Patent Publication Number: US-2022221178-A1

Title: Heating, ventilation, and air conditioning system control using adaptive occupancy scheduling

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to Heating, Ventilation, and Air Conditioning (HVAC) system control, and more specifically to HVAC system control using adaptive occupancy scheduling. 
     BACKGROUND 
     Existing heating, ventilation, and air conditioning (HVAC) systems typically rely on a user (e.g. a homeowner) to provide scheduling information about when they will be home or away. However, some users may never provide this information to the HVAC system. Determining when a homeowner will be present or away without requiring the user to provide this information in advance poses several technical challenges because existing HVAC systems are unable to determine this information on their own. Without this information, an HVAC system is unable to provide energy-saving benefits, for example, reduced power consumption, and to reduce the wear on its components because existing HVAC systems are unable to automatically adjust set point temperatures without knowing when a user will be present. It is typically not desirable for an HVAC system to make changes to a set point temperature without knowing when a user will be present because these changes may affect the comfort level of the user. 
     SUMMARY 
     The system disclosed in the present application provides a technical solution to the technical problems discussed above by providing an adaptive heating, ventilation, and air conditioning (HVAC) control system that is configured to predict when a user will be away from a space and when they will return to the space. By predicting when a user will be present within a space, the adaptive control system is able to provide better control and management of an HVAC system. For example, the adaptive control system may adjust the HVAC settings (e.g. set point temperature) to provide energy-saving benefits and reduced power consumption for the space while the user is away. The adaptive HVAC control system may also predict when the user will return to the space. This feature allows the adaptive HVAC control system to adjust the HVAC settings back to a comfortable level before the user returns. This process allows the adaptive HVAC control system to provide energy savings and improved resource utilization when the user is away and to maintain a comfortable environment for the user while they are present. 
     The disclosed system provides several practical applications and technical advantages which include a process for predicting when a user will be away and when they will return to a space. Unlike existing HVAC systems that rely on a user (e.g. a homeowner) providing scheduling information about when they will be home or away, the adaptive HVAC control system uses historical information based on the user&#39;s behavior and patterns to predict when the user will be away from the space. This process also allows the adaptive HVAC control system to learn and predict when the user will be away from the space without relying on an input from the user. This process also allows the adaptive HVAC control system to efficiently control the operation of the HVAC system based on when the user will be away from the space. 
     In one embodiment, the system comprises a device that is configured to train a machine learning model based on a user&#39;s behavior and patterns. For example, the device may identify timestamps over a time period when a space is unoccupied. The device also identifies a set point temperature for each timestamp. The device may then train a machine learning model using the timestamps and corresponding set point temperatures. The machine learning model is configured to receive a timestamp for the current day and/or time as an input and to output a predicted return time for a user and a set point temperature based on the timestamp. The machine learning model is trained using an occupancy history log that stored timestamps for when a user is detected within the space, timestamps for when a user is not present within the space, set point temperatures, or any other suitable type of information about a user&#39;s behavior. After the training process, the machine learning model will be configured to determine a predicted return time when a user will return to the space as well as a suitable set point temperature for the space while the user is away. 
     After training the machine learning model, the device may use the machine learning model to control an HVAC system. For example, the device may determine a timestamp that corresponds with the current day and/or time, input the timestamp into the machine learning model, and obtain HVAC control settings from the machine learning model in response to inputting the timestamp into the machine learning model. The HVAC control settings include a predicted return time for the user and a set point temperature for the space while the user is away. The device may then operate the HVAC system at the set point temperature until the return time. 
     Certain embodiments of the present disclosure may include some, all, or none of these advantages. These advantages and other features will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings and claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this disclosure, reference is now made to the following brief description, taken in connection with the accompanying drawings and detailed description, wherein like reference numerals represent like parts. 
         FIG. 1  is a schematic diagram of an embodiment of an adaptive control system for heating, ventilation, and air conditioning (HVAC) systems; 
         FIG. 2  is a flowchart of an embodiment of an adaptive control process for an HVAC system; 
         FIG. 3  is an embodiment of an adaptive control device for the HVAC system; 
       and 
         FIG. 4  is a schematic diagram of an embodiment of an HVAC system configured to integrate with the adaptive control system. 
     
    
    
     DETAILED DESCRIPTION 
     System Overview 
       FIG. 1  is a schematic diagram of an embodiment of an adaptive control system  100  for heating, ventilation, and air conditioning (HVAC) systems  104 . The adaptive control system  100  is generally configured to predict when a space  106  (e.g. a home) is unoccupied and to control an HVAC system  104  for the space  106  while the space is unoccupied to provide energy savings and improved resource utilization. 
