, air velocity, average radiant temperature, and so forth.

The average radiant temperature is the temperature of an imaginary black envelope that would cause the same heat loss through radiation from a person as the actual envelope. Calculating the average radiation temperature would require measuring the temperature of all surfaces in the room and the angle factor between each surface and the person. Performing such calculations would be very difficult and time consuming. Therefore, an approximation to the average radiant temperature may be used instead. For example, a sphere temperature may be determined, wherein the average radiation temperature is determined from the sphere temperature.

These may include integrated temperatures, such as operating temperature (t)₀) Equivalent temperature (t)_eq) And Effective Temperature (ET). In addition, a parameter called the operating temperature may also be calculated. The operating temperature at a given point is equal to the temperature to which the unheated manikin dummy adjusts itself. An operating temperature transducer may be used to determine the operating temperature. Such a transducer is a 160 mm long light gray ellipsoid with a diameter of 54 mm. The average surface temperature of the transducer is the operating temperature.

The outdoor environmental conditions are retrieved (block 104). The outdoor environmental conditions may be retrieved from sensors associated with the building. In some embodiments, the outdoor environmental conditions may be retrieved from an external source, such as via the internet.

Data is retrieved regarding the room to be conditioned (block 106). The data may include the dimensions of the room, objects within the room, the purpose of the room, and so forth. The objects in the room may include any object that generates heat, such as electronics, refrigerators, computers, lighting, and the like. The purpose of the room includes information about which activities are typically performed in the room. For example, a room may typically be used for exercise. Rooms may typically be used for reading and research. The room may typically be used for sleeping.

Fields of temperature, humidity, air velocity, average radiation temperature, etc. are created based on the data provided above (block 108). A field may mean that the indoor environmental conditions are calculated for a plurality of points in the room. This can be achieved in one of a number of different ways. For example, the Kriging method can be used.

Kriging is an interpolation method as follows: for this interpolation method, the interpolated values are modeled by a Gaussian process governed by a priori covariance. The basic idea of Kriging is to predict the value of a function at a given point by computing a weighted average of the known values of the function in a neighborhood of points. Kriging is used to derive a field for each indoor environment variable for each room. By knowing the values of quantities in some points in space (e.g., temperatures measured near the entrance and diagonal), we can determine the values of quantities at other points where no metric exists (e.g., in the center of a room where no thermometer exists).

An estimate is generated regarding the customer's clothing insulation (block 110). The customer's clothing may be estimated using a variety of different factors. For example, outdoor environmental conditions are an indication of how many garments a typical person may wear. However, outdoor environmental conditions may have a smaller impact depending on the location of the room and the typical use of the room. For example, a hotel lobby may have people still wear outdoor clothing. However, people can take their coats or jackets off while in a restaurant or conference room located in the same building. The garment insulation values discussed above may be estimated during this block.

The thermal comfort of the user is calculated (block 112). The fields of temperature (and other conditions related to thermal comfort, such as humidity and air velocity) at multiple points generated in block 108 are used to calculate thermal comfort at the same multiple points. The thermal comfort may be estimated in one of a number of different ways. For example, the Fanger equation may be used to estimate the predicted average vote value for a user. This may be calculated for each of the points for which a field was generated in block 108.

The Fanger equation provides for the calculation of a predicted mean vote value (PMV) based on a number of different factors. The PMV may be a scale ranging from-3 (a value representing too cold) to positive 3 (a value representing too hot). A value of 0 would indicate a thermally neutral feel. In some embodiments, the scale may be a continuous scale, not limited to only integers between-3 and 3. The Fanger equation is presented in fig. 2.

In equation 1, M represents the metabolic rate measured in watts per square meter. W represents the effective mechanical power in watts per square meter and can be set to generally zero. From a physical perspective, W measures the mechanical work done by the occupant. H represents the loss of dry heat. E_cIndicating the heat exchange by evaporation on the skin. C_resWhich represents the heat exchange by convection in the breath. E_resIndicating evaporative heat exchange in the breath.

