Systems and methods for adaptive control of heating cycle endpoint

Abstract

Disclosed are systems and methods of a novel framework to improve the performance and reduce resource usage of a water heater. By adaptively stopping the heating of the tank at a lower temperature, which can be predicted by past use to supply adequate hot water (energy), and then heating prior to a learned use demand period, the disclosed systems and methods provide functionality that enables the standby temperature to be optimized for energy loss. Among other benefits, this not only reduces the expenditure of resources, but also improves the accuracy and efficiency of the water heater system.

Description

Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCTSYSTEMS AND METHODS FOR ADAPTIVE CONTROL OF HEATING CYCLE ENDPOINTCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority from U.S. Provisional Patent Application No. 63 / 697,717, filed September 23, 2024, and U.S. Provisional Patent Application No. 63 / 713,388, filed October 29, 2024, which are incorporated herein by reference in their entirety.FIELD OF THE DISCLOSURE

[0002] The present disclosure relates to water heater control mechanisms, and more particularly, to a decision intelligence (Dl)-based computerized framework that automatically and dynamically monitors and controls energy usage and operation of water heaters.SUMMARY OF THE DISCLOSURE

[0003] Energy loss in systems, such as gas water heaters, contains the parasitic loss from the water temperature through thermal conduction and radiative paths to room ambient and water line temperatures. Traditional algorithms using a temperature threshold to engage heating and a second threshold, such as. for example, the temperature set point, stop the heating cycle to address such issues; however, this results in a waste of energy, as this causes energy to exit / emanate from the tank water temperature which can be above the needed standby temperature.

[0004] To that end, according to some embodiments, the disclosed systems and methods provide a novel computerized framework to work conjunctively with water heaters to address such shortcomings, among others. As discussed herein, by adaptively stopping the heating of the tank at an analytically derived temperature, which can be predicted by past use to supply adequate hot water (energy), and then heating prior to a learned use demand period, the disclosed systems and methods provide functionality that enables the standby temperature to be controlled to minimize energy loss. As provided herein, this not only reduces the expenditure of resources, but also improves the accuracy and efficiency of the water heater system.

[0005] It should be understood that while the discussion herein focuses on water heaters, it should not be construed as limiting, as other types of devices and / or systems can be the basis of the disclosed framework’s operation without departing from the scope of the instant disclosure. For example, boilers, steam generators, heat exchangers, heat pumps, distillation units, pasteurizers, and the like. Moreover, while the discussion herein may focus on gas waterResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT heaters, it should not be so limited, as one of skill in the art would recognize that embodiments exist, without departing from the scope of the instant disclosure, where other types of known or to be know water heaters can be utilized - for example, solar water heaters, tankless water heaters, heat pump water heaters, indirect water heaters, point of use water heaters, combination boiler systems, geothermal water heaters, and other types of conventional water heaters, and the like.

[0006] According to some embodiments, a method is disclosed that automatically and dynamically monitors and controls energy usage of water heaters for an optimized energy loss. In accordance with some embodiments, the present disclosure provides a non-transitory computer-readable storage medium for carry ing out the above-mentioned technical steps of the framework'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 that automatically and dynamically monitors and controls energy' usage of water heaters for an optimized energy loss.

[0007] In accordance with one or more embodiments, a system is provided that includes one or more processors and / or computing devices configured to provide functionality in accordance with such embodiments. In accordance with one or more 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 is embodied in, by' and / or on a non-transitory computer-readable medium.DESCRIPTIONS OF THE DRAWINGS

[0008] 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:

[0009] FIG. 1 A 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;

[0010] FIG. IB is a block diagram illustrating components of an exemplary' system according to some embodiments of the present disclosure;Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT

[0011] FIG. 2A illustrates a detailed cross-sectional view of a residential gas water heater according to some embodiments of the present disclosure;

[0012] FIG. 2B shows heat transfer mechanisms throughout the water heater according to some embodiments of the present disclosure;

[0013] FIG. 3 illustrates a temperature vs. time graph according to some embodiments of the present disclosure;

[0014] FIG. 4 illustrates models used by the framework to calculate heat transfer rate according to some embodiments of the present disclosure;

[0015] FIG. 5 depicts a process for determining the flow of water according to some embodiments of the present disclosure;

[0016] FIG. 6 shows a mathematical derivation related to extracting flow according to some embodiments of the present disclosure;

[0017] FIG. 7 illustrates the framework extracting 1st and 2nd derivatives of temperature data according to some embodiments of the present disclosure;

[0018] FIG. 8 shows a non-limiting example output of binned data according to some embodiments of the present disclosure;

[0019] FIG. 9 shows the framew ork calculating w ater usage based on power needed according to some embodiments of the present disclosure;

[0020] FIG. 10 depicts the framework executing an energy calculation according to some embodiments of the present disclosure;

[0021] FIG. 1 1 shows a generalized form of the example of FIG. 10 according to some embodiments of the present disclosure;

[0022] FIG. 12 illustrates water temperature and its 1st derivative (gradient) according to some embodiments of the present disclosure;

[0023] FIG. 13 depicts a ground truth dataset according to some embodiments of the present disclosure;

