Climate control systems and methods for energy management and real-world environmental controls

Abstract

Disclosed are systems and methods that provide a novel system for calculating R-value losses and air infiltration based on measurements obtained by components of a climate control system for a location (e.g., HVAC). The system includes a feedback loop configured to adjust one or more climate control components in response to calculated changes in R-value and / or air. Some embodiments include advanced analytics configured to predict changes in power loss. R-value, and / or air infiltration using artificial intelligence. The disclosed system provides non-native functionality for predicting one or more variables of interest, which can trigger automated, proactive decisions related to climate control components that can mitigate efficiency losses.

Description

CLIMATE CONTROL SYSTEMS AND METHODS FOR ENERGY MANAGEMENTAND REAL-WORLD ENVIRONMENTAL CONTROLSFIELD OF THE DISCLOSURE

[0001] This application claims the benefit of, and priority to, U.S. Provisional Patent Application No. 63 / 518,932, filed August 11, 2023, its entirety of which is incorporated herein by reference.FIELD OF THE DISCLOSURE

[0002] The present disclosure is generally related to climate control systems, and more particularly, to a decision intelligence (Dl)-based computerized framework for controlling one or more climate control components based in response to calculated R-value losses and air infiltration.BACKGROUND

[0003] Air infiltration in the context of a HVAC (Heating, Ventilation, and Air Conditioning) system refers to the unintended or accidental leakage of outside air into a home's conditioned living space. This can occur through various points of entry, such as cracks, gaps, or openings in windows, doors, walls, ceilings, or floors. Infiltration can lead to energy wastage, as the HVAC system has to work harder to maintain the desired indoor temperature.

[0004] Total energy use on planet Earth was about 604 Quadrillion Btu (quads) in 2021. The United States used about 98 quads or 16% of this value. Of the 98 quads, US homes used about 12 quads of energy, or about 12% of the total US usage. Of the 12 quads used for US residential, about 50% is for heating and cooling: -13,000 pounds of CO2 per US home. Studies show air infiltration can account for 20-40% loss in heating or cooling energy in a home. The carbon footprint can be 3,000 - 5,000 pounds of CO2 per US home from the air infiltration loss, which requires - 90 full grown trees per home to offset.SUMMARY OF THE DISCLOSURE

[0005] Systems and methods described herein according to some embodiments are directed to determining an R-value and a leakage area for a location, as w ell as using these values to build expected power losses for a climate control system for given temperatures, and / or comparing these expected values to sensed and / or predicted values to generate control instructions for mitigation. A ‘“location,” as used herein, includes any enclosed structure such as a home, office,building and / or any other definable location and / or structure for which is fitted with a climate control system).

[0006] Stack induced air infiltration includes air that leaks into or out of a location (e.g., home) due to temperature induced pressure differences from the stack effect. The stack effect, also known as the chimney effect, occurs when indoor air moves in and out of a location due to differences in temperature. Hot air is lighter and rises, creating a kind of "stack" effect. As the hot air escapes from the upper parts of the location, cooler outdoor air is draw n in through lower openings or gaps to replace it. Under stable conditions (e g., steady temperature and / or wind conditions), the climate control system described herein is configured to generate R- values and / or leakage area values for a location without the use of conventional equipment such as blower doors and / or thermal cameras.

[0007] Wind-induced air infiltration happens when outdoor air enters a location through small openings or cracks because of the wind's force. The wind pushes air into the location on one side and pulls it out on the other side. This is a non-limiting example of a variable that can affect power consumption, as more air is being drawn through the leakage area than under stable conditions.

[0008] Energy losses occur when heating, cooling, humidifying, and / or dehumidifying infiltration air. Losses may also occur from filtering particles from infiltrated outside air when managing Indoor Air Quality (IAQ) levels. In addition, a high level of wind induced turbulence pressure inside the location creates subconscious discomfort. The disclosed climate control system includes and / or is configured to manipulate one or more mechanical components associated with controlling these variables as further described herein.

[0009] Conventional methods to mitigate infiltration and improve energy efficiency include taking measures such as weather-stripping doors and windows, sealing gaps and cracks, and ensuring proper insulation. Regular maintenance of the HVAC system and conducting an energy audit can also help identify and address infiltration issues, leading to a more comfortable and energy-efficient location.

[0010] Traditional means of detecting and correcting infiltration include using a blower door, which is used to pressurize or depressurize a structure to find the leakage area and leaks. Leakage area determined from collecting a pressure differential curve vs. blower door air flow rate. Leaks are located by using a smoke stick to find the leakage areas. Other techniques include using infrared cameras to find the leaks by imaging signatures from outside air leaking into a depressurized building. However, the cost, complexity, and energy required to conduct these detection methods, tied with a lack of functionality for controlling air infiltration withina home after construction, are two examples, among others, that evidence the technical shortcomings in existing systems. Therefore, there is a need in the art for an air infiltration system that can use the components of a smart heating, ventilation, and air conditioning (sHVAC) system, as well as air exchange systems and humidifying systems, as non-limiting examples.

[0011] To that end, according to some embodiments, the disclosed systems and methods provide a novel computerized framework that addresses current shortcomings in the field by providing a climate control system that can calculate, predict, and / or or control air infiltration within a home. In some embodiments, the climate control system is configured to calculate an R-value and / or a leakage area for a home without the need for traditional equipment. In some embodiments, these include, inter alia, heat or cooling power (P) as well as inside temperature (Ti). Some embodiments described herein include HVAC components that include sensors for measuring pressure and / or temperature inside and / or outside the home. In some embodiments, the climate control system is configured to obtain information for variables outside the home (e.g., wind speed, wind direction, outside temperature, outside humidity, outside air quality) via a network connection and / or APIs configured to access databases with past, current, and / or future predictions for one or more variables described herein. Solving for known, obtained, and or calculated variables enables the climate control system to determine R-values and / or leakage area for a range of temperatures and power losses.

[0012] With all variables of interest known, using the mathematical models further described herein with reference to at least FIG. 2, FIG. 3 and FIG. 4, the climate control system is configured to determine R-value and / or air infiltration losses. Some embodiments provide a control engine that can execute an efficiency balance configured to change one or more HVAC component parameters to minimize power consumption realized by dynamic losses, which includes losses from environmental and / or structural changes at a location, thereby integrating the model into a practical application.

[0013] To ensure a continuous supply of fresh air while controlling air infiltration, locations often use mechanical ventilation systems. These systems bring in outdoor air through controlled vents, filters it. and / or distribute it throughout the location. Some embodiments described herein include heat recovery ventilation (HRV) or energy recovery ventilation (ERV) or air to air heat exchanger (AAHX) systems configured to pre-heat or pre-cool the incoming air, thus improving energy efficiency. Using one or more climate control system components, in some embodiments, the climate control system described herein is configured to ensure the house is properly balanced in terms of indoor air pressure to prevent excessive air infiltration orexfiltration, thereby maintaining the right pressure differentials to minimize air leaks. Some non-limiting example control actions executed by the climate control system according to some embodiments include mechanical ventilation control and / or pressure balancing.