     Existing HVAC systems typically rely on a user (e.g. a homeowner) to provide scheduling information about when they will be home or away. However, some users may never provide this information to the HVAC system. Determining when a homeowner will be present or away without requiring the user to provide this information in advance poses several technical challenges because existing HVAC systems are unable to determine this information on their own. Without this information, an HVAC system is unable to provide energy-saving benefits, for example, reduced power consumption, and to reduce the wear on its components because existing HVAC systems are unable to automatically adjust set point temperatures without knowing when a user will be present. 
     In contrast, the adaptive control system  100  is configured to predict when a user will be away from a space  106  and when they will return to the space  106 . By predicting when a user will be present within a space  106 , the adaptive control system  100  is able to provide better control and management of the HVAC system  104 . For example, the adaptive control system  100  may adjust the HVAC settings (e.g. set point temperature) to provide energy saving benefits and reduced power consumption for the space  106  while the user is away from the space  106 . The adaptive control system  100  may also predict when the user will return to the space  106 . This feature allows the adaptive control system  100  to adjust the HVAC settings back to a comfortable level before the user returns. This process allows the adaptive control system  100  to provide energy savings and improved resource utilization when the user is away from a space  106  and to maintain a comfortable environment for the user while they are present within the space  106 . 
     In one embodiment, the adaptive control system  100  comprises a thermostat  102  and an HVAC system  104  that are in signal communication with each other over a network  108 . The network  108  may be any suitable type of wireless and/or wired network including, but not limited to, all or a portion of the Internet, an Intranet, a private network, a public network, a peer-to-peer network, the public switched telephone network, a cellular network, a local area network (LAN), a metropolitan area network (MAN), a personal area network (PAN), a wide area network (WAN), and a satellite network. The network  108  may be configured to support any suitable type of communication protocol as would be appreciated by one of ordinary skill in the art. 
     HVAC System 
     An HVAC system  104  is generally configured to control the temperature of a space  106 . Examples of a space  106  include, but are not limited to, a room, a home, an apartment, a mall, an office, a warehouse, or a building. The HVAC system  104  may comprise the thermostat  102 , compressors, blowers, evaporators, condensers, and/or any other suitable type of hardware for controlling the temperature of the space  106 . An example of an HVAC system  104  configuration and its components are described in more detail below in  FIG. 4 . Although  FIG. 1  illustrates a single HVAC system  104 , a location or space  106  may comprise a plurality of HVAC systems  104  that are configured to work together. For example, a large building may comprise multiple HVAC systems  104  that work cooperatively to control the temperature within the building. 
     Thermostat 
     The thermostat  102  is generally configured to collect information about when a user is present within the space  106 , to collect information about a user&#39;s temperature preferences, and to control the HVAC system  104  based on the collected information. An example of the thermostat  102  in operation is described below in  FIG. 2 . In one embodiment, the thermostat  102  comprises an adaptive HVAC control engine  110 , and a memory  112 . The thermostat  102  may further comprise a graphical user interface, a display, a touch screen, buttons, knobs, or any other suitable combination of components. Additional details about the hardware configuration of the thermostat  102  are described in  FIG. 3 . 
     The adaptive HVAC control engine  110  is generally configured to predict when a user will be away from a space  106  and when they will return to the space  106 . By predicting when a user will be present within a space  106 , the adaptive HVAC control engine  110  is able to provide better control and management of the HVAC system  104 . For example, the adaptive HVAC control engine  110  may adjust the HVAC settings (e.g. set point temperature) to provide energy-saving benefits and reduced power consumption for the space  106  while the user is away. The adaptive HVAC control engine  110  may also predict when the user will return to the space  106 . This feature allows the adaptive HVAC control engine  110  to adjust the HVAC settings back to a comfortable level before the user returns. This process allows the adaptive HVAC control engine  110  to provide energy savings and improved resource utilization when the user is away from a space  106  and to maintain a comfortable environment for the user while they are present within the space  106 . An example of the adaptive HVAC control engine  110  in operation is described in  FIG. 2 . 