[0024] FIG. 14 illustrates the extraction of w ater usage from a heating request according to some embodiments of the present disclosure;

[0025] FIG. 15 illustrates how the framework extracts water usage based on cooling and heating rates according to some embodiments of the present disclosure;

[0026] FIG. 16 shows a calculation for the expected rate of heating according to some embodiments of the present disclosure;

[0027] FIG. 17 shows how heating events are extracted using the 1st derivative according to some embodiments of the present disclosure;Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT

[0028] FIG. 18 illustrates statistics for the rate of change in water temperature according to some embodiments of the present disclosure;

[0029] FIG. 19 illustrates the framework’s execution of an adaptive setpoint according to some embodiments of the present disclosure;

[0030] FIG. 20 depicts computer implemented steps by the framework according to some embodiments of the present disclosure;

[0031] FIG. 21 shows a Markov Probability Model for hot water use probability according to some embodiments of the present disclosure;

[0032] FIG. 22 illustrates steps to extract water standby loss during no water use according to some embodiments of the present disclosure;

[0033] FIG. 23 shows an analysis of standby loss by temperature changes according to some embodiments of the present disclosure;

[0034] FIG. 24 illustrates the framew ork executing a calculation of the mass of a water tank according to some embodiments of the present disclosure;

[0035] FIG. 25 illustrates an exemplary workflow according to some embodiments of the present disclosure;

[0036] FIG. 26 illustrates an exemplary workflow' according to some embodiments of the present disclosure;

[0037] FIG. 27 illustrates an exemplary cloud computing environment according to some embodiments of the present disclosure;

[0038] FIG. 28 depicts an exemplary' implementation of an architecture according to some embodiments of the present disclosure; and

[0039] FIG. 29 is a block diagram illustrating a computing device show ing an example of a client or server device used in various embodiments of the present disclosure.DETAILED DESCRIPTION

[0040] The present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, which form a part 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, embodimentsResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT may, for example, take the form of hardware, software, 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.

[0041] 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.

[0042] 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 charactenstic 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.

[0043] 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, 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 block diagrams 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 blocksResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT 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.

[0044] 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 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 nonremovable 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.

[0045] For the purposes of this disclosure the term “server” should be understood to refer to a service 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 netw ork 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.

[0046] 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 betw een a server and a client device or other ty pes 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.Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT

[0047] 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.

[0048] In short, a wireless network may include virtually any type 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.

[0049] A computing device 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.

[0050] 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.

[0051] 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 or previously mentioned devices 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.

[0052] Certain embodiments and principles will be discussed in more detail with reference to the figures. As discussed herein, optimizing energy loss for a water heater can lead to significantResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT benefits in terms of energy efficiency and cost savings. Energy loss in modem water heaters occurs through several predominate heat conduction paths. The tank R-value represents one of these. Here an insulative blanket or layer is used to surround the tank such that the tanks temperature is insulated from the ambient temperature. Higher R-value means higher insulation and less heat transfer or loss. In gas water heaters heat is also lost through stack effect, whereby ambient temperature room air comes in contact with the inner chimney of the water heater, is warmed by the tanks warmer temperature, and then rises and exits through the exhaust gas flue. Additionally, there are thermal conduction paths from the hot water tank to the connection plumbing supply and source water lines. Here heat is lost through the plumbing pipes and water thermal conductivities. For all of these loss cases, the magnitude of the loss is proportional to the temperature difference across the particular conduction path. Reducing the temperature difference has the effect of reducing the amount of loss. By allowing for an analytic based control of the tank temperature, the system can control the heat losses that are present and improve overall efficiency. This optimization can be performed based on historical data about the water heater's performance, usage patterns, and environmental conditions.

[0053] According to some embodiments, as discussed herein, in order to implement such an approach, the disclosed framework can analyze past information such as temperature fluctuations, energy consumption patterns, and hot water demand cycles. Such data can reveal opportunities for strategic control of the heating cycle end point temperature, to optimize energy loss while maintaining consistent hot water availability. For example, by adaptively setting the end temperature of the heating cycle based on data that predicts usage needs, and usage times, the standby temperature can be optimized. This can be particularly effective when combined with smart controls that adjust heating elements based on learned usage patterns.

[0054] With reference to FIG. 1 A. a system is depicted for a location 100 which includes water heater 102, user equipment (UE) 112 (e.g., a client device, as mentioned above and discussed below in relation to FIG. 10), network 104, cloud system 106, database 108 and management engine 200. It should be understood that while system 100 is depicted as including such components, it should not be construed as limiting, as one of ordinary skill in the art would readily understand that varying numbers of water heaters, UEs, sensors, cloud systems, databases and networks can be utilized; however, for purposes of explanation, system 100 is discussed in relation to the example depiction in FIG. 1 A.

[0055] According to some embodiments, water heater 102 is an appliance configured to heat and / or store water for various domestic uses such as bathing, washing dishes, laundry, and other types of plumbing needs. Water heater 102 typically consists of a large, insulated tank that holdsResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT the water, a heating mechanism to raise the water temperature, and various controls to regulate the heating process and maintain the desired temperature. Water heater 102 can also include safety features, such as pressure relief valves and thermostats to prevent overheating. Water heater 102 can connect to a location’s (e.g., home, office and / or other building / structure, for example) plumbing system, drawing in cold water to heat and storing it until needed, at which point the heated water can be distributed through pipes to different fixtures throughout the location.

