DISTRIBUTED SMART THERMOSTAT

Abstract

In embodiments of the disclosure, a distributed thermostat includes a controller unit that houses a controller operable to control operation of a heating, ventilation, and air conditioning (HVAC) system. The controller is further operable to receive environmental information from a sensor network that is distributed from the controller unit; receive user inputs from a user interface application that is distributed from the controller unit; and control the operation of the HVAC system based at least in part on the environmental information and the user inputs.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 63/388,432 filed Jul. 12, 2022, the disclosure of which is incorporated herein by reference in its entirety.

BACKGROUND

Exemplary embodiments of the present disclosure relate to smart thermostats, and more particularly, to a distributed smart thermostat operable to control operation of a heating, ventilation, and air conditioning (HVAC) system without the need to mount a thermostat on the wall of a site.

The adjective “smart” is often used to describe the use of computer-based, networked technologies to augment the features of a product or a system. Smart products are embedded with processors, sensors, software, and connectivity that allow data about the product to be gathered, processed, and transmitted to external systems. The data collected from smart/connected products can be analyzed and used to inform decision-making and enable operational efficiencies of the product.

Smart thermostats are Wi-Fi thermostats that can be used with home automation and are responsible for controlling a home's HVAC system, Smart thermostats allow users to control the temperature of their home throughout the day using a schedule, but also contain additional features, such as sensors and Wi-Fi connectivity that enable the thermostat to connect to the Internet. Users can adjust heating settings from other Internet-connected devices, such as a laptop or smartphones, which allows users to control the thermostat remotely.

BRIEF DESCRIPTION

According to an embodiment, a distributed thermostat includes a controller unit that houses a controller operable to control operation of an HVAC system. The controller is further operable to receive environmental information from a sensor network that is distributed from the controller unit; receive user inputs from a user interface application that is distributed from the controller unit; and control the operation of the HVAC system based at least in part on the environmental information and the user inputs.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the operation of the HVAC system includes maintaining a set point temperature in a conditioned space of a site.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the sensor network includes a first sensor positioned in or on the HVAC system.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the sensor network includes a second sensor positioned within the conditioned space of the site.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller is operable to perform a calibration operation that includes receiving a first temperature information from the first sensor; receiving a second temperature information from the second sensor; and based at least in part on the first temperature information and the second temperature information, determining an offset between the first temperature information and the second temperature information.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller includes a machine learning algorithm having a machine learning model of the site and the HVAC system, and the machine learning model is trained to perform a task that includes the calibration operation.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller is further operable to maintain the set point temperature in the conditioned space of the site based at least in part on the offset.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the first sensor positioned in or on the HVAC system includes the first sensor positioned within an air duct of the HVAC system.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller is operable to communicate with the user interface application through multiple communication paths that include a direct communication path between the controller and the user interface application; an indirect communication path through the controller, one or more intermediary elements, and the user interface application.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller unit also houses an alternative user interface application and a display that are operable to also receive and transmit the user inputs without going through the user interface application.

According to another embodiment, a method of operating a distributed thermostat includes using a controller to control operation of an HVAC system, where the controller is housed by a controller unit. The controller is operable to receive environmental information from a sensor network that is distributed from the controller unit; receive user inputs from a user interface application that is distributed from the controller unit; and control the operation of the HVAC system based at least in part on the environmental information and the user inputs.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the operation of the HVAC system includes maintaining a set point temperature in a conditioned space of a site.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the sensor network includes a first sensor positioned in or on the HVAC system.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the sensor network includes a second sensor positioned within the conditioned space of the site.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller performs a calibration operation that includes receiving a first temperature information from the first sensor; receiving a second temperature information from the second sensor; and based at least in part on the first temperature information and the second temperature information, determining an offset between the first temperature information and the second temperature information.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller includes a machine learning algorithm having a machine learning model of the site and the HVAC system; and the machine learning model is trained to perform a task comprising the calibration operation.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller maintaining the set point temperature in the conditioned space of the site is based at least in part on the offset.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the first sensor positioned in or on the HVAC system comprises the first sensor positioned within an air duct of the HVAC system.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller communicates with the user interface application through multiple communication paths that include a direct communication path between the controller and the user interface application; and an indirect communication path through the controller, one or more intermediary elements, and the user interface application.