     The memory  112  is configured to store a user-provided schedule  114 , an occupancy history log  118 , a machine learning model  120 , and/or any other suitable type of data. A user-provided schedule  114  comprises information about when a user plans to be present or away from a space  106 . For example, the user-provided schedule  114  may comprise timestamps that identify days and times of the day when a user will be present or away from the space  106 . The user-provided schedule  114  may further comprise user preferences such as preferred set point temperatures for the space  106 . For example, the user-provided schedule  114  may associate set point temperatures with the timestamps for when the user will be present or away from the space  106 . The user-provided schedule  114  allows the user to indicate their preferred set point temperatures while they are present within the space  106  as well as suitable set point temperatures while they are away from the space  106 . In some embodiments, the user-provided schedule  114  may further comprise any other suitable type of information associated with the user and their preferences. A user may provide the user-provided schedule  114  to the thermostat  102  using a graphical user interface. As an example, a user may provide the user-provided schedule  114  to the thermostat  102  using a display and interface (e.g. a touchscreen) on the thermostat  102 . As another example, a user may provide the user-provided schedule  114  to the thermostat  102  using a mobile device application, a computer application, or an online interface (e.g. a website). In other examples, a user may provide the user-provided schedule  114  to the thermostat  102  using any other suitable technique. 
     The occupancy history log  118  is generally configured to store information about the behavior of a user of the space  106 . For example, the occupancy history log  118  may store timestamps for when a user is detected within the space  106 , timestamps for when a user is not present within the space  106 , set point temperatures, or any other suitable type of information about a user&#39;s behavior. The information in the occupancy history log  118  comprises information based on a user&#39;s actual behavior which may differ from the information provided in the user-provided schedule  114 . Using the occupancy history log  118 , the thermostat  102  is able to learn about the patterns and preferences of the user based on their behavior. The occupancy history log  118  comprises a plurality of entries  128 . In one embodiment, each entry  128  comprises a timestamp  122 , an occupancy status  124  that indicated whether a user was present or away from the space  106 , and a set point temperature  126 . In other examples, each entry  128  may further comprise any other suitable type or combination of information that is associated with the behavior of a user. 
     Examples of machine learning models  120  include, but are not limited to, a multi-layer perceptron or any other suitable type of neural network model. The machine learning models  120  are generally configured to output HVAC settings for the space  106  based on a timestamp for the current day and/or time. In one embodiment, a machine learning model  120  is configured to receive a timestamp as an input and to output a predicted return time and a set point temperature for the space  106  based on the timestamp. The machine learning model  120  is trained using the occupancy history log  118 . During the training process, the machine learning model  120  determines weight and bias values for a mapping function that allows the machine learning model  120  to map a timestamp for a current day and/or time to a predicted return time and set point temperature. Through this process, the machine learning model  120  is configured to determine a predicted return time when a user will return to the space  106 . The machine learning model  120  is also configured to determine a suitable set point temperature for the space  106  while the user is away. The occupancy detection engine  110  may train the machine learning model  120  using any suitable technique as would be appreciated by one of ordinary skill in the art. 
     Adaptive Control Process for an HVAC System 
       FIG. 2  is a flowchart of an embodiment of an adaptive control process  200  for an HVAC system  104 . The adaptive control system  100  may employ process  200  to predict when a space  106  (e.g. a home) is unoccupied and to control an HVAC system  104  for the space  106  while the space is unoccupied to provide energy savings and improved resource utilization. In a first phase, the adaptive control system  100  collects historical information for the space  106  that is used to train a machine learning model  120  to predict when the space  106  is unoccupied based on the behavior of a user. The historical information comprises timestamps for when a user is detected within the space  106 , timestamps for when a user is not present within the space  106 , and set point temperatures over a period of time. In a second phase after the machine learning model  120  is trained, the adaptive control system  100  determines a timestamp for a current day and/or time and provides the timestamp to the trained machine learning model  120  to obtain a predicted return time for the user and a set point temperature for the space  106  while the user is away. Process  200  allows the adaptive control system  100  to efficiently control the operation of the HVAC system  104  based on whether the space  106  is occupied. 
     Machine Learning Model Training Phase 
     At step  202 , the thermostat  102  obtains a user-provided schedule  114  for a space  106 . The user-provided schedule  114  comprises information about when a user plans to be present or away from a space  106 . For example, the user-provided schedule  114  may comprise a calendar with timestamps that identify days and times of the day when a user will be present or away from the space  106 . The user-provided schedule  114  may also comprise set point temperatures for the space  106  while the user is present or away from the space  106 . A user may provide the user-provided schedule  114  to the thermostat  102  using a graphical user interface. For example, a user may provide the user-provided schedule  114  using a display and interface (e.g. a touchscreen) on the thermostat  102 . As another example, a user may provide the user-provided schedule  114  using a mobile device application, a computer application, or an online interface (e.g. a website). In other examples, a user may provide the user-provided schedule  114  using any other suitable technique. In some embodiments, step  202  may be optional or omitted. 