[0056] By way of an example, water heater 102 can be a gas water heater, which may have a 50-gallon tank (e.g., a model suitable for a typical family home). This unit could have a gas burner at the bottom of the tank, controlled by a thermostat. When the water temperature drops below a predetermined threshold, below the set point, the thermostat opens the gas valve, igniting the burner. The flame heats the bottom of the tank, and the hot gases flow through a central chimney or flue pipe, transferring heat to the surrounding water as they rise. In some embodiments, for example, such water heater model can include features, such as, but not limited to, a pilot light for ignition, a thermopile to generate electricity for the gas valve, and multiple anode rods to protect against corrosion. The water heater may be vented to the outside to safely remove combustion gases and typically achieve an energy factor (EF) rating of around 0.60 to 0.65, for example, indicating its overall efficiency.

[0057] Indeed, as discussed above, while the discussion herein may focus on gas water heaters, it should not be so limited, as one of skill in the art would recognize that embodiments exist, without departing from the scope of the instant disclosure, where other types of known or to be know water heaters can be utilized - for example, solar water heaters, tankless water heaters, heat pump water heaters, indirect water heaters, point of use water heaters, combination boiler systems, geothermal water heaters, and other types of conventional water heaters, and the like.

[0058] According to some embodiments, UE 112 can be any type of device, such as, but not limited to, a mobile phone, tablet, laptop, sensor, Internet of Things (loT) device, autonomous machine, and any other device equipped with a cellular or wireless or wired transceiver. In some embodiments, UE 112 can be a device associated with an individual (or set of individuals) for which climate control services are being provided. In some embodiments, UE 112 may correspond to a device of a climate service provider entity' (e.g., a thermostat, for example), which can control and / or provide instructions to the water heater 102.

[0059] In some embodiments, UE 112 may correspond to a sensor for providing information to the water heater 102. For example. UE 112 may include and / or correspond to, but not be limited to, a temperature sensor, flow sensor, pressure sensor, occupancy sensor, humidity sensor, w aterResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT quality sensor, gas sensor, leak detection sensor, energy' consumption sensor, weather sensor, and the like.

[0060] 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 system 100, as illustrated in FIG. 1A.

[0061] According to 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, system 106 may be a sendee provider and / or network provider from where services and / or applications may be accessed, sourced or executed from. For example, system 106 can represent the cloud-based architecture associated with a climate-control system provider, which has associated network resources hosted on the internet or private network (e.g., network 104), which enables (via engine 200) the location management discussed herein.

[0062] 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, a database 108 of cloud system 106 may store a dataset of data and metadata associated with local and / or network information related to a user(s) of UE 112 / water heater 102 and the UE 112 / water heater 102, and the services and applications provided by cloud system 106 and / or management engine 200.

[0063] In some embodiments, for example, cloud system 106 can provide a private / proprietary management platform, whereby engine 200, discussed infra, corresponds to the novel functionality system 106 enables, hosts and provides to a network 104 and other devices / platforms operating thereon.

[0064] Turning to FIG. 27 and FIG. 28, 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 a service (laaS) 510, platform as a service (PaaS) 508. and / or software as a service (SaaS) 506 using a web browser, mobile app, thin client, terminal emulator or other endpoint 504. FIG. 27 and FIG. 28 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 APIs of the present disclosure may be specifically configured to operate.Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT

[0065] Turning back to FIG. 1A, 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), a plurality of platforms, and / or water heater 102 and / or UE 112. Database 108 may receive storage instruct ons / requests from, for example, 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 ty pe of secure data repository'.

[0066] Management engine 200, as discussed above and further below in more detail, can include components for the disclosed functionality. According to some embodiments, management engine 200 may be a special purpose machine or processor, and can be hosted by a device on network 104, within cloud system 106, on UE 112, and / or water heater 102. In some embodiments, engine 200 may be hosted by a server and / or set of servers associated with cloud system 106.

[0067] According to some embodiments, as discussed in more detail below, management engine 200 may be configured to implement and / or control a plurality' of services and / or microservices, where each of the plurality of services / microservices are configured to execute a plurality of workflows associated with performing the disclosed location (e.g.. climate) management. Non-limiting embodiments of such workflows are provided below.

[0068] According to some embodiments, as discussed above, management engine 200 may function as an application provided by cloud system 106. In some embodiments, engine 200 may function as an application installed on a server(s), network location and / or other type of network resource associated with system 106. In some embodiments, engine 200 may function as an application installed and / or executing on UE 112 and / or water heater 102. In some embodiments, such application may be a web-based application accessed by UE 112 and / or water heater 102 and / or other devices over network 104 from cloud system 106. In some embodiments, engine 200 may be configured and / or installed as an augmenting script, program or application (e.g., a plug-in or extension) to another application or program provided by cloud system 106 and / or executing on UE 112 and / or thermostat 102.