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the controller unit also houses an alternative user interface application and a display that are operable to also receive and transmit the user inputs without going through the user interface application.

BRIEF DESCRIPTION OF THE DRAWINGS

The following descriptions should not be considered limiting in any way. With reference to the accompanying drawings, like elements are numbered alike:

FIG. 1 is a simplified block diagram of an exemplary distributed thermostat according to an embodiment;

FIG. 2 is a simplified block diagram of an exemplary heating, ventilation and air conditioning (HVAC) system having a distributed thermostat according to an embodiment;

FIG. 3 is a simplified block diagram of an exemplary HVAC system having a distributed thermostat according to an embodiment;

FIG. 4 is a flow diagram of an exemplary method according to an embodiment;

FIG. 5 is a block diagram illustrating how aspects of embodiments can be implemented using a classifier;

FIG. 6 is a block diagram of learning phase functionality that can be used to train the classifier shown in FIG. 5; and

FIG. 7 is a block diagram of a programmable computer system operable to implement aspects embodiments.

DETAILED DESCRIPTION

A detailed description of one or more embodiments of the disclosed systems and methods are presented herein by way of exemplification and not limitation with reference to the Figures.

Turning now to a more detailed description of aspects of the present disclosure, as depicted in FIG. 1, embodiments provide a distributed thermostat 100 operable to control operation of an HVAC system 200 without the need to mount a thermostat on the wall of a site. The term site can include, but is not limited to, office building, manufacturing locations, warehouse areas, or any types of areas that utilize heating, ventilation, or air conditioning. The thermostat 100 is “distributed” in that the three (3) major components of the thermostat 100 are not integrated into a single thermostat unit. In some embodiments, the three (3) major components of the thermostat 100 are housed in separate components that have separate physical locations. As depicted in FIG. 1, the three (3) major components of the distributed thermostat 100 include a controller 110, a user interface (UI) application 120, and a distributed sensor network 130.

The distributed thermostat 100 is a “smart” and \A % i-Fi enabled thermostat that can be used with home automation and is responsible for controlling a home's HVAC system (e.g., HVAC systems 200, 200A shown in FIGS. 2 and 3). The distributed thermostat 100 allows users to control the temperature of their home (i.e., a site) throughout the day using a schedule, but also contains additional features, such as Wi-Fi connectivity that enable the distributed components of the thermostat 100 to communicate directly with one another, or communicate with one another through the Internet. The UI application 120 can be loaded onto internee-connected devices, such as a laptop or smartphones, which allow users to adjust HVAC temperature settings (i.e., temperature set-points) of the HVAC system 200 by instructing the UI application 120 to send control signals to the controller 110.

In some embodiments, the controller 110 includes a set-point controller module 112, a sensor calibration module 114, and an alternative UI application module 116. The set-point controller module 112 contains control algorithms that are used to control operations of the HVAC system 200, 200A relevant to controlling when and how the HVAC system delivers heat and/or cooling to its associated site. The sensor calibration module 114 performs a methodology 400 (depicted in FIG. 4) that is used to develop offsets for the HVAC sensors 132 of the distributed sensor network 130. The alternative UI application module 116 is operable to function as an alternative to the UI application 120, which allows the HVAC system 200, 200A to be controlled if for some reason the UI application 120, which is loaded onto a mobile internet-connected device, is not at the site, and a person who is at the site (e.g., a visiting relative) needs to enter user controls to the controller 110. In embodiments of the disclosure, the alternative UI module 116 can include full functionality of the UI application 120, or can include a more basic set of functionality (i.e., less than full functionality of the UI application 120) that is appropriate for a guest. For example, the alternative UI module 116 can be configured to not allow access to (or password protect) heating/cooling schedules set up by an occupant/user through the UI application 120. In some embodiments, the controller 110 is housed within a control unit (not shown separately from the controller 110) that can be mounted directly to an outer wall of the HVAC system 200, 200A. In some embodiments, the controller 110 is housed within a control unit (not shown separately from the controller 110) that can be mounted directly to a wall of the site near the HVAC system 200, 200A. In some embodiments, the controller 110 can be integrated with a motherboard controller (not shown) of the HVAC system 200, 200A, and the motherboard controller can be coupled to a display (not shown) that can be mounted directly to an outer wall of the HVAC system 200, 200A. The mounted display is operable to provide user access to the alternative UI module 116.