     At step  204 , the thermostat  102  collect historical information for the space  106  over a predetermined time interval. Here, the thermostat  102  compiles information about when a user is present and away from the space  106  over a predetermined time interval. The thermostat  102  also compiles the user&#39;s preferred set point temperatures over the predetermined time interval. The predetermined time interval may be one week, two weeks, one month, two months, or any other suitable amount of time. During the predetermined time interval, the thermostat  102  populates entries  128  in the occupancy history log  118  based on the user&#39;s behavior. As an example, each entry  128  may comprise a timestamp  122 , an occupancy status  124  that indicated whether a user was present or away from the space  106 , and a set point temperature  126 . In this example, each timestamp  112  may identify a particular day and time (e.g. an hour and/or minute). The occupancy status  124  may be a value (e.g. a Boolean value) that indicates whether a person is present or away from the space  106 . For example, a Boolean value of one may indicate that the user is present within the space  106  and a Boolean value of zero may indicate that the user is away from the space  106 . The thermostat  102  may determine whether the user is present within the space  106  using a proximity sensor, motion detecting sensors, or any other suitable type of sensor. The set point temperature  126  indicates the current set point temperature for the space  106 . In other examples, each entry  128  may further comprise any other suitable type or combination of information that is associated with the behavior of a user. 
     In some embodiments, the thermostat  102  may be configured to check for weather alerts before populating an entry  128  in the occupancy history log  118 . For example, the thermostat  102  may query a third-party server about the forecasted weather for the current day. The thermostat  102  may communicate with the third-party server using an application programming interface (API) or any other suitable technique. In response to sending the query, the thermostat  102  may receive information about the forecasted weather for the current day. The received weather information may comprise an indication about whether a weather alert has been forecasted for the current day. Examples of weather alerts include, but are not limited to, rainstorms, ice storms, snow, tornados, freezing temperatures, high temperatures, high winds, or any other type of inclement weather. If the received weather information comprises a weather alert, the thermostat  102  may determine to not populate an entry  128  in the occupancy history log  118  for the current day. In this case, the weather alert indicates that the current day may be an outlier which means that the user may deviate from their normal behavior patterns due to the inclement weather. By omitting the entries  128  in the occupancy history log  118  during inclement weather, the thermostat  102  is able to more accurately capture the normal behavior patterns for the user. 
     The information in the occupancy history log  118  comprises information based on a user&#39;s actual behavior which may differ from the information provided in the user-provided schedule  114 . Using the occupancy history log  118 , the thermostat  102  is able to learn about the patterns and preferences of the user based on their behavior. In one embodiment, the thermostat  102  may be configured to identify conflicts between the information in the user-provided schedule  114  and the occupancy history log  118 . As an example, the thermostat  102  may identify timestamps  122  in the occupancy history log  118  that conflict with the user-provided schedule  114 . For instance, a timestamp  122  may indicate that the user is away from the space  106  when the user indicated that they would be present in the user-provided schedule  114 . As another example, the thermostat  102  may identify set point temperatures  126  in the occupancy history log  118  that are different from the set point temperatures provided by the user in the user-provided schedule  114 . If the thermostat  102  detects a conflict between the information in the user-provided schedule  114  and the occupancy history log  118 , thermostat  102  may prompt the user to reconcile the conflicts. For example, the thermostat  102  may display any identified conflicts to the user using a graphical user interface. The thermostat  102  may also request a user input to accept or change entries  128  associated with the conflicts in the occupancy history log  118 . In some embodiments, the thermostat  102  may not prompt the user and may proceed with the collected information in the occupancy history log  118 . 
     At step  206 , the thermostat  102  trains a machine learning model  120  based on the collected historical information for the space  106 . The thermostat  102  may use any suitable technique for training the machine learning model  120  using the occupancy history log  118  as would be appreciated by one of ordinary skill in the art. After training the machine learning model  120 , the machine learning model  120  is configured to output a predicted return time and set point temperature based on a timestamp for a current day and/or time. 
     Adaptive HVAC Control Phase 
     After training the machine learning model  120 , the thermostat  102  may begin using the machine learning model  120  to predict when a user will be away from the space  106  and to adjust the HVAC settings of the HVAC system  104  to transition the HVAC system  104  to an energy-saving or low-power mode (i.e. an adaptive HVAC control mode) when the user is away from the space  106 . 