[0069] As illustrated in FIG. IB, according to some embodiments, management engine 200 includes identification module 202, analysis module 204, determination module 206 and control module 208. It should be understood that the engine(s) and modules discussed hereinResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT 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 engine 200 and each of its modules, and their role within embodiments of the present disclosure will be discussed below.

[0070] FIG. 2A illustrates a detailed cross-sectional view of a residential gas water heater, along with various components and their arrangement, as a non-limiting example compatible with the system described herein. At the top of the water heater, the vent allows exhaust gases to escape from the system, while the hot water outlet, positioned near the top of the tank, provides a pathway for hot water to exit the tank and be delivered to the household plumbing system. The cold water inlet, situated at the top of the tank, allows cold water to enter the tank, where it will be heated according to the system and methods described herein.

[0071] To ensure safety, a temperature and pressure relief valve is installed on the side of the tank. Inside the flue, a baffle helps to direct the flow of hot gases, improving heat transfer to the water in the tank. The flue includes a vertical pipe running through the center of the tank, allowing combustion gases to rise and exit through the vent while transferring heat to the surrounding water. Some non-limiting example variables for heat transfer considered by the system are illustrated in FIG. 2B, which shows heat transfer mechanisms such as heat transfer through the upper portion (qUdtPu), through the tank walls (qRdt PR), from the heating element (qPIdt PH), and through the venting system stack or chimney (qSdt Ps).

[0072] Turning back to FIG. 2A, the dip tube extends from the cold water inlet down to the bottom of the tank, ensuring that incoming cold water is delivered to the lower part of the tank for efficient heating. The main body of the water heater, the tank, holds the water to be heated and is surrounded by insulation to minimize heat loss. The outer shell of the water heater, the jacket, encases the tank and insulation, providing structural support and protection.

[0073] To prevent corrosion, an anode rod is inserted into the tank. This rod attracts corrosive elements in the water, thereby extending the life of the tank. At the bottom of the tank, a drain valve allows for the removal of water from the tank for maintenance or replacement. The water heater in this non-limiting example also features a flammable vapor ignition resistant (FVIR) system, configured to prevent the ignition of flammable vapors outside the water heater.

[0074] Positioned at the bottom of the water heater, a burner is configured to heat the water in the tank. A standing pilot light ignites the burner when heating is required. Attached to the gas inlet, a thermostat regulates the temperature of the water by controlling the operation of the burner. In this non-limiting example, the gas inlet supplies natural gas or propane to the burner for combustion and heating of the water. While shown as gas water heater, the system can beResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT implemented in conjunction with an electric water heater, as well as any other type of heat transfer system where the heating element can be selectively controlled (e.g.. turned on and off).

[0075] Referring now to FIG. 3, a temperature vs. time graph as captured by one or more sensors by the disclosed framework is illustrated. In some embodiments, the framework is configured to identify changes in temperature, such as a negative change in temperature over time, as well as the slope of the change, which are used in further calculations, discussed infra. The change is a result of the hot water, stored at a temperature represented by the horizontal line, being driven down as cold water enters the heater to keep the level in the heater constant. In some embodiments, the framework is configured to associate the change with use, which results in hot water leaving the system and cold water entering. In some embodiments, the framew ork records a negative slope as use, a positive slope as heating, a linear portion with a negative slope as use without heat input, a linear portion with a positive slope as heating without use, and / or non-linear portion with a negative slope as combined use and heat input.

[0076] As shown in FIG. 4, models used by the framework to calculate heat transfer rate include flow into the tank, the volume of the tank, and / or the tank material, as non-limiting examples. FIG. 4 also defines the variables used in calculations executed by the disclosed framework in accordance with some embodiments.

[0077] FIG. 5 illustrates a process for determining the flow of water based on the rate of change of the water temperature in a tank, derived form the principle of conservation of energy. In this example, heat transfer is applied to calculate the flow rate by using the interaction between the inflowing water and the water already present in the tank, accounting for temperature variations over time. FIG. 6 shows a mathematical derivation executed by the framework related to extracting flow from the rate of change of the tank temperature, which includes the tank’s mass. Conservation of energy' is also used in the FIG. 6 derivation, where heat transferred or absorbed by the system is related to the mass, specific heat capacity, and temperature changes of the water and the tank.

[0078] Through one or more execute program steps, which may include the use of the Al models described herein, the framework is configured to extract 1st and 2nd derivatives of the temperature data, and / or execute filtering to reduce noise in both the raw readings and their derivatives, as illustrated in FIG. 7. Thresholding may then be applied to distinguish between positive and negative changes in these derivatives.

[0079] Based on the calculated derivatives, the framework is configured to bin the derivatives (changes) into one or more groups: High loss, which includes values between -1.0 and -0.03,Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT represent the moments where the hot water is heavily used. Steady loss (-0.03, -0.015) indicates that the water is used in a steady manner. Low loss (-0.015, -0.0005) shows there is a small consumption of water. A stable derivative (-0.0005, 0.0005) indicates no water consumption. A low rise (0.0005, 0.015) is interpreted by the framework as a small increase in the water temperature due to heating, and may indicate either the heating just started, or there's a water consumption occurring at the same time. A stable rise (0.015, 0.030) shows a larger increase in the water temperature as the result of no or negligible water consumption. The most efficient increase of temperature due to heating is indicated by a high rise (0.03, 1.0), which the framework is configured to associate as definitely no water consumption. FIG. 8 shows a nonlimiting example output of binned data according to some embodiments.