In embodiments, the distributed sensor network 130 includes HVAC sensors 132 and room sensors 134. In embodiments of the disclosure, the HVAC sensors 132 can be positioned at selected locations of the HVAC system 200, 200A, including but not limited to within or on a supply or return air duct; and/or within or on an indoor device of the HVAC system 200, 200A. In some embodiments, the room sensors 134 are positioned in one or more rooms of the site that is serviced by the HVAC system 200, 200A. In some embodiments, the room sensors 134 can provide direct room temperature feedback to the controller 110 for performing set-point control operations. In some embodiments, in addition a desire to not mount a traditional non-distributed thermostat to walls (e.g., for aesthetic reasons), users can desire to also not mount the room sensors 134 to walls (e.g., for aesthetic reasons). In such situations, the HVAC sensors 132, which are mounted in or one the HVAC system 200, 200A, are used to provide the primary temperature and humidity feedback to the controller 110 for performing set-point control operations. The sensor calibration module 114 is used to calibrate the HVAC sensors 132 and determine offsets that are applied to the outputs of the HVAC sensors 132 that are used by the controller 110 to perform set-point temperature control operations. In some embodiments, the room sensors 134 used by the sensor calibration module 114 are portable (i.e., not permanently mounted) and can be put away when the operations performed by the server calibration module 114 (and the methodology 400 shown in FIG. 4) are completed.

The cloud computing system 102 can be in wired or wireless electronic communication with one or all of the components of the distributed thermostat 100. Cloud computing system 102 can supplement, support, or replace some or all of the functionality of the components of the distributed thermostat 100. Additionally, some or all of the functionality of the components of the distributed thermostat 100 can be implemented as a node of the cloud computing system 102.

FIG. 2 depicts the distributed thermostat 100 coupled with the HVAC system 200. Embodiments of the disclosure can be applied to a wide variety of technologies for heating, and cooling applications, including but not limited to evaporative cooling, convective cooling, or solid state cooling such as electrothermic cooling. One of the most prevalent cooling technologies in use for residential and commercial refrigeration and air conditioning is the vapor compression refrigerant heat transfer loop.

The HVAC system 200 is depicted in FIG. 1 as a furnace coil or fan coil unit 200. Although described herein as furnace or fan coil unit it should be appreciated that the HVAC system 100 can be any heating or cooling system. As shown, the furnace coil or fan coil unit 200 includes a cabinet or housing duct 202 within which various components of the HVAC system are located. For example, housed within the cabinet 202 of the furnace coil or fan coil unit 200 is a heat exchanger assembly 204 operable to heat and/or cool the adjacent air. A blower or fan assembly 206 can also he arranged within the cabinet 202 or alternatively, at a position outside of but in fluid communication with the cabinet 202. The blower 206 is operable to circulate a flow of air A through the interior of the cabinet 202, across the heat exchanger assembly 204. Depending on the desired characteristics of the furnace coil or fan coil unit 200, the blower 206 can be positioned either downstream with respect to the heat exchanger assembly 204 (i.e., a “draw through” configuration), or upstream with respect to the heat exchanger assembly 204 (i.e., a “blow through” configuration), as shown in FIG. 2.