     At step  208 , the thermostat  102  determines a timestamp that corresponds with the current day and/or time. Here, the thermostat  102  determines the current day and/or time which will be used as an input for the machine learning model  120 . The machine learning model  120  is configured to output HVAC settings based on the timestamp for a current day and/or time. At step  210 , the thermostat  102  inputs the timestamp into the trained machine learning model  120  to obtain HVAC control settings. In one embodiment, the HVAC control settings comprise a predicted return time for the user and a set point temperature. In some embodiments, the machine learning model  120  may also output a confidence level or probability that is associated with the predicted return time and set point temperature. 
     At step  212 , the thermostat  102  determines whether to implement the adaptive HVAC control mode. Here, the thermostat  102  may perform one or more checks to determine whether to implement the adaptive HVAC control mode using the prediction results from the machine learning model  120 . In one embodiment, the thermostat  102  may determine to implement the adaptive HVAC control when the user will be away from the space  106  for at least a predetermined amount of time. For example, the thermostat  102  may determine a time difference between the current time and the predicted return time for the user. The thermostat may then compare the time difference to a time difference threshold value. The time difference threshold value identifies a minimum amount of time that the space  106  will be unoccupied to implement the adaptive HVAC control mode. The time difference threshold value may be set to four hours, six hours, eight hours, or any other suitable amount of time. In this example, the thermostat  102  determines to implement the adaptive HVAC control mode when the determined time difference is greater than or equal to the time difference threshold value. 
     In some embodiments, the thermostat  102  may also consider weather information when determining whether to implement the adaptive HVAC control mode. For example, the thermostat  102  may query a third-party server about the forecasted weather for the current day. In response to sending the query, the thermostat  102  receives information about the forecasted weather for the current day. The received weather information may comprise an indication about whether a weather alert has been forecasted. In this example, the thermostat  102  may determine to implement the adaptive HVAC control mode when a weather alert is not forecasted for the current day. When a weather alert is forecasted for the current day, the thermostat  102  may determine to not implement the adaptive HVAC control mode since the user&#39;s typical behavior may change because of the weather. In other embodiments, the thermostat  102  may use any other suitable type or combination of criteria for determining whether to implement the adaptive HVAC control mode. 
     The thermostat  102  returns to step  208  in response to determining not to implement the adaptive HVAC control mode. In this case, will return to step  208  to wait until a later time to check again whether to implement the adaptive HVAC control mode. For example, the thermostat  102  may wait for twelve hours or twenty-fours before checking again whether to implement the adaptive HVAC control mode based on a new timestamp. In other examples, the thermostat  102  may wait for any other suitable amount of time before checking again whether to implement the adaptive HVAC control mode. 
     The thermostat  102  proceeds to step  214  in response to determining to implement the adaptive HVAC control mode. At step  214 , the thermostat  102  controls the HVAC system  104  using the HVAC control settings until the predicted return time for the user. For example, the thermostat  102  may send commands or instructions to the HVAC system  104  to operate the HVAC system  104  at the set point temperature that was provided by the machine learning model  120  in step  210 . This process allows the thermostat  102  to operate the HVAC system  104  in an energy-saving or low-power mode while the user is away from the space  106 . 
     In some embodiments, the thermostat  102  may be further configured to determine a transition time that occurs before when the user is expected to return to the space  106 . The transition time is an amount of time that will be used to transition the adjusted set point temperature back to a user-preferred set point temperature before the user returns to the space  106 . For example, thermostat  102  may increase the temperature within the space  106  while the user is away. In this example, the thermostat  102  will then reduce the temperature back to a comfortable temperature before the user returns to the space  106 . The transition time may be set to thirty minutes, one hour, or any suitable amount of time before when the user is expected to return to the space  106 . Thermostat  102  may use information from the user-provided schedule  114  and/or the occupancy history log  118  to determine a new set point temperature based on the user&#39;s preferences. For example, the thermostat  102  may use the timestamp for the predicted return time for the user to identify a similar timestamp within the user-provided schedule  114  and/or the occupancy history log  118 . The thermostat  102  may then identify a new set point temperature that corresponds with the matching timestamp. The thermostat  102  may then send commands or instructions to the HVAC system  104  to operate the HVAC system  104  at the new set point temperature starting at the transition time. 
     While the thermostat  102  is implementing the adaptive HVAC control mode, the thermostat  102  periodically checks whether any triggering events have been detected for exiting the adaptive HVAC control mode. At step  216 , the thermostat  102  determines whether any triggering events have been detected for aborting the adaptive HVAC control mode. As an example, a triggering event may be detecting the presence of the user within a predetermined distance of the space  106 . In this example, the thermostat  102  may use a geofence or Global Positioning System (GPS) information to determine whether the user is within the predetermined distance of the space  106 . For instance, a user device (e.g. a smartphone) that is associated with the user may be configured to periodically provide location information (e.g. a GPS coordinate) to the thermostat  102 . The thermostat  102  determines a distance between the location of the space  106  and the current location of the user to determine whether the user is within the predetermined distance of the space  106 . The predetermined distance of the space  106  may be set to one mile, two miles, five miles, or any other suitable distance. 