Equations 2 through 5 illustrate how some of the variables in equation 1 are determined. In equation 2, the dry heat loss H is calculated using information about the garment, such as garment insulation, garment surface area factor, and air temperature. In equation 3, E_cThe metabolic rate and the water vapor partial pressure (which is a component of the relative humidity) were used for calculation. In equation 4, the heat exchange is calculated using the metabolic rate and the temperature. In equation 5, the evaporative heat exchange is calculated using the metabolic rate and the partial pressure of water vapor (which is a component of the relative humidity).

Thereafter, the HVAC system may be set based on the thermal comfort data (block 114). For example, if the user's thermal comfort indicates that the user is cold, the heater may be turned on. Similarly, if the thermal comfort equation indicates that the user is hot, the air conditioning system may be turned on.

The user's opinion regarding the current heat level may be collected periodically (block 116). The opinion of the user may be collected in a number of different ways. For example, a software application (also referred to as an "app") may be utilized by a user to provide feedback to the user regarding their current comfort level.

If there is a discrepancy between the calculated thermal comfort level (calculated in block 112) and the perceived thermal comfort level (collected in block 116), the HVAC may be readjusted (block 118). For a particular user, a thermal profile of the user may be saved (block 120). The user's profile may include characteristics of the user with respect to thermal comfort calculations. For example, the profile may indicate that the user typically feels colder (or warmer) than the computed equation would indicate.

The profile may be retrieved whenever the user is located in the room to be adjusted. In some embodiments, the room or building in which the room is to be adjusted may retrieve the thermal profile and automatically use the thermal profile to make adjustments to the calculated thermal comfort level. For example, when a user sensitive to cold anomalies enters a building, the building may sense the user and automatically consider the user when calculating the thermal comfort level. The building may be a "smart" building with multiple sensors that determine the presence and/or location of a user to use the user's thermal profile.

The method described hereinabove addresses the thermal comfort of a single user. To determine how to adjust for a group (e.g., a room containing multiple people), a PMV may be calculated for each person in the group. Thereafter, the average PMV may be used in place of the calculated thermal comfort (calculated in block 112).

In addition, the calculated PMV may be used to determine a predicted dissatisfaction percentage (PPD) for a group setting. In fig. 2, PPD is shown as equation 6. In general, PPD indicates how many people are not satisfied with a certain PMV value. For example, PMV of plus 1.5 or minus 1.5 has been found to cause 50% of the people to be dissatisfied. When calculating the average PMV, PPD may be used as a constraint to ensure that the percentage of people who are not satisfied with a given set of conditions is as small as possible.

FIG. 3 depicts a high-level block diagram of a computer system 300, which may be used to implement one or more embodiments. More specifically, the computer system 300 may be used to implement the hardware components of a system capable of performing the methods described herein. Although one exemplary computer system 300 is shown, computer system 300 includes a communication path 326, communication path 326 connecting computer system 300 to additional systems (not depicted), and may include one or more Wide Area Networks (WANs) and/or Local Area Networks (LANs), such as the internet, intranet(s), and/or wireless communication network(s). Computer system 300 and additional systems communicate via a communication path 326, for example, to communicate data therebetween.

Computer system 300 includes one or more processors, such as processor 302. The processor 302 is connected to a communication infrastructure 304 (e.g., a communication bus, crossbar, or network). The computer system 300 may include a display interface 306, the display interface 306 forwarding graphics, textual content, and other data from the communication infrastructure 304 (or from a frame buffer not shown) for display on a display unit 308. Computer system 300 also includes a main memory 310, preferably Random Access Memory (RAM), and computer system 300 may also include a secondary memory 312. The secondary memory 312 may include, for example, a hard disk drive 314 and/or a removable storage drive 316, representing, for example, a floppy disk drive, a magnetic tape drive, or an optical disk drive. The hard disk drive 314 may be in the form of a Solid State Drive (SSD), a conventional magnetic disk drive, or a mixture of both. There may also be more than one hard disk drive 314 contained within the secondary memory 312. The removable storage drive 316 reads from and/or writes to a removable storage unit 318 in a manner well known to those of ordinary skill in the art from the removable storage unit 318. Removable storage unit 318 represents, for example, a floppy disk, a compact disk, a magnetic tape, or an optical disk, etc. which is read by and written to by removable storage drive 316. As will be appreciated, the removable storage unit 318 includes a computer readable medium having stored therein computer software and/or data.