[0014] In some embodiments, the climate control system includes a feedback loop configured to adjust one or more climate control system components in response to calculated changes in the environment and / or structure by comparing actual power losses to expected losses for a given R-value and / or leakage area. However, some embodiments include advanced analytics configured to predict the effect of changes in environment and / or structure using artificial intelligence (Al) as further described below. Accordingly, as discussed herein, the disclosed framework provides non-native functionality' to a device (and / or devices) associated with, part of and / or operating in-line with a climate control system, as discussed herein, via automated, proactive decisions related to components that can utilized to control and / or to mitigate efficiency losses.

[0015] For example, as discussed below, program applications can be associated with the climate control system described herein to provide predicted temperature, humidity, pressure, wind speed, wind direction, air quality, and / or any other conventional environmental related phenomenon

[0016] In a ty pical urban or suburban area, barometric pressure can change multiple times per day due to the passage of weather fronts, the movement of high and low -pressure systems, and local weather phenomena. In some embodiments, the framework can receive predictions for environmental conditions, such as barometric pressure, as a non-limiting example, and adjust one or more components before the barometric environmental changes affect the efficiency of the climate control system. For example, one or more vents can be adjusted before a pressure change occurs, and / or one or more pressure balances within a home can be executed in response to an expected change in wind direction and / or speed. In some embodiments, temperatures and / or pressures of different areas / spaces within a location can be adjusted proactively by the climate control system to obtain less power loss than what would be required in response to a near real-time feedback from the actual sensed change.

[0017] Advantageously, in some embodiments, the climate control system is configured to use one or more analytical techniques, which may include AI / ML, to determine the expected climate control system response for a given mechanical configuration to a change in one or more variables. For example, the climate control system may determine optimum setting for one or more components, and / or may generate electronic messages which may include instructions for improving efficiency, which are further described herein.

[0018] According to some embodiments, a method is disclosed for a Dl-based computerized climate control system for deterministically controlling and / or managing components and / or devices (e.g., computer devices). In accordance with some embodiments, the present disclosure provides one or more computers comprising one or more non-transitory computer-readable storage medium for carrying out the above-mentioned technical steps of the climate control system’s functionality. The non-transitory computer-readable storage medium has tangibly stored thereon, or tangibly encoded thereon, computer readable instructions that when executed by a device cause at least one processor to perform a method for controlling and / or managing device and / or component settings and / or electronic messages.

[0019] In accordance with one or more embodiments, a climate control system is provided that includes one or more processors and / or computing devices configured to provide functionality in accordance with some embodiments. In some embodiments, functionality is embodied in steps of a method performed by at least one computing device. In accordance with one or more embodiments, program code (or program logic) executed by a processor(s) of a computing device to implement functionality in accordance with one or more such embodiments are embodied in. by and / or on one or more non-transitory computer-readable media.DESCRIPTIONS OF THE DRAWINGS

[0020] The features, and advantages of the disclosure will be apparent from the following description of embodiments as illustrated in the accompanying drawings, in which reference characters refer to the same parts throughout the various views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating principles of the disclosure:

[0021] FIG. 1 is a block diagram of an example configuration within which the systems and methods disclosed herein could be implemented according to some embodiments of the present disclosure;

[0022] FIG. 2 is a block diagram illustrating components of an exemplary7control engine according to some embodiments of the present disclosure;

[0023] FIG. 3 illustrates an example configuration workflow for training and / or implementing an Al model according to some embodiments of the present disclosure;

[0024] FIG. 4 illustrates an example control engine execution workflow according to some embodiments of the present disclosure;

[0025] FIG. 5 depicts an example implementation of a cloud architecture according to some embodiments of the present disclosure;

[0026] FIG. 6 depicts an example implementation of a web architecture according to some embodiments of the present disclosure;

[0027] FIG. 7 is a block diagram illustrating a computing device showing an example of a client and / or server device used in various embodiments of the present disclosure;

[0028] FIG. 8 illustrates the concept of wind induced air infiltration and stack induced air infiltration according to some embodiments of the present disclosure;

[0029] FIG. 9 shows mathematical manipulation and input variables that enable the disclosed framework to solve for a leakage area according to some embodiments of the present disclosure;

[0030] FIG. 10 depicts a Bernoulli simplification for flowrate according to some embodiments of the present disclosure;

[0031] FIG. 11 depicts a mathematical model that allows for the framework to solve for an R- value for a location according to some embodiments of the present disclosure;

[0032] FIG. 12 shows a system executed mathematical model for determining combined power loss from R-value losses and leakage area losses according to some embodiments of the present disclosure;

[0033] FIG. 13 shows an algorithm for initially determining an R-value and leakage area for a location according to some embodiments of the present disclosure; and

[0034] FIG. 14 depicts a table of expected power losses for a given leakage area, R-value, and combined R-value and leakage area for a location according to some embodiments of the present disclosure.DETAILED DESCRIPTION

[0035] The present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, which form apart hereof, and which show, by way of non-limiting illustration, certain example embodiments. Subject matter may, however, be embodied in a variety of different forms and, therefore, covered or claimed subject matter is intended to be construed as not being limited to any example embodiments set forth herein; example embodiments are provided merely to be illustrative. Likewise, a reasonably broad scope for claimed or covered subject matter is intended. Among other things, for example, subject matter may be embodied as methods, devices, components, or systems. Accordingly, embodiments may, for example, take the form of hardware, softw are, firmware or any combination thereof (other than software per se). The following detailed description is. therefore, not intended to be taken in a limiting sense.

[0036] Throughout the specification and claims, terms may have nuanced meanings suggested or implied in context beyond an explicitly stated meaning. Likewise, the phrase "‘in one embodiment” as used herein does not necessarily refer to the same embodiment and the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment. It is intended, for example, that claimed subject matter include combinations of example embodiments in whole or in part, according to some embodiments.

[0037] In general, terminology may be understood at least in part from usage in context. For example, terms, such as “and”, “or”, or “and / or,” as used herein may include a variety of meanings that may depend at least in part upon the context in which such terms are used. Typically, “or” if used to associate a list, such as A, B or C, is intended to mean A, B, and C, here used in the inclusive sense, as well as A. B or C, here used in the exclusive sense. In addition, the term “one or more” as used herein, depending at least in part upon context, may be used to describe any feature, structure, or characteristic in a singular sense or may be used to describe combinations of features, structures or characteristics in a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again, may be understood to convey a singular usage or to convey a plural usage, depending at least in part upon context. In addition, the term “based on” may be understood as not necessarily intended to convey an exclusive set of factors and may, instead, allow for existence of additional factors not necessarily expressly described, again, depending at least in part on context.

[0001] As used herein, “can” or “may” or derivations there of (e.g., the system display can show X) are used for descriptive purposes only and is understood to be synonymous and / or interchangeable with “configured to” (e.g., the computer is configured to execute instructions X) when defining the metes and bounds of the climate control system. The phrase “configured to” also denotes the step of configuring a structure or computer to execute a function according to some embodiments.