     As another example, a triggering event may be detecting a user device that is associated with a user has joined a wireless network (e.g. a WiFi network) that is associated with the space  106 . In this example, the thermostat  102  may be configured to periodically receive information from an access point about the devices that are currently connected to a wireless network for the space  106 . The thermostat  102  may compare an identifier for a known user device (e.g. a smartphone) to the list of devices that are currently connected to the access point to determine whether the user is present. The thermostat  102  determines that the user is present at the space  106  when the known user device matches one of the devices in the list of devices that are currently connected to the access point. 
     As another example, a triggering event may be detecting the presence of the user within the space  106 . In this example, the thermostat  102  may use proximity sensors, motion detection sensors, door sensors, or any other suitable type of sensors to detect the presence of the user. In other examples, the thermostat  102  may use any other suitable type or combination of triggering events for determining whether to abort the adaptive HVAC control mode. 
     The thermostat  102  returns to step  214  in response to determining that no triggering events have been detected for aborting the adaptive HVAC control mode. In this case, the thermostat  102  will continue using the current HVAC settings until the predicted return time for the user or a determined transition time is reached or until a triggering event has been detected. 
     Otherwise, the thermostat  102  will terminate process  200  in response to detecting a triggering event for aborting the adaptive HVAC control mode. In this case, the thermostat  102  will exit the adaptive occupancy mode and resume normal operation of the HVAC system  104  using the previous user-provided settings (e.g. set point temperature). Here, the thermostat  102  adjusts the temperature back to a comfortable temperature for the user. For example, the thermostat  102  may use a process similar to the process described in step  214  to determine a new set point temperature for the user and to operate the HVAC system  104  based on the new set point temperature. 
     Hardware Configuration for an Adaptive Control Device 
       FIG. 3  is an embodiment of an adaptive control device (e.g. thermostat  102 ) of an adaptive control system  100 . As an example, the thermostat  102  comprises a processor  302 , a memory  112 , a display  308 , and a network interface  304 . The thermostat  102  may be configured as shown or in any other suitable configuration. 
     Processor 
     The processor  302  comprises one or more processors operably coupled to the memory  112 . The processor  302  is any electronic circuitry including, but not limited to, state machines, one or more central processing unit (CPU) chips, logic units, cores (e.g. a multi-core processor), field-programmable gate array (FPGAs), application-specific integrated circuits (ASICs), or digital signal processors (DSPs). The processor  302  may be a programmable logic device, a microcontroller, a microprocessor, or any suitable combination of the preceding. The processor  302  is communicatively coupled to and in signal communication with the memory  112 , the display  308 , and the network interface  304 . The one or more processors are configured to process data and may be implemented in hardware or software. For example, the processor  302  may be 8-bit, 16-bit, 32-bit, 64-bit, or of any other suitable architecture. The processor  302  may include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, processor registers that supply operands to the ALU and store the results of ALU operations, and a control unit that fetches instructions from memory and executes them by directing the coordinated operations of the ALU, registers and other components. 
     The one or more processors are configured to implement various instructions. For example, the one or more processors are configured to execute adaptive HVAC control instructions  306  to implement the adaptive HVAC control engine  110 . In this way, processor  302  may be a special-purpose computer designed to implement the functions disclosed herein. In an embodiment, the adaptive HVAC control engine  110  is implemented using logic units, FPGAs, ASICs, DSPs, or any other suitable hardware. The adaptive HVAC control engine  110  is configured to operate as described in  FIGS. 1 and 2 . For example, the adaptive HVAC control engine  110  may be configured to perform the steps of process  200  as described in  FIG. 2 . 
     Memory 
     The memory  112  is operable to store any of the information described above with respect to  FIGS. 1 and 2  along with any other data, instructions, logic, rules, or code operable to implement the function(s) described herein when executed by the processor  302 . The memory  112  comprises one or more disks, tape drives, or solid-state drives, and may be used as an over-flow data storage device, to store programs when such programs are selected for execution, and to store instructions and data that are read during program execution. The memory  112  may be volatile or non-volatile and may comprise a read-only memory (ROM), random-access memory (RAM), ternary content-addressable memory (TCAM), dynamic random-access memory (DRAM), and static random-access memory (SRAM). 