The heat exchanger assembly 204 is part of a closed loop refrigeration circuit through which refrigeration (not shown separately from the heat exchanger assembly 204) flows. The heat exchanger assembly 204 can include any of a plurality of configurations. As illustrated in FIG. 2, the heat exchanger assembly 204 includes one or more heat exchanger coils 208, which can be arranged in a non-linear configuration. For example, the heat exchanger assembly 204 can have a generally V-shaped configuration, a generally A-shaped configuration, or a generally N-shaped configuration, or any other suitable configuration as is known in the art. In other embodiments, the heat exchanger assembly 204 can include a single heat exchanger coil 208 arranged at an angle with respect to the flow path of air A through the cabinet 202. In embodiments where the furnace coil or fan coil unit 200 is operable to provide cool air, the heat exchanger assembly 204 absorbs heat from the air A passing through the heat exchanger assembly 204 and the resultant cool air A is provided to a space to be conditioned. It should be understood that the refrigeration system illustrated herein is intended as an example only and that a HVAC system 200 having any suitable configuration is within the scope of the disclosure.

FIG. 3 depicts a distributed thermostat 100A coupled with an HVAC system 200A. The HVAC system 200A is a more detailed implementation of the HVAC system 200 (shown in FIG. 2), and the distributed thermostat 100A is a more detailed implementation of the distributed thermostat 100 (shown in FIGS. 1 and 2). As shown in FIG. 3, the distributed thermostat 100A includes a mobile smartphone 310, a controller 110A and a distributed network of HVAC sensors 132A configured and arranged as shown. The smartphone 310 houses a UI application 120A, which has substantially the same functionality as the UI application 120. The HVAC system 200A includes an outdoor device 308, an indoor device 302, a return air duct 306, and a supply air duct 304, configured and arranged as shown. Multiple instance of the HVAC sensors 132A are provided, including instances of the HVAC sensors 132A at or in the supply air duct 304, at or in the return air duct 306, and at or in the indoor device 302.

FIG. 4 is a flow diagram of an exemplary methodology 400 according to an embodiment. The methodology 400 is performed by the distributed thermostat 100, 100A (shown in FIGS. 1, 2 and 3), and more specifically by the controller 110, 110A (shown in FIGS. 1, 2 and 3) of the distributed thermostat 100, 100A. In some embodiments, the methodology 400 is implemented using one or more algorithms of the controller 110, 110A. The methodology 400 starts at block 402 then moves to block 404 where a temperature set-point is selected/updated. In embodiments of the disclosure, the methodology 400 is operable to calibrate the temperature readings from the HVAC sensors 132, 132A with the temperature readings from the room sensors 132 such that the temperature readings from the HVAC sensors 132, 132A can be offset (up or down) to accurately represent the temperature in the conditioned space being serviced by the HVAC system 200, 200A. Accordingly, in some embodiments of the disclosure, the methodology 400 can be performed automatically by initially selecting a set-point temperature at block 404 then continuing to perform iterations of the methodology 400 until all of the available temperature set-points have been calibrated. In some embodiments of the disclosure, the set-point temperatures calibrated by the methodology 400 can be limited to a range (e.g., between 68 degrees Fahrenheit and 80 degrees Fahrenheit) that is expected to be selected by the users of the HVAC system 200, 200A.

At block 406, the methodology 400 computes differences between readings from the HVAC sensors and the room sensors while operating the HVAC system to reach and maintain the set-point temperature selected at block 404. In some embodiments, the set-point temperature selected at block 404 is selected to have a wide separation (e.g., at least 10 degrees Fahrenheit, up or down) from the immediately preceding set-point temperature to allow multiple HVAC sensor readings and room temperature readings at block 406. The output from block 406 is provided to block 408, along with outputs from blocks 410 and 412. Block 410 accesses and accumulates static data of the HVAC system (e.g., capacity of the HVAC system) and dynamic data of the HVAC system (e.g., a variety of HVAC operating parameters) during runtime of the operations at block 406. Block 412 accesses and accumulates data of the site that is being serviced by the HVAC system (e.g., weather, square footage of the site, occupancy, seasonal climate, etc.) during runtime of the operations at block 406.