[0038] The present disclosure is described below with reference to block diagrams and operational illustrations of methods and devices. It is understood that each block of the block diagrams or operational illustrations, and combinations of blocks in the block diagrams or operational illustrations, represent portions of an algorithm that can be implemented by means of analog or digital hardware and computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer to alter its function as detailed herein, a special purpose computer, ASIC, or other programmable data processing apparatus, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the functions / acts specified in the blockdiagrams or operational block or blocks. In some alternate implementations, the functions / acts noted in the blocks can occur out of the order noted in the operational illustrations. For example, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality / acts involved.

[0039] For the purposes of this disclosure a non-transitory computer readable medium (or computer-readable storage medium / media) stores computer data, which data can include computer program code (or computer-executable instructions) that is executable by a computer, in machine readable form. By way of example, and not limitation, a computer readable medium may include computer readable storage media, for tangible or fixed storage of data, or may include communication media for transient interpretation of code-containing signals. Computer readable storage media, as used herein, refers to physical or tangible storage (as opposed to signals) and includes without limitation volatile and non-volatile, removable and non-removable media implemented in any method or technology for the tangible storage of information such as computer-readable instructions, data structures, program modules or other data. Computer readable storage media includes, but is not limited to, RAM. ROM. EPROM. EEPROM, flash memory or other solid state memory technology, optical storage, cloud storage, magnetic storage devices, or any other physical or material medium which can be used to tangibly store the desired information or data or instructions and which can be accessed by a computer or processor.

[0040] For the purposes of this disclosure the term ‘"server” should be understood to refer to a sendee point which provides processing, database, and communication facilities. By way of example, and not limitation, the term “server” can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. Cloud servers are examples.

[0041] For the purposes of this disclosure a “network” should be understood to refer to a network that may couple devices so that communications may be exchanged, such as between a server and a client device or other types of devices, including between wireless devices coupled via a wireless network, for example. A network may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), a content delivery network (CDN) or other forms of computer or machine-readable media, for example. A network may include the Internet, one or more local area networks (LANs), one or more wide area networks(WANs), wire-line ty pe connections, wireless type connections, cellular or any combination thereof. Likewise, sub-networks, which may employ differing architectures or may be compliant or compatible with differing protocols, may interoperate within a larger network.

[0042] For purposes of this disclosure, a “wireless network’" should be understood to couple client devices with a network. A wireless network may employ stand-alone ad-hoc networks, mesh networks. Wireless LAN (WLAN) networks, cellular networks, or the like. A wireless network may further employ a plurality of network access technologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, Wireless Router mesh, or 2nd, 3rd, 4thor 5thgeneration (2G, 3G, 4G or 5G) cellular technology, mobile edge computing (MEC), Bluetooth, 802.1 Ib / g / n, or the like. Network access technologies may enable wide area coverage for devices, such as client devices with varying degrees of mobility, for example.

[0043] In short, a wireless network may include virtually any ty pe of wireless communication mechanism by which signals may be communicated between devices, such as a client device or a computing device, between or within a network, or the like

[0044] A computing device, which may include one or more computers, may be capable of sending or receiving signals, such as via a wired or wireless network, or may be capable of processing or storing signals, such as in memory as physical memory states, and may, therefore, operate as a server. Thus, devices capable of operating as a server may include, as examples, dedicated rack-mounted servers, desktop computers, laptop computers, set top boxes, integrated devices combining various features, such as two or more features of the foregoing devices, or the like.

[0045] For purposes of this disclosure, a client (or user, entity’, subscriber or customer) device may include a computing device capable of sending or receiving signals, such as via a wired or a wireless network. A client device may, for example, include a desktop computer or a portable device, such as a cellular telephone, a smart phone, a display pager, a radio frequency (RF) device, an infrared (IR) device a Near Field Communication (NFC) device, a Personal Digital Assistant (PDA), a handheld computer, a tablet computer, a phablet, a laptop computer, a set top box, a wearable computer, smart watch, an integrated or distributed device combining various features, such as features of the forgoing devices, or the like. In some embodiments, a client can include one or more components of a climate control system and / or any system described herein; some non-limiting examples of which include computer controlled dampers, thermometers, vents, heat exchangers, air exchanges, humidifiers, dehumidifiers, fans, blowers, smart electrical meters, compressors, and / or any component of a conventional climatecontrol system that is configured to send and / or receive signals and / or provide automated mechanical component actuation. A client device may vary in terms of capabilities or features. Claimed subject matter is intended to cover a wide range of potential variations, such as a web- enabled client device. Clients may include a high-resolution screen (HD or 4K for example), one or more physical or virtual keyboards, mass storage, one or more accelerometers, one or more gyroscopes, global positioning system (GPS) or other location-identifying type capability, or a display with a high degree of functionality, such as a touch-sensitive color 2D or 3D display, for example.

[0046] Certain embodiments and principles will be discussed in more detail with reference to the figures. According to some embodiments, the disclosed climate control system provides integrated control and management of one or more client devices, the components associated therewith, and / or the applications executing thereon.

[0047] According to some embodiments, the discussion herein may focus on embodiments related to determining R-value and / or air infiltration at a location (e.g., a home, building, storage environment, and / or any environment with an at least partially enclosed volume capable of heat and / or gas exchange with an outside environment), as well as controlling one or more climate control components. However, these examples should not be construed as limiting, as one of skill in the art would understand that the disclosed climate control system described herein can apply to various scenarios without departing from the scope of the instant disclosure. For example, in an industrial environment, for example, the climate control system may be applied to one or more individual spaces within a location (e.g., rooms, storage bins, temporary enclosures, etc.). In some embodiments, the climate control system can apply separate control to maintain one or more user defined parameters for one or more spaces and even one or more remote locations, as non-limiting examples.

[0048] Moreover, embodiments exist where the disclosed climate control system can be applied to home energy management. For example, while the climate control system is directed to energy management of any location, some embodiments described herein are suitable for reducing energy losses in a residence. Through monitoring of the environment, which includes the use of a network to access one or more remote clients and / or databases, in some embodiments the climate control system implements a variety of control over various devices to achieve greater energy efficiency. While the control can include execution of program instructions to change the air pressure and / or temperature in a room, for example, control commands can also include the generation of electronic messages, such as "Please close themaster bedroom window,” or "The leakage area in your is calculated at 150 in2and is much higher than desired, would you like this information passed to your HVAC professional?", where the action taken by the climate control system is executed by the user themselves per the climate control system’s instruction, and / or via an electronic message, which may include any efficiency data described herein, that can be automatically sent to a recipient.

[0049] With reference to FIG. 1, climate control system 100 is depicted which includes user equipment (UE) 102 (e.g., one or more computers, as mentioned above and discussed below in relation to FIG. 7), network 104, cloud system 106, database 108, control engine 200 and climate system components 110. It should be understood that while climate control system 100 is depicted as implying a single instance of portions of the framework, FIG. 1 should not be construed as limiting the disclosure to a single instance, as the illustration is representative of one or more UEs 102, one or more networks 104, one or more cloud systems 106, one or more databases 108, and one or more climate system components 110 according to some embodiments. However, for discussion purposes, climate control system 100 is described in relation to the example depiction in FIG. 1.