[0080] Turning now to FIG. 9, the illustration shows the framework calculating water usage based on the power needed to heat the water. The heat requested (heat_req) represents the controlled state of the burner, which is on or off in this non-limiting example. The total energy from the burner over a heating period is integrated by the framework and compared to the energy needed to heat the water used. In some embodiments, the framework calculates water use by correlating the amount of energy consumed from a first heating to a second heating with the amount of hot water drawn, yielding a use time for the hot water heater.

[0081] FIG. 10 shows a non-limiting example where the system executes an energy7calculation of the burner and correlates the calculation with the amount of w ater heated. The burner power is given as 38.000 BTU / h. and the total energy consumed over a time interval (1149 seconds) is calculated by converting the power into joules, where 1 BTU - 1055J. Then, using the formula q = mc T, the mass of water heated is determined by solving for m (mass), considering the specific heat capacity7of water (4.182 J / gK) and the temperature difference (45°C). The final conversion from mass to gallons gives the water usage. FIG. 11 shows a generalized form of the example of FIG. 10.

[0082] Referring now to FIG. 12, water temperature and the water temperature’s 1stderivative (gradient) are illustrated, along the heating request (heat_req: on / off) shown as a bar in each graph. The top graph shows the gradient value, depicting changes in temperature over time, the peaks correlating to moments of heating activation. The bottom graph represent the water temperature, which rises when heat requests triggers multiple heating events, visible as spikes in the temperature gradient and increases in the overall temperature, followed by cooling once the heat turns off.

[0083] FIG. 13 depicts a ground truth dataset in accordance with some embodiments. The table on the bottom is a log of various runs with timestamps, w ater usage, flow rates, and heaterResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT activation status (on / off). The graph shows hot water temperature (brown), water consumption (blue), and heating request (red). The temperature increases during water use. corresponding to the heating request, and the data is analyzed to identify these patterns. In some embodiments, one or more labeled data sets, which include the identification of the patterns and / or variables described herein, are used as training data sets for one or more Al models described herein.

[0084] Turning now to FIG. 14, graphs showing the extraction of ater usage from heating request using data from 10 test cycles are depicted in accordance with some embodiments. In this non-limiting example implementation of the system, the first six cycles had the recirculation pump off, w hile the last four had it on. The charts show a comparison of measured versus calculated water usage. The top group doesn’t account for burner efficiency, while the bottom graph adjust for a 39% burner efficiency factor and altitude factor of 80%. The results show that removing the recirculation pump’s influence and including burner efficiency improves the accuracy of water use calculation in accordance with some embodiments.

[0085] In FIG. 15, how7to extract water usage based on the cooling and heating rates of water temperatures is illustrated. In some embodiments, the system is configured to track the heating rate over time to estimate how much water is being heated based on changes in the temperature. The framework is configured to use the positive rate of temperature change over time (dT / dt) to calculate the maximum heating rate. The graph show s w ater temperature fluctuations, and highlights an area where the rate of change in temperature increases.

[0086] FIG. 16 show s a calculation for the expected rate of heating for a water tank 102 using the conservation of energy equation q=mcAT. The input heat energy is provided by the burner (38,000 BTU / h, converted to joules). The water mass (189 kg) and tank mass (100 kg) are multiplied by their respective specific heat capacities (water: 4184 J / kgK, tank 420 J / kgK) to calculate the total energy needed to change the temperature. By solving for AT, the temperature changes rate is determined to be approximately 1.3e - 2 °C / s. This gives the expected rate at which the tank heats up, accounting for energy losses.

[0087] FIG. 17 show s how heating events are extracted using the 1stderivative of the water temperature and the heat request (heafyreq) signal. In some embodiments, the framework identifies when water usage has stopped by detecting a stable derivative. The colored regions represent distinct stages of water heating: low loss, stable, steady loss, low rise, steady rise, and high rise, the ranges of which were discussed supra. The yellow7section highlights the period of constant heating, isolating the heating derivative to analyze burner performance. In some embodiments, the framework executes an analysis to distinguish when the heating isResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT active, which provides insight into how efficiently the burner operates during heating events, and is used to adjust one or more control functions described herein.

[0088] The graph depicted in FIG. 18 illustrates the statistics for the rate of change in water temperature during heating. The lines represent the minimum, maximum, mean, and standard deviation of the temperature gradient over time. The mean and standard deviation help the framework identify trends and variations in heating behavior. In some embodiments, the framework is configured to use these statistics to provide an early warning of potential tank and / or burner failures by reporting abnormal deviations from expected heating patterns. In some embodiments, the framework is configured to automatically close one or more valves in response to an abnormal pattern. In some embodiments, the framework is configured to disable one or more heater functions in response to an abnormal pattern. In some embodiments, the framework is configured to turn a heater off and / or not allow a heating element to be turned on in response to an abnormal pattern.