At block 408, the methodology 400 uses the outputs from blocks 406, 410, and 412 to compute and/or predict offsets for the HVAC sensors when taking the HVAC system to the selected/updated temperature set-point. In some embodiments, the operations at block 408 can be performed using a machine learning algorithm and a machine learning model (e.g., machine learning algorithm 512 and machine learning model 516 shown in FIG. 5) trained to perform the task of predicting offsets for the HVAC sensors when taking the HVAC system to the selected/updated temperature set-point. In some embodiments, the machine learning model is trained to include a model of the HVAC system, a model of the HVAC sensors, a model of the room sensors, and a model of the site (in any combination). In embodiments, block 408 continues to predict offsets until the confidence level (CL) of the offset predictions exceed a threshold (TH). In aspects of the embodiments, the machine learning algorithms 512 and the machine learning models 516 can be configured to apply CLs to various ones of their results/determinations in order to improve the overall accuracy of the particular result/determination. When the machine learning algorithms 512 and/or the machine learning models 516 make a determination or generate a result for which the value of CL is below a predetermined TH (i.e., CL<TH), the result/determination can be classified as having sufficiently low “confidence” to justify a conclusion that the determination/result is not valid, and this conclusion can be used to determine when, how, and/or if the determinations/results are handled in downstream processing. If CL>TH, the determination/result can be considered valid, and this conclusion can be used to determine when, how, and/or if the determinations/results are handled in downstream processing. Many different predetermined TH levels can be provided. The determinations/results with CL>TH can be ranked from the highest CL>TH to the lowest CL>TH in order to prioritize when, how, and/or if the determinations/results are handled in downstream processing. Many different predetermined TH levels can be provided such that the results 520 with CL>TH can be ranked from the highest CL>TH to the lowest CL>TH.

After the operations at block 408 are completed, the methodology 408 moves to decision block 414 to determine whether there are more temperature set-point to evaluate. If the answer to the inquiry at decision block 414 is yes, the methodology 400 returns to block 404 to select a next temperature set-point and perform another iteration (or additional iterations) of the methodology 400 for the next temperature set-point. If the answer to the inquiry at decision block 414 is no, the methodology 400 move to block 416 and ends.

Additional details of machine learning techniques that can be used to implement functionality of the controller 110, 110A will now be provided. The various classification, prediction and/or determination functionality of the controllers or processors described herein can be implemented using machine learning and/or natural language processing techniques. In general, machine learning techniques are run on so-called “learning machines,” which can be implemented as programmable computers operable to run sets of machine learning algorithms and/or natural language processing algorithms. Machine learning algorithms incorporate knowledge from a variety of disciplines, including neurophysiology, cognitive science/psychology, physics (statistical mechanics), control theory, computer science, artificial intelligence, statistics/mathematics, pattern recognition, computer vision, parallel processing and hardware (e.g., digital/analog/VLSI/optical).

The basic function of learning machines and their machine learning algorithms is to recognize patterns by interpreting unstructured sensor data through a kind of machine perception. Unstructured real-world data in its native form (e.g., images, sound, text, or time series data) is converted to a numerical form (e.g., a vector having magnitude and direction) that can be understood and manipulated by a computer. The machine learning algorithm performs multiple iterations of learning-based analysis on the real-world data vectors until patterns (or relationships) contained in the real-world data vectors are uncovered and learned. The learned patterns/relationships function as predictive models that can be used to perform a variety of tasks, including, for example, classification (or labeling) of real-world data and clustering of real-world data. Classification tasks often depend on the use of labeled datasets to train the classifier (i.e., the model) to recognize the correlation between labels and data. This is known as supervised learning. Examples of classification tasks include identifying objects in images (e.g., stop signs, pedestrians, lane markers, etc.), recognizing gestures in video, detecting voices, detecting voices in audio, identifying particular speakers, transcribing speech into text, and the like. Clustering tasks identify similarities between objects, which the clustering task groups according to those characteristics in common and which differentiate them from other groups of objects. These groups are known as “clusters.”