[0050] According to some embodiments, UE 102 can include any type of device, such as. but not limited to, a mobile (smart) phone, tablet, laptop, sensor, Internet of things (loT) device, autonomous machine, appliance, controller, and / or any other device equipped with a wireless or wired transceiver. For example, UE 102 can include a smart thermometer comprising one or more computers which include one or more processors and one or more non-transitory computer readable media. In some embodiments, at least a portion of UE 102 may include one or more climate system components 110, which may include components (e.g., dampers, sensors, fans, air exchangers, filters, compressors, etc.) of a climate control (e.g., HVAC) system. In some embodiments, at least a portion of UE 102 may reside in cloud system 106, for example. Regardless of where portions of UE 102 reside, UE 102 can operate independent of the executing environment according to some embodiments.

[0051] In some embodiments, UE 102 can interface with one or more climate system components 110 directly (e.g., if at least a portion of UE 102 resides on a climate system component) and / or through a wired and / or wireless network 104 (e.g., to execute instructions for a damper and / or air exchange system), which may include the use of various application programming interfaces (APIs) to send and / or receive data through the network 104. In some embodiments, a climate system components 110 can include any type of device that is able to interface with UE 102 via any type of known or to be known pairing mechanism, including, but not limited to, WiFi, Bluetooth™, Bluetooth Low Energy (BLE), NFC, and the like.

[0052] According to some embodiments, network 104 includes one or more network devices that create a wireless local area network (WLAN) for the UE 102 to communicate with one or more climate system components 110 for a location (e.g., home, office, building and / or any other t pe of structure or geographically defined area fitted with a system that can control the climate therein). According to some embodiments, the network devices can be, but are not limited to, a router, switch, hub and / or any other type of network hardware that can project a WiFi signal to a designated area. In some embodiments, network 104 can be any type of network, such as, but not limited to, a wireless network, cellular network, the Internet, and the like (as discussed above). Network 104 facilitates connectivity of the components of climate control system 100, as illustrated in FIG. 1 according to some embodiments.

[0053] With reference to FIG. 5, in some embodiments, cloud system 106 may be any type of cloud operating platform and / or network-based system upon which applications, operations, and / or other forms of network resources may be located. For example, cloud system 106 may be a service provider and / or network provider from where services and / or applications may be accessed, sourced or executed from. For example, cloud system 106 can represent the cloudbased architecture associated with a smart home or network provider, which has associated network resources hosted on the internet or private network (e.g., network 104), which enables (via control engine 200) the device control and management discussed herein.

[0054] In some embodiments, cloud system 106 may include a server(s) and / or a database of information which is accessible over network 104. In some embodiments, cloud system 106 may communicate with database 108 directly (e.g., if the database 108 is part of the cloud system 106) or indirectly through network 104 (e.g., if the database is part of a climate system component 110). In some embodiments, cloud system 106 may store a dataset of data and metadata associated with local and / or network information related to a user(s) of the components of climate control system 100 and / or each of the components of climate control system 100 (e.g., UE 102, network 104, database 108, and / or climate system components 110).

[0055] In some embodiments, for example, cloud system 106 can provide a private / proprietary management platform, whereby control engine 200, discussed infra, corresponds to the novel functionality climate control system 100 enables, hosts and provides to a network 104 and / or other devices / platforms operating thereon.

[0056] Turning to FIG. 5 and FIG. 6, in some embodiments, the exemplary' computer-based systems / platforms, the exemplary computer-based devices, and / or the exemplary computer- based components of the present disclosure may be specifically configured to operate in a cloud computing / architecture 106 such as, but not limiting to: infrastructure as a sendee (laaS) 610,platform as a service (PaaS) 608. and / or software as a service (SaaS) 606 using a web browser, mobile app, thin client, terminal emulator or other endpoint 604. FIG. 5 and FIG. 6 illustrate schematics of non-limiting implementations of the cloud computing / architecture(s) in which the exemplary computer-based systems for administrative customizations and control of network-hosted application program interfaces (APIs) of the present disclosure may be specifically configured to operate.

[0057] Turning back to FIG. 1, according to some embodiments, database 108 may correspond to a data storage for a platform (e.g., a network hosted platform, such as cloud system 106, as discussed supra) or a plurality of platforms. In some embodiments, database 108 may receive storage instructions / requests from, for example, control engine 200 (and associated microservices), which may be in any type of known or to be known format, such as, for example, standard query language (SQL). According to some embodiments, database 108 may correspond to any type of known or to be known storage, for example, a memory or memory stack of a device, a distributed ledger of a distributed network (e.g., blockchain, for example), a look-up table (LUT). and / or any other type of secure data repository’.

[0058] Control engine 200, as discussed above and further below in more detail, can include components for the disclosed functionality. According to some embodiments, control engine 200 may be a special purpose computer implementing the disclosed framework. In some embodiments, control engine 200 may be hosted by a server and / or set of servers associated with cloud system 106.

[0059] According to some embodiments, as discussed in more detail below, control engine 200 may be configured to implement and / or control a plurality of services and / or microsendees, where each of the plurality of services / microservices are configured to execute a plurality of workflows associated with performing the disclosed application control and management framew ork. Non-limiting embodiments of such workflows are provided below.

[0060] According to some embodiments, as discussed above, control engine 200, or at least a portion thereof, may function as an application provided by cloud system 106. In some embodiments, control engine 200 may function as an application installed on UE 102, components 110. a server(s). network location and / or other type of network resource associated with system 106. In some embodiments, control engine 200 may function as application installed and / or executing on climate system components 110, as previously discussed. In some embodiments, control engine 200, or at least a portion thereof, may be configured and / or installed as an augmenting script, program or application (e.g., a plug-in or extension) toanother application or program provided by cloud system 106 and / or executing on climate system components 110.

[0061] As illustrated in FIG. 2, according to some embodiments, control engine 200 includes input module 202, analysis module 204, Al module 206, and / or execution module 208. It should be understood that the engine(s) and modules discussed herein are non-exhaustive, as additional or fewer engines and / or modules (or sub-modules) may be applicable to the embodiments of the systems and methods discussed. More detail of the operations, configurations and functionalities of control engine 200 and each of its modules, and their role within embodiments of the present disclosure will be discussed below. Turning to FIG. 3, Process 300 provides nonlimiting example embodiments for training Al according to some embodiments. In some embodiments, Process 300 provides non-limiting embodiments for training an artificial intelligence (Al) model to generate values for R-losses and / or leakage area for which the disclosed framework (e.g., via control engine 200) can use to control, manage, and / or manipulate one or more climate system components 110 to mitigate power loss and / or improve system efficiency. Steps described in the figures represent both an execution of a computer algorithm and a method of implementing the system.

[0062] According to some embodiments, Steps 302-312 are executed by the Al model executing in Al module 206, although Al module 206 may employ analysis module 204 for directly calculated values.