[0089] FIG. 19 demonstrates the framework’s execution of an adaptive setpoint resulting in saved energy by adjusting the water tank temperature based on usage probability. In some embodiments, the framework is configured to learn, by example, through one or more Al models, when and / or how much hot water is used. In some embodiments, the framework is configured to set the tank to provide hot water only during expected usage periods. In some embodiments, the framework executes computer instructions to reduce the tank’s standby temperature when hot water is not needed. As illustrated, a line shows a standard heating profile, as noted in the figure, while another line, as noted in the figure, shows the adaptive setpoint profile. By lowering the temperature during periods of no use, and raising the temperature before expected demand, the system optimized energy usage without sacrificing hot water availability.

[0090] According to some embodiments, in graph 250 in FIG. 19, depicted is the standard heating profile over time for a water heater at a location, and its corresponding R-value loss. And, in graph 252 in FIG. 19, depicted is the adaptive heating profile provided via operation of the disclosed framework for the water heater at the location. As provided, there is a lower standby temperature, which improves energy efficiency of the water heater, as discussed herein. That is, as provided in more detail below, according to some embodiments, by adaptively stopping the heating of the tank at a lower temperature, which can be predicted by past use to supply adequate hot water (energy), and then heating prior to a learned use demand period, the disclosed systems and methods provide functionality that enables the standby temperature to be optimized for energy loss.Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT

[0091] FIG. 20 illustrates computer implemented steps by the framework in accordance with some embodiments. Some embodiments include a step to identify periods with a high probability of hot water use. Some embodiments includes a step of preheating the water tank in advance of the water use. In some embodiments, during low probability periods, the framework is configured to maintain the water tank at a lower standby temperature to reduce unnecessary heating. In some embodiments, in the framework is configured to not energize a heating element between one or more periods of high probability of hot water use. Some embodiments include a step to reactivate the pre-heating cycle before the next expected high- probability water use to ensure availability of hot water without wasting energy.

[0092] Turning now to FIG. 21, a Markov Probablity Model shows how the probability of hot water use is extracted by comparing two methods: derivatives and heat requested. The top chart shows the extraction without a recirculation pump, while the bottom chart includes a recirculation pump. The left-side graphs use derivatives for analysis, while the right-side graphs use heat requested. In some embodiments, the derivatives calculation can identify7and exclude the influence of the recirculation pump, providing a more accurate measurement of actual water use. This novel implementation improves the framework's ability to predict water usage patterns, providing a practical application for the calculation.

[0093] Steps the framework executes to extract water standby loss during periods of no water use by measuring the rate of temperature decrease over time (-dT / dt) are illustrated in FIG. 22. In some embodiments, the average standby loss is calculated by analyzing these temperature drops, which are then related to energy loss. The graph shows how the water temperature decreases during standby, and how the framework tracks these changes over time to monitor and calculate the standby energy7losses, which provides insight into how much energy7is lost when the water is not being used. In some embodiments, the system is configured to mark changes in the graph with breaks in the trend (indicated by arrows), and / or assign the changes as groups.

[0094] Turning now to FIG. 23, the graphs shows a preliminary analysis of standby loss by extracting the rate of temperature changes during periods of no water use. The results shown by the red track (tank water temperature) reflect both negative and positive rates of temperature change. Fluctuations are due to complex thermal processes such as stirring, diffusion, and temperature equilibration within the water tank. The presence of a pilot light might also influence these observations, causing minor temperature changes, which the framework is configured to take into account, along with one or more other identified fluctuations, when determining high-probability7(e.g., > 80%) water usage events.Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT

[0095] FIG. 24 illustrates the framework executing a calculation of the mass of a water tank 102, described supra in relation to FIG. 2B, using the tank's dimensions and material properties. The tank's volume and surface area are calculated using geometric formulas for various sections. In some embodiments, the total volume is configured to mass using the density of steel (7850 kg / m?), for example, with an assumption the tank’s skin is 4mm thick. In some embodiments, the framework is configured to enable a use to input one or more measurements. In some embodiments, the framework is configured to enable a user, on a graphical user interface (GUI), to input a heater name and / or model, where the system is configured to retrieve stored measurements from a database. The calculation in this example results in a mass of approximately 151bs for 4mm a steal tank skin and 22.6 lbs for 6mm steel tank skin.

[0096] As provided via the operations of engine 200, discussed in more detail below in FIG. 25 and FIG. 26, infra, the disclosed framework’s operation can result in savings of approximately $25 per gas water heater per year in energy costs. For example, assuming water heater heats to 130F and ambient room temperature is 70F (60F delta), with two full supply hot water windows of 4 hours each, then 16 hours or 67% of the day the water temperature can be reduced, which results in a 15F reduction in standby temperature (e g., a 25% reduction in loss). Thus, with a typical water heater standby loss at $100 - $200 / year, and approximate adaptive heating savings = 67% x 25% x $150 / year = $25 / yr. Moreover, such energy- savings in context involves: savings is about 82.5 lbs of CO2 per water heater per year, which is a significant impact on the climate, in general.