An example of machine learning techniques that can be used to implement embodiments of the disclosure will be described with reference to FIGS. 5 and 6. FIG. 5 depicts a block diagram showing a classifier system 500 capable of implementing various predicting and determining aspects of the embodiments described herein. More specifically, the functionality of the system 500 is used in embodiments of the disclosure to generate various models and/or sub-models that can be used to implement predicting and determining functionality in embodiments of the disclosure. The classifier system 500 includes multiple data sources 502 in communication (e.g., through a network 504) with a classifier 510. In some embodiments of the disclosure, the data sources 502 can bypass the network 504 and feed directly into the classifier 510. The data sources 502 provide data/information inputs that will be evaluated by the classifier 510 in accordance with embodiments of the disclosure. The data sources 502 also provide data/information inputs that can be used by the classifier 510 to train and/or update model(s) 516 created by the classifier 510. The data sources 502 can be implemented as a wide variety of data sources, including but not limited to, sensors operable to gather real time data, data repositories (including training data repositories), and outputs from other classifiers. The network 504 can be any type of communications network, including but not limited to local networks, wide area networks, private networks, the Internet, and the like.

The classifier 510 can be implemented as algorithms executed by a programmable computer such as the computing system 700 (shown in FIG. 7). As shown in FIG. 5, the classifier 510 includes a suite of machine learning (ML) algorithms 512; and model(s) 516 that are relationship (or prediction) algorithms generated (or learned) by the ML algorithms 512. The algorithms 512, 516 of the classifier 510 are depicted separately for ease of illustration and explanation. In embodiments of the disclosure, the functions performed by the various algorithms 512, 516 of the classifier 510 can be distributed differently than shown. In some embodiments of the disclosure, natural language processing (NLP) algorithms can be integrated within the ML algorithms 512.

Referring now to FIGS. 5 and 6 collectively, FIG. 6 depicts an example of a learning phase 600 performed by the ML algorithms 512 to generate the above-described models 516. In the learning phase 600, the classifier 510 extracts features from the training data and coverts the features to vector representations that can be recognized and analyzed by the ML algorithms 512. The features vectors are analyzed by the ML algorithm 512 to “classify” the training data against the target model (or the model's task) and uncover relationships between and among the classified training data. Examples of suitable implementations of the ML algorithms 512 include but are not limited to neural networks, support vector machines (SVMs), logistic regression, decision trees, hidden Markov Models (HMMs), etc. The learning or training performed by the ML algorithms 512 can be supervised, unsupervised, or a hybrid that includes aspects of supervised and unsupervised learning. Supervised learning is when training data is already available and classified/labeled. Unsupervised learning is when training data is not classified/labeled so must be developed through iterations of the classifier 510 and the ML algorithms 512. Unsupervised learning can utilize additional learning/training methods including, for example, clustering, anomaly detection, neural networks, deep learning, and the like.

When the models 516 are sufficiently trained by the ML algorithms 512, the data sources 502 that generate “real world” data are accessed, and the “real world” data is applied to the models 516 to generate usable versions of the results 520. In some embodiments of the disclosure, the results 520 can be fed back to the classifier 510 and used by the ML algorithms 512 as additional training data for updating and/or refining the models 516.

FIG. 7 illustrates an example of a computer system 700 that can be used to implement the controller 120 described herein. The computer system 700 includes an exemplary computing device (“computer”) 702 configured for performing various aspects of the content-based semantic monitoring operations described herein in accordance embodiments of the disclosure. In addition to computer 702, exemplary computer system 700 includes network 714, which connects computer 702 to additional systems (not depicted) and can 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 702 and additional system are in communication via network 714, e.g., to communicate data between them.

Exemplary computer 702 includes processor cores 704, main memory (“memory”) 710, and input/output component(s) 712, which are in communication via bus 703. Processor cores 704 includes cache memory (“cache”) 706 and controls 708, which include branch prediction structures and associated search, hit, detect and update logic, which will be described in more detail below. Cache 706 can include multiple cache levels (not depicted) that are on or off-chip from processor 704. Memory 710 can include various data stored therein, e.g., instructions, software, routines, etc., which, e.g., can be transferred to/from cache 706 by controls 708 for execution by processor 704. Input/output component(s) 712 can include one or more components that facilitate local and/or remote input/output operations to/from computer 702, such as a display, keyboard, modem, network adapter, etc. (not depicted).

Embodiments of the disclosure described herein can be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a controller or processor to carry out aspects of the embodiments of the disclosure.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage 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 disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove 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 being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

The term “about” is intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present 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.