[0063] It should be understood that while the discussion herein will be with reference to one or more climate control systems, it should not be construed as limiting, as any type of program, website, network resource, platform or device can engine 200’ s functionality, discussed herein, without departing from the scope of the instant disclosure.

[0064] According to some embodiments, Process 300 begins with Step 302 where control engine 200 receives efficiency data as a training set for the Al. In some embodiments, the efficiency data can include one or more values relating to stack pressure delta, temperature induced delta pressure, density of air, gravity' values, building air column height, turbulent flow values, flow rate, power loss, air specific heat, inside temperature, outside temperature, leakage area, surface area for R-value loss, power, power losses, and / or R-values. In some embodiments, these values can be used to determine one or more optimum settings for climate control system 100. As mentioned previously, by using the mathematical manipulation of variables as illustrated in FIG. 9 to FIG. 13, in some embodiments, the system may only need to receive values related to P combined which may be obtained from the climate control system runtime data (e.g., heating and / or cooling data) from the thermostat and / or other powermeasurement devices (e.g., smart energy devices), inside temperature which may be obtained from one or more temperature sensors, and outside temperature which can be obtained either directly through one or more temperature sensors and / or indirectly (such as a web weather data API) through one or more external databases to build an expected model for a location. However, other training data set values can be used by the Al to analyze differences in directly- calculated power losses and expected power losses as determined by the methods described in relation to FIG. 9 to FIG. 13.

[0065] In Step 304, control engine 200 can analyze the training set data. In some embodiments, the analysis can include, but is not limited to, an execution of the algorithmic steps shown in FIG. 3, FIG. 4 and FIG. 12, for example. In general, the analysis can include a comparison between actual measured values for one or more variables and expected values based on calculated R-values and / or leakage area, which is represented in a non-limiting example shown in FIG. 13.

[0066] In Step 306, control engine 200 is configured to predict power loss according to some embodiments. In some embodiments, the power loss prediction can include the generation of a table that includes one or more of power losses from stack effect vs temperature, power losses from R-value vs temperature, and / or total power losses. FIG. 13 illustrates the results of nonlimiting calculated values to which the predictions are compared according to some embodiments. In some embodiments, if the predicted value does not fall w ith a calculated trend and / or confidence interval, the Al is configured to execute and / or generate instructions to control of one or more climate control components (e g., via client devices associated therewith) to attempt to create a mechanical lineup and / or pressure balance that will result in a power loss that substantially fits the calculated curve. According to some embodiments, the analysis can involve any type of known or to be computational analysis that can enable engine 200 to derive, determine, extract, retneve or otherwise generate and / or fit data to the expected calculated values. In some embodiments, such computational analysis can involve parsing the data, and extracting information indicating times, duration, activity, and the like.

[0067] In some embodiments, such computational analysis can involve control engine 200 executing any type of known or to be known computational analysis technique, algorithm, mechanism or technology. In some embodiments, control engine 200 may include a specific trained artificial intelligence model which may include a machine learning model (ML), a particular machine learning model architecture, a particular machine learning model type (e.g., convolutional neural network (CNN), recurrent neural network (RNN), autoencoder, supportvector machine (SVM), and the like), or any other suitable definition of a Al (or machine learning (ML)) model or any suitable combination thereof.

[0068] In some embodiments, control engine 200 may be configured to utilize one or more Al / ML techniques chosen from, but not limited to, computer vision, feature vector analysis, decision trees, boosting, support-vector machines, neural networks, nearest neighbor algorithms. Naive Bayes, bagging, random forests, logistic regression, and the like.

[0069] In some embodiments and, optionally, in combination of any embodiment described above or below, a neural network technique may be one of, without limitation, feedforward neural network, radial basis function network, recurrent neural network, convolutional network (e.g., U-net) or other suitable network. In some embodiments and, optionally, in combination of any embodiment described above or below, an implementation of Neural Network may be executed as follows: a. define Neural Network architecture / model for the control framework, b. transfer the input data to the neural network model, c. train the model incrementally, d. determine the accuracy for a specific number of timesteps, e. apply the trained model to process the newly received input data, f. optionally and in parallel, continue to train the trained model with a predetermined periodicity.

[0070] In some embodiments and, optionally, in combination of any embodiment described above or below, the trained Al model may specify a neural network by at least a neural network topology, a series of activation functions, and connection weights. For example, the topology of a neural network may include a configuration of nodes of the neural network and connections between such nodes. In some embodiments and, optionally, in combination of any embodiment described above or below, the trained Al model may also be specified to include other parameters, including but not limited to, bias values / functions and / or aggregation functions. For example, an activation function of anode may be a step function, sine function, continuous or piecewise linear function, sigmoid function, hyperbolic tangent function, or other type of mathematical function that represents a threshold at which the node is activated. In some embodiments and, optionally, in combination of any embodiment described above or below, the aggregation function may be a mathematical function that combines (e.g., sum, product, and the like) input signals to the node. In some embodiments and, optionally, in combination of any embodiment described above or below, an output of the aggregation function may be used as input to the activation function. In some embodiments and, optionally, in combination of anyembodiment described above or below, the bias may be a constant value or function that may be used by the aggregation function and / or the activation function to make the node more or less likely to be activated.

[0071] Advantageously, the AI / ML is configured to leam how to configure one or more climate control components to achieve maximum efficiency. In some embodiments, simply changing a temperature and / or pressure balance within one or more spaces within a location can influence air infiltration and the resulting power losses. For example, if a space (e.g.. room) temperature at a location (e g., home) is set to one value one day, and another value another day, the Al can analyze the difference for a given outside temperature to determine the effect on air infiltration based on those values. In some embodiments, the Al is configured to determine an optimum mechanical, temperature, and / or pressure configuration for a location and / or space based on comparisons of received variable inputs and actual power losses. In some embodiments, the (sensed) result of configuration changes based on predicted values are fed back to the Al model as a training set, thereby continually improving predictions and / or control. Executing control of a climate control system using this information is illustrated with reference to at least FIG. 4.

[0072] Turning to FIG. 4, Process 400 provides non-limiting example embodiments for the deployment and / or implementation of the disclosed climate control system. According to some embodiments, Steps 402 and 406 of Process 400 can be performed by input module 202 of control engine 200; Steps 404 and 416 can be performed by analysis module 204; Step 408 can be performed by Al module 206; and Steps 410, 412, and 414 can be performed by execution module 208.

[0073] According to some embodiments, Process 400 begins with Step 402 where control engine 200 receives system inputs from various sensors. In some embodiments, the inputs include power consumption and / or temperature values from one or more climate system components 110 in accordance with the configuration shown in FIG. 1. In some embodiments, R-value and / or leakage area can be determined by the analytics module 204. In some embodiments, power loss due to environmental and / or structural changes can be predicted by the Al module 206. thereby enabling execution of the efficiency balance in step 408 in a proactive manner (e.g., before environmental changes occur).

[0074] In Step 406, control engine 200, through direct communication and / or through network 104, can determine the current state of one or more climate control components according to some embodiments. In some embodiments, these values are stored as part of efficiency data, and may be sent to the Al model as part of step 408 to improve predicted values.