[0097] In FIG. 25 and FIG. 26, Processes 300 and 350, respectively, provide non-limiting example embodiments of the disclosed framework that provides features, capabilities and / or functionality for enhanced and / or improved water heater functionality. As discussed herein, operation, via engine 200, can involve adaptively determining and leveraging temperature set points that enable the storage of a value of energy for a standby time, whereby water can be preheated to address predicted energy demand periods in a manner that ensures it is available when needed, as discussed herein.

[0098] As discussed herein, the disclosed framework operates to read water temperature and change settings for the water heater such as the water temperature set points. By monitoring a user’s water heater use, engine 200 can learn the “use” profile of the water heater and develop a model of what water temperature is required through the day and night for a given user (and / or at a location). Such processing is provided via Process 300 in FIG. 25. Subsequently, as the water heater water cools slowly (at or below a threshold rate of reduction in temperature),Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT operations by engine 200 via Process 350 in FIG. 26 can provide control of the heating cycle endpoint, such that the standby water temperature is set to a lower level and to a level that provides adequate reserve heat during stand by. By setting the end point level lower than the traditional heating cycle end point, energy can be saved due to reduced thermal loss through the tank insulation, corresponding R-Value, and stack effect losses. The standby water temperature can then be predictively increased to an optimal heat and or energy’ level as demanded and as predicted by the learned use model, which use various calculations and models depicted in FIGs. 2-24, discussed supra.

[0099] Turning to FIG. 25, according to some embodiments, Steps 302 and 304 of Process 300 can be performed by identification module 202 of management engine 200; Step 306 can be performed by analysis module 204; Step 308 can be performed by determination module 206; and Step 310 can be performed by control module 208.

[0100] According to some embodiments, Process 300 begins with Step 302 where engine 200 can monitor usage of the water heater. According to some embodiments, such monitoring can occur according to, but not limited to, periodically, continuously, a criteria, a detected event, request, and the like, or some combination thereof. In some embodiments, such criteria can correspond to, but is not limited to, measurements, a time period, date, user identity (ID), threshold values (e.g., measurements meet or satisfy a threshold - for example, a temperature is at or below a threshold, for example), mode / settings on the water heater (and / or thermostat), and the like, or some combination thereof.

[0101] In Step 304, engine 200 can collect usage data for the water heater based on the monitored time periods. According to some embodiments, the collection can be specific to sensors and / or devices within and / or associated with the water heater, which can correspond to and / or indicate data related to the detected measurements / values, modes, type, timestamps, and the like, or some combination thereof. In some embodiments, the collected usage data can be stored in database 108, as discussed above.

[0102] In some embodiments, water heater usage data can provide valuable insights for optimizing performance and efficiency. Such data can include, but is not limited to, water temperature at various points in the system, such as inlet, outlet, and tank temperatures, which are recorded at regular intervals. Flow rates and volume of hot water consumed can also be tracked, often broken down by time of day, day of the week, and season, to identity’ usage patterns. Energy consumption data, whether gas or electricity', can be collected to monitor efficiency and costs. The duration and frequency of heating cycles can be logged, along with recovery times after major draws. Water pressure readings can indicate system health andResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT potential leaks. Some advanced systems may also track water quality parameters like hardness or pH levels. Additionally, data on ambient temperature and humidity around the water heater can be collected to understand environmental impacts on performance. This comprehensive dataset, among other types of data, allows for detailed analysis of the water heater's operation, enabling informed decisions on maintenance, upgrades, and usage habits to improve efficiency and reduce costs, as discussed herein.

[0103] In Step 306, engine 200 can analyze the collected data. In some embodiments, the data can be parsed, whereby data and / or metadata related to a detected event can be identified and / or extracted from the data. For example, event information can be identified, which can be related to, but not limited to, the sensor / device collecting the data, position within the location (e.g., which room, which floor, and the like) of the sensor / device. the measurement, time period, and the like, or some combination thereof.

[0104] Thus, in Step 306, engine 200 can analyze the collected usage data to determine patterns of water heater usage, as in Step 308. For example, water heater usage patterns can fall into several categories based on location behaviors and routines. For example, morning peak usage is common in many households, with high demand for showers and kitchen use. In another example, evening peaks often occur as families return home, using hot water for cooking, cleaning, and bathing. And, in another non-limiting example, weekend patterns often differ from weekdays, with more sporadic usage throughout the day. Seasonal variations may also be identified, with higher usage and longer heating cycles in colder months. As discussed herein, identifying such patterns helps in optimizing water heater settings, sizing systems appropriately, and implementing energy-saving strategies tailored to specific usage behaviors.

[0105] In some embodiments, such patterns can be compiled, created and / or determined / identified as a data structure, that can be input into a model for further computational analysis and control of the water heater, as discussed infra.

[0106] In some embodiments, Steps 306 and 308 can be performed in a single operational step by engine 200; and, in some embodiments, Steps 306 and 308 can be performed in any operational order.

[0107] In some embodiments, engine 200 can implement any type of known or to be known computational analysis technique, algorithm, mechanism or technology to perform the analysis and determination in Steps 306-308.