     The memory  112  is operable to store adaptive HVAC control instructions  306 , user-provided schedules  114 , occupancy history logs  118 , machine learning models  120 , and/or any other data or instructions. The adaptive HVAC control instructions  306  may comprise any suitable set of instructions, logic, rules, or code operable to execute the adaptive HVAC control engine  110 . The user-provided schedules  114 , the occupancy history logs  118 , and the machine learning models  120  are configured similar to the user-provided schedules  114 , the occupancy history logs  118 , and the machine learning models  120  described in  FIGS. 1-2 . 
     Display 
     The display  308  is configured to present visual information to a user using graphical objects. Examples of the display  308  include, but are not limited to, a liquid crystal display (LCD), a liquid crystal on silicon (LCOS) display, a light-emitting diode (LED) display, an active-matrix OLED (AMOLED), an organic LED (OLED) display, a projector display, or any other suitable type of display as would be appreciated by one of ordinary skill in the art. 
     Network Interface 
     The network interface  304  is configured to enable wired and/or wireless communications. The network interface  304  is configured to communicate data between the thermostat  102  and other devices (e.g. the HVAC system  104 ), systems, or domains. For example, the network interface  304  may comprise an NFC interface, a Bluetooth interface, a Zigbee interface, a Z-wave interface, an RFID interface, a WIFI interface, a LAN interface, a WAN interface, a PAN interface, a modem, a switch, or a router. The processor  302  is configured to send and receive data using the network interface  304 . The network interface  304  may be configured to use any suitable type of communication protocol as would be appreciated by one of ordinary skill in the art. 
     HVAC System Configuration 
       FIG. 4  is a schematic diagram of an embodiment of an HVAC system  104  configured to integrate with an adaptive control system  100 . The HVAC system  104  conditions air for delivery to an interior space of a building or home. In some embodiments, the HVAC system  104  is a rooftop unit (RTU) that is positioned on the roof of a building and the conditioned air is delivered to the interior of the building. In other embodiments, portions of the system may be located within the building and a portion outside the building. The HVAC system  104  may also include heating elements that are not shown here for convenience and clarity. The HVAC system  104  may be configured as shown in  FIG. 4  or in any other suitable configuration. For example, the HVAC system  104  may include additional components or may omit one or more components shown in  FIG. 4 . 
     The HVAC system  104  comprises a working-fluid conduit subsystem  402  for moving a working fluid, or refrigerant, through a cooling cycle. The working fluid may be any acceptable working fluid, or refrigerant, including, but not limited to, fluorocarbons (e.g. chlorofluorocarbons), ammonia, non-halogenated hydrocarbons (e.g. propane), hydrofluorocarbons (e.g. R- 410 A), or any other suitable type of refrigerant. 
     The HVAC system  104  comprises one or more condensing units  403 . In one embodiment, the condensing unit  403  comprises a compressor  404 , a condenser coil  406 , and a fan  408 . The compressor  404  is coupled to the working-fluid conduit subsystem  402  that compresses the working fluid. The condensing unit  403  may be configured with a single-stage or multi-stage compressor  404 . A single-stage compressor  404  is configured to operate at a constant speed to increase the pressure of the working fluid to keep the working fluid moving along the working-fluid conduit subsystem  402 . A multi-stage compressor  404  comprises multiple compressors configured to operate at a constant speed to increase the pressure of the working fluid to keep the working fluid moving along the working-fluid conduit subsystem  402 . In this configuration, one or more compressors can be turned on or off to adjust the cooling capacity of the HVAC system  104 . In some embodiments, a compressor  404  may be configured to operate at multiple speeds or as a variable speed compressor. For example, the compressor  404  may be configured to operate at multiple predetermined speeds. 
     In one embodiment, the condensing unit  403  (e.g. the compressor  404 ) is in signal communication with a controller or thermostat  102  using a wired or wireless connection. The thermostat  102  is configured to provide commands or signals to control the operation of the compressor  404 . For example, the thermostat  102  is configured to send signals to turn on or off one or more compressors  404  when the condensing unit  403  comprises a multi-stage compressor  404 . In this configuration, the thermostat  102  may operate the multi-stage compressors  404  in a first mode where all the compressors  404  are on and a second mode where at least one of the compressors  404  is off. In some examples, the thermostat  102  may be configured to control the speed of the compressor  404 . 
     The condenser  406  is configured to assist with moving the working fluid through the working-fluid conduit subsystem  402 . The condenser  406  is located downstream of the compressor  404  for rejecting heat. The fan  408  is configured to move air  409  across the condenser  406 . For example, the fan  408  may be configured to blow outside air through the heat exchanger to help cool the working fluid. The compressed, cooled working fluid flows downstream from the condenser  406  to an expansion device  410 , or metering device. 