[0075] In Step 408. control engine 200 executes a system efficiency balance. In some embodiments, the execution in step 408 may include control of component setting (e.g., damper angle) at a Substep 410. In some embodiments, component settings include mechanical ventilation settings to promote a healthier and more comfortable indoor environment while optimizing energy efficiency. Larger locations such as buildings or homes with multiple zones may have zoned ventilation systems. In some embodiments, each zone has its own ventilation controls, allowing for customized air exchange rates in different areas based on occupancy and specific indoor air quality needs.

[0076] In some embodiments, component settings include control of heat recovery' ventilation (HRV), energy recovery ventilation (ERV), and / or air to air heat exchanger (AAHX) units. These devices transfer heat or energy between the outgoing indoor air and the incoming outdoor air, pre-conditioning the fresh air before it enters the building according to some embodiments. In some embodiments, the ventilation system controls the rate of air exchange by adjusting airflow. In some embodiments, it is configured to increase or decrease the amount of outdoor air brought in and the amount of indoor air exhausted based on the indoor air quality and other factors. This helps to reduce energy consumption while still providing fresh air exchange. In some embodiments, the control engine 200 is configured to reduce and / or eliminate air flow through an air exchanger during predicted and / or realized excessive air infiltration (because air exchange is occurring anyway), thereby by offsetting the power consumption of a compressor, for example, due to the increase in air infiltration. In some embodiments, component settings include one or more temperature settings within a location and / or for spaces within a location.

[0077] In some embodiments, the execution in step 408 may include the execution of a pressure balance at Substep 412. In some embodiments, climate control systems can be designed to create positive or negative pressure in specific areas, depending on the requirements. For example, some rooms or spaces (like bathrooms or kitchens) may benefit from negative pressure to exhaust stale air and odors, while other rooms may require positive pressure to prevent the entry of outdoor pollutants. Ensuring a location is properly balanced in terms of indoor air pressure helps prevent excessive air infiltration and / or exfiltration. In some embodiments, the execution of a pressure balance for one or more climate control components are configured to maintain optimum pressure differentials to minimize air leaks.

[0078] In some embodiments, the execution in step 408 may include the compiling of an electronic message at Substep 414. While control engine 200 can execute complex control options, some changes to a location configuration may need to be implemented by a person. To this end, in some embodiments, control engine 200 is configured to compile an electronicmessage such as “Please close upstairs window.” In some embodiments, control engine 200 is configured to determine abnormal changes in leakage area from sensed pressure changes and / or abnormal changes in temperature in a location and / or space. In some embodiments, the Al module 206 is configured to correlate these abnormal changes to a root cause, such as a cracked door or window, as non-limiting examples. In some embodiments, engine 200 is configured to generate a graphical user interface (GUI) configured to enable a user to define the root cause in response to an electronic message (e.g., alert). For example, if the temperature in a room should be stable, but is increasing / decreasing outside of expected values for given inputs, the system can prompt the user to investigate the space and / or submit a reason for the abnormal condition, which may be presented as a root cause later if the situation reoccurs. In some embodiments, the Al module 206 and / or analysis module 204 can correlate changes in temperature and / or pressure to external conditions such as wind direction and / or changes in barometric pressure, which affects air infiltration as previously described. By identifying the source of additional air infiltration, efficiency can be improved. In some embodiments, engine 200 is configured to enable a user to enter location data (e.g., zip code) where the framework monitors environmental conditions based on the location data (e.g.. through one or more APIs).

[0079] At step 416, input module 202 can receive inputs from one or more sensors to determine the system response. In some embodiments, the system response is stored as efficiency data, which may be sent to the Al model as training data in step 312. As mentioned previously, in some embodiments, the Al model is configured to use the response data, which is part of the efficiency data, to predict system responses to climate control system configurations and / or abnormal (e.g., environmental, structural) changes.

[0080] FIG. 8 illustrates the concept of wind induced air infiltration 801 and stack induced infiltration 802 according to some embodiments. In the case of wind induced air infiltration 801, when there is wind blowing against a location (e.g., building), it creates a difference in air pressure between the inside and outside which causes air to be pushed into the location through a leakage area. Similarly, for stack induced infiltration 802, if the inside of a location is warmer than the outside, the warmer air will rise, creating a lower pressure inside, which can draw in cooler air from outside through gaps and cracks. Alternatively, according to some embodiments, if it is warmer outside and cooler inside the reverse can occur. Accordingly, in some embodiments, the "stacks" can have different "stacking" pressures which can cause lateral forces that force air to move from one temperature region to another. As described previously, system-controlled pressure balancing of spaces (e.g., rooms) with air leakage helps to mitigate such effects.

[0081] FIG. 9 shows the mathematical steps for determining leakage area (AL) according to some embodiments. As shown, the stack pressure delta 901 can be calculated by inputting the values for air density, gravity, and air column height, where air column height includes the height of the location (e.g., the height of the enclosed space defined by the location). These values, which are generally constants for a particular location can be applied to simplify the temperature induced delta pressure formula 902. For example, Ttcan equal 273K, via the conversion offset from the Kelvin to Celsius scale, and the temperature induced delta pressure equation can be further simplified to equation 903. Substituting flowrate equation 904 into power loss equation 905, and entering known variables such as densify, air characteristics («), and the specific heat of air (c) yields simplified power loss equation 906 for stack induced air infiltration. The power loss (P) can be obtained and / or calculated by the system based on energy usage according to some embodiments. With all variables 901 accounted for, leakage area (AL) 907 can now be calculated as a function of heating and / or cooling power and temperature differential.

[0082] FIG. 10, according to some non-limiting example embodiments, depicts a Bernoulli simplification for flowrate, as discussed herein. In some embodiments, the simplification begins with an energy balance equation 1001. At Step 1002, the various substitutions are simplified down to equations that contain system variables. In Step 1003, ideal conditions are assumed, where leak entrance and exit are at the same height and there is no wind, which allow s for further simplification and substitution to arrive at flowrate equation 904 as used in FIG. 9.

[0083] FIG. 11 shows the math behind R-value calculations according to some embodiments. In some embodiments, the system is configured to generate a GUI to enable a user to input a width and / or depth of the location, and wall height, as values for surface area 1101 of the location according to some embodiments. For a known (e.g., input, received) temperature differential 1102 and / or power loss 1 103, the R-value 1104 can be solved by the system without the need for conventional techniques according to some embodiments. Once the R-value is known, power loss vs temperature 1402 can be graphed as shown in FIG. 14, which also can comprise resulting power loss from the stack effect 1401 and / or combined power loss 1404. FIG. 12 illustrates a model for generating a combined power loss calculation according to some embodiments.