In alternative embodiments, secondary memory 312 may include other similar means for allowing computer programs or other instructions to be loaded into the computer system. Such devices may include, for example, a removable storage unit 320 and an interface 322. Examples of such devices can include program packages and package interfaces, such as those found in video game devices, removable memory chips such as EPROM, secure digital card (SD card), compact flash (CF card), Universal Serial Bus (USB) memory, or PROM and associated socket, and other removable storage units 320 and interfaces 322 that allow software and data to be transferred from the removable storage unit 320 to the computer system 300.

Computer system 300 may also include a communications interface 324. Communications interface 324 allows software and data to be transferred between the computer system and external devices. Examples of communication interface 324 may include a modem, a network interface (such as an ethernet card), a communication port or PC card slot and card, a universal serial bus port (USB), and so forth. Software and data transferred via communications interface 324 are in the form of signals which may be, for example, electronic, electromagnetic, optical or other signals capable of being received by communications interface 324. These signals are provided to communications interface 324 via a communications path (i.e., channel) 326. Communications path 326 carries signals and may be implemented using wire or cable, fiber optics, a telephone line, a cellular telephone link, an RF link, and/or other communications channels.

In the present description, the terms "computer program medium," "computer usable medium," and "computer readable medium" are used to refer to media such as main memory 310 and secondary memory 312, removable storage drive 316, and a hard disk installed in hard disk drive 314. Computer programs (also called computer control logic) are stored in main memory 310 and/or secondary memory 312. Computer programs may also be received via communications interface 324. Such computer programs, when executed, enable the computer system to perform the features discussed herein. In particular, the computer programs, when executed, enable the processor 302 to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system. Thus, as can be seen from the foregoing detailed description, one or more embodiments provide technical benefits and advantages.

Referring now to fig. 4, a computer program product 400 is generally shown, according to an embodiment, the computer program product 400 including a computer-readable storage medium 402 and program instructions 404.

Embodiments may be systems, methods, and/or computer program products. The computer program product may include computer-readable storage medium(s) having computer-readable program instructions thereon for causing a processor to implement aspects of embodiments of the invention.

The computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium includes the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch cards or raised structures in slots having instructions recorded thereon, and any suitable combination of the foregoing. A computer-readable storage medium as used herein is not to be construed as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a corresponding computing/processing device, or downloaded to an external computer or external storage device via a network (e.g., the internet, a local area network, a wide area network, and/or a wireless network). The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing/processing device.

Computer-readable program instructions for implementing embodiments may include assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, an electronic circuit, including, for example, a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), may execute computer-readable program instructions to perform embodiments of the invention by personalizing the electronic circuit with state information of the computer-readable program instructions.

Embodiments may be implemented using one or more techniques. In some embodiments, an apparatus or system may include one or more processors and memory storing instructions that, when executed by the one or more processors, cause the apparatus or system to perform one or more method acts as described herein. Various mechanical components known to those skilled in the art may be used in some embodiments.

Embodiments may be implemented as one or more devices, systems, and/or methods. In some embodiments, the instructions may be stored on one or more computer program products or computer-readable media (such as transitory and/or non-transitory computer-readable media). The instructions, when executed, may cause an entity (e.g., a processor, device, or system) to perform one or more method acts as described herein.

While the disclosure has been described with reference to one or more exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the disclosure not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this disclosure, but that the disclosure will include all embodiments falling within the scope of the claims.