     The expansion device  410  is configured to remove pressure from the working fluid. The expansion device  410  is coupled to the working-fluid conduit subsystem  402  downstream of the condenser  406 . The expansion device  410  is closely associated with a cooling unit  412  (e.g. an evaporator coil). The expansion device  410  is coupled to the working-fluid conduit subsystem  402  downstream of the condenser  406  for removing pressure from the working fluid. In this way, the working fluid is delivered to the cooling unit  412  and receives heat from airflow  414  to produce a treated airflow  416  that is delivered by a duct subsystem  418  to the desired space, for example, a room in the building. 
     A portion of the HVAC system  104  is configured to move air across the cooling unit  412  and out of the duct sub-system  418 . Return air  420 , which may be air returning from the building, fresh air from outside, or some combination, is pulled into a return duct  422 . A suction side of a variable-speed blower  424  pulls the return air  420 . The variable-speed blower  424  discharges airflow  414  into a duct  426  from where the airflow  414  crosses the cooling unit  412  or heating elements (not shown) to produce the treated airflow  416 . 
     Examples of a variable-speed blower  424  include, but are not limited to, belt-drive blowers controlled by inverters, direct-drive blowers with electronically commutated motors (ECM), or any other suitable types of blowers. In some configurations, the variable-speed blower  424  is configured to operate at multiple predetermined fan speeds. In other configurations, the fan speed of the variable-speed blower  424  can vary dynamically based on a corresponding temperature value instead of relying on using predetermined fan speeds. In other words, the variable-speed blower  424  may be configured to dynamically adjust its fan speed over a range of fan speeds rather than using a set of predetermined fan speeds. This feature also allows the thermostat  102  to gradually transition the speed of the variable-speed blower  424  between different operating speeds. This contrasts with conventional configurations where a variable-speed blower  424  is abruptly switched between different predetermined fan speeds. The variable-speed blower  424  is in signal communication with the thermostat  102  using any suitable type of wired or wireless connection  427 . The thermostat  102  is configured to provide commands or signals to the variable-speed blower  424  to control the operation of the variable-speed blower  424 . For example, the thermostat  102  is configured to send signals to the variable-speed blower  424  to control the fan speed of the variable-speed blower  424 . In some embodiments, the thermostat  102  may be configured to send other commands or signals to the variable-speed blower  424  to control any other functionality of the variable-speed blower  424 . 
     The HVAC system  104  comprises one or more sensors  440  in signal communication with the thermostat  102 . The sensors  440  may comprise any suitable type of sensor for measuring the air temperature. The sensors  440  may be positioned anywhere within a conditioned space (e.g. a room or building) and/or the HVAC system  104 . For example, the HVAC system  104  may comprise a sensor  440  positioned and configured to measure an outdoor air temperature. As another example, the HVAC system  104  may comprise a sensor  440  positioned and configured to measure a supply or treated air temperature and/or a return air temperature. In other examples, the HVAC system  104  may comprise sensors  440  positioned and configured to measure any other suitable type of air temperature. 
     The HVAC system  104  comprises one or more thermostats  102 , for example, located within a conditioned space (e.g. a room or building). A thermostat  102  may be a single-stage thermostat, a multi-stage thermostat, or any suitable type of thermostat as would be appreciated by one of ordinary skill in the art. The thermostat is configured to allow a user to input a desired temperature or temperature set point for a designated space or zone such as the room. 
     While several embodiments have been provided in the present disclosure, it should be understood that the disclosed systems and methods might be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present examples are to be considered as illustrative and not restrictive, and the intention is not to be limited to the details given herein. For example, the various elements or components may be combined or integrated with another system or certain features may be omitted, or not implemented. 
     In addition, techniques, systems, subsystems, and methods described and illustrated in the various embodiments as discrete or separate may be combined or integrated with other systems, modules, techniques, or methods without departing from the scope of the present disclosure. Other items shown or discussed as coupled or directly coupled or communicating with each other may be indirectly coupled or communicating through some interface, device, or intermediate component whether electrically, mechanically, or otherwise. Other examples of changes, substitutions, and alterations are ascertainable by one skilled in the art and could be made without departing from the spirit and scope disclosed herein. 
     To aid the Patent Office, and any readers of any patent issued on this application in interpreting the claims appended hereto, applicants note that they do not intend any of the appended claims to invoke 35 U.S.C. § 112(f) as it exists on the date of filing hereof unless the words “means for” or “step for” are explicitly used in the particular claim.