[0084] Thus, conventional methods are not needed to calculate the R-Value 1104 or leakage area 907 for a location. Referring to FIG. 13, in some embodiments, the total power input into a structure for heating and or cooling can be obtained from runtime data from a controlling device and / or component such as a (smart) thermostat. As previously described, thecharacteristics of the heating and or cooling system are used together with the temperature delta between inside and outside to generated accumulated data pairing between the power input and the temperature delta to build and / or display a table such as is shown in FIG 13. Fitting the data in the table to the model described results in a fit for the unknows of R-Value 1104 and leakage area 907.

[0085] The resulting power loss from the stack effect 1401 can be validated and / or tested by varying leakage area. According to some embodiments, the change in leakage area, which may be indicative of a physical change in the leakage area itself (e.g., an open door) and / or an environmental change (e.g., increase in wind), can be calculated from power losses. As discussed previously, one or both parameters can be correlated to changes in the structure and / or environment according to some embodiments.

[0086] FIG. 13 shows a non-limiting example calculation executed by the system according to some embodiments to build a measured power loss table for a location model. Table data is generated for measured heating and or cooling power and measured inside to outside temperature difference by the climate control system. At Step 1302, in some embodiments, engine 200 determines if the inside temperature is stable. In some embodiments, stable includes less than a 1 degree change in temperature over a measurement period (e.g., 1 minute, 1 hour, etc.). At Step 1302, in some embodiments, engine 200 determines if the outside temperature is stable. In some embodiments, at Step 1306, engine 200 receives wind speed data, and if the wind speed is below a predetermined value (e.g.. less than 5 mph) can proceed to step 1308, where powder loss is calculated. At Steps 1310, 1312, and 1314, the values from Steps 1304, 1302, and 1308 are stored as efficiency data, respectively, according to some embodiments. For the conditions where inside and outside temperatures are stable (e.g., the delta temperature is stable) the input power from the heating and or cooling system is equal to the power loss. In some embodiments, that these measurements are performed at night to eliminate the variable for solar radiative heating. In some embodiments, at Step 131 , engine 200 is configured to generate a power loss vs temperature table. At Step 1318, engine 200 is configured to fit an effective R-value and / or stack infiltration leakage area based on the stored table values. In some embodiments, the engine 200 can initially measure and build the table, then fit the table for R- value and leakage area. In some embodiments, the resulting efficiency data can be used for additional purposes as previously described.

[0087] FIG. 14 shows the result for the data fit executed in FIG. 13 according to some embodiments. While an output on a display showing 1403 is optional, it provides a visual representation of the power losses expected due to stable changes in temperature. Power lossesthat do not fit this curve according to some embodiments are indicative of a change in the location structure and / or environment. In some embodiments, engine 200 is configured to execute an analysis (e.g., via analysis module 204) to determine which of one or more changes from one or more inputs (e.g., wind speed, barometric pressure, and / or any sensed or derived variable value) correlate to the abnormal power loss. Using this information, control of various climate system components 110 (e.g., HVAC system components) can be altered in response to, and / or from prediction of, the input change. For example, as wind induced air infiltration causes pressure changes within a location, the execution of a pressure balance by control engine 200 can increase / decrease the pressure in a space (e.g., room) within the location through various damper configurations according to some embodiments. In some embodiments, instructions for these changes are generated proactively by the Al model, such as when future wind conditions (e.g., via a weather API) are received, for example, mitigating efficiency losses before they happen (even if a wind sensor is present in the system).

[0088] FIG. 7 is a schematic diagram illustrating a client device showing an example embodiment of a computing device that may be used within the present disclosure and / or the UE 102 illustrated in FIG. 1. Computing device 700 may include many more or less components than those shown in FIG. 7, such as a plurality of one or more computers. However, the components shown are sufficient to disclose an illustrative embodiment for implementing the present disclosure. Computing device 700 may represent, for example, UE 102 discussed above at least in relation to FIG. 1.

[0089] As shown in the figure, in some embodiments, computing device 700 includes one or more processors (CPU) 722 and / or Microcontrollers (MCU) in communication with one or more non-transitory computer readable media 730 via a bus 724. Computing device 700 also includes a power supply 726, one or more network interfaces 750, an audio interface 752. a display 754, a keypad 756, an illuminator 758, an input / output interface 760, a haptic interface 762, an optional global positioning systems (GPS) receiver 764 and a camera(s) or other optical, thermal or electromagnetic sensors 766. Computing device 700 can include one camera / sensor 766, or a plurality of cameras / sensors 766, as understood by those of skill in the art. Power supply 726 provides power to computing device 700.

[0090] Computing device 700 may optionally communicate with a base station (not shown), or directly with another computing device, such as a computer within a climate system component 110. In some embodiments, network interface 750 is sometimes known as a transceiver, transceiving device, or network interface card (NIC).

[0091] Audio interface 752 is arranged to produce and receive audio signals such as the sound of a human voice in some embodiments. Display 754 may be a liquid crystal display (LCD), gas plasma, light emitting diode (LED), or any other type of display used with a computing device. Display 754 may also include a touch sensitive screen arranged to receive input from an object such as a stylus or a digit from a human hand.

[0092] Keypad 756 may include any input device arranged to receive input from a user. Illuminator 758 may provide a status indication and / or provide light.

[0093] Computing device 700 also includes input / output interface 760 for communicating with external climate system components. Input / output interface 760 can utilize one or more communication technologies, such as USB, infrared, Bluetooth™, or the like in some embodiments. Haptic interface 762 is arranged to provide tactile feedback to a user of the client device.

[0094] Optional GPS transceiver 7 4 can determine the physical coordinates of computing device 700 on the surface of the Earth, which typically outputs a location as latitude and longitude values. GPS transceiver 764 can also employ other geo-positioning mechanisms, including, but not limited to. triangulation, assisted GPS (AGPS). E-OTD, CI, SAL ETA, BSS or the like, to further determine the physical location of computing device 700 on the surface of the Earth. In one embodiment, however, computing device 700 may through other components, provide other information that may be employed to determine a physical location of the device, including for example, a MAC address. Internet Protocol (IP) address, or the like, which may be used to in conjunction with one or more APIs to obtain environmental data as described herein.

[0095] Mass memory' 730 includes a RAM 732, a ROM 734, and other storage means. Mass memory 730 illustrates another example of computer storage media for storage of information such as computer readable instructions, data structures, program modules, usage data, or other data. Mass memory 730 stores a basic input / output system (“BIOS”) 740 for controlling low- level operation of computing device 700. The mass memory also stores an operating system 741 for controlling the operation of computing device 700.

[0096] Memory 730 further includes one or more data stores, which can be utilized by computing device 700 to store, among other things, applications 742 and / or other information or data. For example, data stores may be employed to store information that describes various capabilities of computing device 700. The information may then be provided to another device based on any of a variety of events, including being sent as part of a header (e.g.. index file of the HLS stream) during a communication, sent upon request, or the like. At least a portion ofthe capability information may also be stored on a disk drive or other storage medium (not shown) within computing device 700.

[0097] Applications 742 may include computer executable instructions which, when executed by computing device 700, transmit, receive, and / or otherwise process audio, video, images, and enable telecommunication with a server and / or another user of a client device. Applications 742 may further include a client that is configured to send, to receive, and / or to otherwise process gaming, goods / services and / or other forms of data, messages and content hosted and provided by the platform associated with control engine 200 and its affiliates.