While the present disclosure has been described with reference to an exemplary embodiment or 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 present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from the essential scope thereof. Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this present disclosure, but that the present disclosure will include all embodiments falling within the scope of the claims.

Claims

1. A distributed thermostat comprising:

a controller unit that houses a controller that is operable to control operation of a heating, ventilation, and air conditioning (HVAC) system;

wherein the controller is further operable to receive environmental information from a sensor network that is distributed from the controller unit;

wherein the controller is further operable to receive user inputs from a user interface application that is distributed from the controller unit; and

wherein the controller is further operable to control the operation of the HVAC system based at least in part on the environmental information and the user inputs.

2. The distributed thermostat of claim 1, wherein the operation of the HVAC system comprises maintaining a set point temperature in a conditioned space of a site.

3. The distributed thermostat of claim 2, wherein the sensor network comprises a first sensor positioned in or on the HVAC system.

4. The distributed thermostat of claim 3, wherein the sensor network comprises a second sensor positioned within the conditioned space of the site.

5. The distributed thermostat of claim 4, wherein the controller is operable to perform a calibration operation comprising:

receiving a first temperature information from the first sensor;

receiving a second temperature information from the second sensor; and

based at least in part on the first temperature information and the second temperature information, determining an offset between the first temperature information and the second temperature information.

6. The distributed thermostat of claim 5, wherein:

the controller comprises a machine learning algorithm having a machine learning model of the site and the HVAC system; and

the machine learning model is trained to perform a task comprising the calibration operation.

7. The distributed thermostat of claim 6, wherein the controller is further operable to maintain the set point temperature in the conditioned space of the site based at least in part on the offset.

8. The distributed thermostat of claim 5, wherein the first sensor positioned in or on the HVAC system comprises the first sensor positioned within an air duct of the HVAC system.

9. The distributed thermostat of claim 1, wherein the controller is operable to communicate with the user interface application through multiple communication paths comprising:

a direct communication path between the controller and the user interface application; and

an indirect communication path through the controller, one or more intermediary elements, and the user interface application.

10. The distributed thermostat of claim 1, wherein the controller unit also houses an alternative user interface application and a display that are operable to also receive and transmit the user inputs without going through the user interface application.

11. A method of operating a distributed thermostat, the method comprising:

using a controller to control operation of a heating, ventilation, and air conditioning (HVAC) system;

wherein the controller is housed by a controller unit;

wherein the controller is operable to receive environmental information from a sensor network that is distributed from the controller unit;

wherein the controller is further operable to receive user inputs from a user interface application that is distributed from the controller unit; and

using the controller to control the operation of the HVAC system based at least in part on the environmental information and the user inputs.

12. The method of claim 11, wherein the operation of the HVAC system comprises maintaining a set point temperature in a conditioned space of a site.

13. The method of claim 12, wherein the sensor network comprises a first sensor positioned in or on the HVAC system.

14. The method of claim 13, wherein the sensor network comprises a second sensor positioned within the conditioned space of the site.

15. The method of claim 14, wherein the controller performs a calibration operation comprising:

receiving a first temperature information from the first sensor;

receiving a second temperature information from the second sensor; and

based at least in part on the first temperature information and the second temperature information, determining an offset between the first temperature information and the second temperature information.

16. The method of claim 15, wherein:

the controller comprises a machine learning algorithm having a machine learning model of the site and the HVAC system; and

the machine learning model is trained to perform a task comprising the calibration operation.

17. The method of claim 16, wherein the controller maintaining the set point temperature in the conditioned space of the site is based at least in part on the offset.

18. The method of claim 15, wherein the first sensor positioned in or on the HVAC system comprises the first sensor positioned within an air duct of the HVAC system.

19. The method of claim 11, wherein the controller communicates with the user interface application through multiple communication paths comprising:

a direct communication path between the controller and the user interface application; and

an indirect communication path through the controller, one or more intermediary elements, and the user interface application.

20. The method of claim 11, wherein the controller unit also houses an alternative user interface application and a display that are operable to also receive and transmit the user inputs without going through the user interface application.