Claims (20)

1. A computer-implemented method for adjusting an interior region, comprising:

retrieving environmental conditions regarding the interior region, wherein the environmental conditions include temperature, humidity, and air velocity;

retrieving an outdoor environmental condition;

generating fields of environmental conditions at a plurality of points within the interior region;

calculating the thermal comfort level of the user to determine a predicted average vote value; and

operating a heating, ventilation and air conditioning system based on the calculated thermal comfort.

2. The computer-implemented method of claim 1, further comprising:

retrieving usage information regarding the interior region.

3. The computer-implemented method of claim 2, further comprising:

estimating a user's garment insulation based on the outdoor environmental condition and the usage information.

4. The computer-implemented method of claim 1, further comprising:

receiving feedback from the user regarding the user's comfort level; and

operating the heating, ventilation and air conditioning system based on the feedback.

5. The computer-implemented method of claim 4, further comprising:

storing the feedback in a thermal profile associated with the user.

6. The computer-implemented method of claim 5, wherein:

calculating the thermal comfort level of the user comprises calculating the thermal comfort level using the thermal profile.

7. The computer-implemented method of claim 1, wherein:

calculating the thermal comfort level of the user comprises calculating the predicted average vote value using a Fanger equation.

8. The computer-implemented method of claim 7, wherein:

the predicted average vote value is on a continuous scale ranging from-3 to 3, where a value of-3 indicates a cold thermal condition and a value of 3 indicates a warm thermal condition.

9. The computer-implemented method of claim 1, further comprising:

calculating the thermal comfort level for each of a plurality of users in the interior region; and

calculating an average thermal comfort level for the plurality of users; wherein:

operating a heating, ventilation, and air conditioning system based on the average thermal comfort of the plurality of users.

10. The computer-implemented method of claim 9, further comprising:

calculating a predicted percentage of unsatisfied users based on the thermal comfort of each of the plurality of users; wherein:

operating the heating, ventilation and air conditioning system further comprises ensuring that the predicted percentage of unsatisfied users is below a predetermined threshold.

11. A computer system for facilitating anonymous and automated communications, comprising:

a processor;

a memory;

computer program instructions configured to cause the processor to perform the method of:

retrieving environmental conditions regarding the interior area, wherein the environmental conditions include temperature, humidity, and air velocity;

retrieving an outdoor environmental condition;

generating fields of environmental conditions at a plurality of points within the interior region;

calculating a thermal comfort level of the user to determine a predicted average vote value; and

operating a heating, ventilation and air conditioning system based on the calculated thermal comfort.

12. The computer system of claim 11, wherein the method further comprises:

retrieving usage information regarding the interior region.

13. The computer system of claim 12, wherein:

estimating a user's garment insulation based on the outdoor environmental condition and the usage information.

14. The computer system of claim 11, wherein the method further comprises:

receiving feedback from the user regarding the user's comfort level; and

operating the heating, ventilation and air conditioning system based on the feedback.

15. The computer system of claim 14, wherein the method further comprises:

storing the feedback in a thermal profile associated with the user.

16. The computer system of claim 15, wherein:

calculating the thermal comfort level of the user comprises calculating the thermal comfort level using the thermal profile.

17. The computer system of claim 11, wherein:

calculating the thermal comfort level of the user comprises calculating the predicted average vote value using a Fanger equation.

18. The computer system of claim 17, wherein:

the predicted average vote value is on a continuous scale ranging from-3 to 3, where a value of-3 indicates a cold thermal condition and a value of 3 indicates a warm thermal condition.

19. The computer system of claim 11, wherein the method further comprises:

calculating the thermal comfort level for each of a plurality of users in the interior region; and

calculating an average thermal comfort level for the plurality of users; wherein:

operating a heating, ventilation, and air conditioning system based on the average thermal comfort of the plurality of users.

20. The computer system of claim 19, wherein the method further comprises:

calculating a predicted percentage of unsatisfied users based on the thermal comfort of each of the plurality of users; wherein

Operating the heating, ventilation and air conditioning system further comprises ensuring that the predicted percentage of unsatisfied users is below a predetermined threshold.

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