[0098] As used herein, the terms "control engine” and “engine” identify at least one software component and / or a combination of at least one software component and at least one hardware component which are designed / programmed / configured to manage / control other software and / or hardware components (such as the libraries, software development kits (SDKs), objects, and the like).

[0099] Examples of hardware elements may include processors, microcontrollers, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), microcontrollers, dual-core microcontrollers, and so forth. Further understanding of the concepts and algorithms applied herein are explained in relation to FIG. 3, FIG. 4, and FIGs. 8 to 13.

[0100] Computer-related systems, computer systems, and systems, as used herein, include any combination of hardware and software. Examples of software may include software components, programs, applications, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computer code, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and / or software elements may vary' in accordance with any number of factors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

[0101] For the purposes of this disclosure a module is a software, hardware, or firmware (or combinations thereof) system, process or functionality , or component thereof, that performs or facilitates the processes, features, and / or functions described herein (with or without human interaction or augmentation). A module can include sub-modules. Software components of a module may be stored on a computer readable medium for execution by a processor. Modules may be integral to one or more servers or be loaded and executed by one or more servers. One or more modules may be grouped into an engine or an application.

[0102] One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as “IP cores,” may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that make the logic or processor. Of note, various embodiments described herein may, of course, be implemented using any appropriate hardware and / or computing software languages (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, and the like).

[0103] For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may be downloadable from a network, for example, a website, as a stand-alone product or as an add-in package for installation in an existing software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be available as a client-server software application, or as a web-enabled software application. For example, exemplary softw are specifically programmed in accordance w ith one or more principles of the present disclosure may also be embodied as a software package installed on a hardware device.

[0104] For the purposes of this disclosure the term “user”, “subscriber” “consumer” or “customer” should be understood to refer to a user of an application or applications as described herein and / or a consumer of data supplied by a data provider. By way of example, and not limitation, the term “user” or “subscriber” can refer to a person w ho receives data provided by the data or service provider over the Internet in a browser session, or can refer to an automated software application which receives the data and stores or processes the data. Those skilled in the art will recognize that the methods and systems of the present disclosure may be implemented in many manners and as such are not to be limited by the foregoing exemplaryembodiments and examples. In other words, functional elements being performed by single or multiple components, in various combinations of hardware and software or firmware, and individual functions, may be distributed among software applications at either the client level or server level or both. In this regard, any number of the features of the different embodiments described herein may be combined into single or multiple embodiments, and alternate embodiments having fewer than, or more than, all of the features described herein are possible.

[0105] Functionality may also be, in whole or in part, distributed among multiple components, in manners now known or to become known. Thus, myriad software / hardware / firmware combinations are possible in achieving the functions, features, interfaces and preferences described herein. Moreover, the scope of the present disclosure covers conventionally known manners for carrying out the described features and functions and interfaces, as well as those variations and modifications that may be made to the hardware or software or firmware components described herein as would be understood by those skilled in the art now and hereafter.

[0106] Furthermore, the embodiments of methods presented and described as flowcharts in this disclosure are provided by way of example in order to provide a more complete understanding of the technology. The disclosed methods are not limited to the operations and logical flow presented herein. Alternative embodiments are contemplated in which the order of the various operations is altered and in which sub-operations described as being part of a larger operation are performed independently.

[0107] While various embodiments have been described for purposes of this disclosure, such embodiments should not be deemed to limit the teaching of this disclosure to those embodiments. Various changes and modifications may be made to the elements and operations described above to obtain a result that remains within the scope of the systems and processes described in this disclosure.

Claims

CLAIMSWhat is claimed is:

1. A method comprising: determining, by a device, an R-value and a leakage area value for a location, the location comprising a climate control system; generating, by the device, a table comprising expected power losses in the climate control system for a set of temperatures using the R-value and the leakage area; monitoring, by the device, an actual power loss in the climate control system: comparing, by the device, the actual power loss to an expected power loss; and executing, by the device, an efficiency balance configured to reduce the actual power loss, the execution of the efficiency balance causing a modification of a real-world environment of the location via the climate control system.

2. The method of claim 1. wherein the efficiency balance includes program instructions configured to manipulate one or more climate control components.

3. The method of claim 1. wherein the efficiency balance includes program instructions configured to execute a pressure balance.

4. The method of claim 3, wherein the pressure balance includes program instructions configured to manipulate one or more climate control components.

5. The method of claim 4, wherein the pressure balance includes program instructions configured to increase or decrease the pressure within at least one space within the location.

6. The method of claim 1. wherein the efficiency balance includes program instructions configured to generate an electronic message.

7. The method of claim 1. wherein the efficiency balance includes program instructions generated by an artificial intelligence program.

8. The method of claim 7, wherein the artificial intelligence program includes instructions that predict power loss based on predicted environmental factors.

9. The method of claim 8, wherein the environmental factors include one or more of barometric pressure, wind speed, wind direction.

10. The method of claim 8. wherein the environmental factors include forecast for one or more of temperature, air quality, overcast conditions, humidity, and rain.

11. A device comprising: a computer configured to: determine an R-value and a leakage area value for a location, the location comprising a climate control system; generate a table comprising expected power losses in the climate control system for a set of temperatures using the R-value and the leakage area; monitor an actual power loss in the climate control system; compare the actual power loss to an expected power loss; and execute an efficiency balance configured to reduce the actual power loss, the execution of the efficiency balance causing a modification of a real-world environment of the location via the climate control system.

12. The device of claim 11, wherein the efficiency balance includes program instructions configured to manipulate one or more climate control components.

13. The device of claim 11, wherein the efficiency balance includes program instructions configured to execute a pressure balance.

14. The device of claim 13, wherein the pressure balance includes program instructions configured to manipulate one or more climate control components.

15. The device of claim 14, wherein the pressure balance includes program instructions configured to increase or decrease the pressure within at least a space within the location.

16. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a device, perform a method comprising: determining, by the device, an R-value and a leakage area value for a location, the location comprising a climate control system; generating, by the device, a table comprising expected power losses in the climate control system for a set of temperatures using the R-value and the leakage area; monitoring, by the device, an actual power loss in the climate control system; comparing, by the device, the actual power loss to an expected power loss; and executing, by the device, an efficiency balance configured to reduce the actual power loss, the execution of the efficiency balance causing a modification of a real-world environment of the location via the climate control system.

17. The non-transitory computer-readable storage medium of claim 16, wherein the efficiency balance includes program instructions configured to generate an electronic message.

18. The non-transitory computer-readable storage medium of claim 16, wherein the efficiency balance includes program instructions generated by artificial intelligence.

19. The non-transitory computer-readable storage medium of claim 18, wherein the artificial intelligence includes program instructions that predict power loss based on predicted environmental factors.

20. The non-transitory computer-readable storage medium of claim 19, wherein the environmental factors include one or more of barometric pressure, wind speed, wind direction, temperature, air quality, overcast conditions, humidity, and rain.

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