[0108] In some embodiments, engine 200 may include a specific trained artificial intelligence / machine learning model (AI / ML). a particular machine learning model architecture, a particular machine learning model type (e.g., convolutional neural network (CNN), recurrentResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT neural network (RNN), autoencoder, support vector machine (SVM), and the like), or any other suitable definition of a machine learning model or any suitable combination thereof.

[0109] In some embodiments, engine 200 may be configured to utilize one or more AI / 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. By way of a non-limiting example, engine 200 can implement an XGBoost algorithm for regression and / or classification to analyze the data, as discussed herein.

[0110] According to 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 N eural N et work architecture / model , 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.[OHl] In some embodiments and, optionally, in combination of any embodiment described above or below, the trained neural network 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 neural network 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 a node 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,Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT 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 any embodiment 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.

[0112] In Step 310, in some embodiments, the determined information from Step 306 and in Step 308 can be stored in a profile in database 108, as discussed above. In some embodiments, the profile can be specific to a user(s), water heater, location, and the like, or some combination thereof. In some embodiments, the profile can be created, compiled, updated, and the like, and can be used to store the data related to, but not limited to, the determined pattern data structures, and / or information about the water heater (e.g.. model, version, characteristics / specifications. and the like) and / or the location (e.g., climate of the location and / or where in the location is the water heater), and the like. As provided below, the stored profile information can be utilized to adaptively manage the operations and controls of the water heater to effectuate an efficient energy usage.

[0113] Turning to FIG. 26, according to some embodiments. Steps 352-356 can be performed by identification module 202 of management engine 200; Step 358 can be performed by analysis module 204; Step 360 can be performed by determination module 206; and Steps 362 and 364 can be performed by control module 208.

[0114] According to some embodiments, Process 350 begins with Step 352 where engine 200 can monitor the usage of the water heater. Such monitoring can be performed in a similar manner as discussed respective to Step 302 of Process 300 in FIG. 25, discussed supra.

[0115] In Step 354, engine 200, based on the monitoring, can determine an event related to the usage of the water heater. Such event can be based on, but not limited to. a request (e.g., set or activated mode of usage, for example), a time, date, user ID, pattern of activity, and the like, or some combination thereof. For example, such event can be based on a determination that an external (or internal) temperature at the location has dropped below a threshold temperature value.

[0116] In Step 356, engine 200 can retrieve information from the profile associated with the water heater based on the event. Such information can include, but not be limited to, a pattern data structure, user information, water heater model information, and the like, for example. For example, from the above example, if the temperature external to a home is at or below a threshold, then a pattern of water heater usage during similar external climates can be retrievedResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT from the profile. In some embodiments, such retrieval can involve engine 200 creating a query that is executed on database 108 to retrieve such requested information.

[0117] In Step 358, engine 200 can analyze the information related to the event based on the retrieved information. Such analysis can involve the AI / ML based computational analysis performed in Steps 306-308, discussed supra. For example, engine 200 can provide the retrieved information and event data / metadata (e.g.. timestamp, values associated with the event, and the like) as input to an AI / ML model.

[0118] In Step 360, based on the computational analysis in Step 358, engine 200 can determine operational values of the water heater. Such operational values can be in relation to, but not limited to, standby water temperature levels, standby timing, heating cycle timing, R-values, thermal values, power usage. For example, engine 200 can determine to heat the water in standby mode to n temperature for k minutes before releasing the water via the water heater throughout the location (e.g., upon a demand, for example).

[0119] In Step 362, engine 200 can execute or run the water heater based on the determined operational values. For example, by setting the temperature end point value to a determined value prior to a requested heating cycle (e.g.. from Step 354), energy can be saved due to reduced thermal loss through the tank insulation and corresponding R-V alue. The standby water temperature can then be predictively increased via the determined operational values and runtime executed (e.g., via Steps 360 and 362) to an optimal heat and or energy level as demanded and as predicted by the learned use model (e.g.. as depicted in FIG. 2-24, discussed supra). In some embodiments, such runtime can cause further monitoring via Process 350, as indicated via the recursive arrow in FIG. 26 from Step 362 to Step 352.

[0120] And, in Step 364, information related to the operational values (and in some embodiments, the runtime in Step 362. can be stored in the profile, in database 108, as discussed above. This can be used to update the patterns, via the steps of Process 300, discussed supra.

[0121] FIG. 29 is a schematic diagram illustrating a client device showing an example embodiment of a client device that may be used within the present disclosure. Client device 600 may include many more or less components than those shown in FIG. 29. However, the components shown are sufficient to disclose an illustrative embodiment for implementing the present disclosure. Client device 600 may represent, for example, UE 112 discussed above at least in relation to FIG. 1A.

[0122] As shown in the figure, in some embodiments, Client device 600 includes a processing unit (CPU) 622 in communication with a mass memory 630 via a bus 624. Client device 600 also includes a power supply 626, one or more network interfaces 650, an audio interface 652,Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT a display 654, a keypad 656, an illuminator 658, an input / output interface 660, ahaptic interface 662, an optional global positioning systems (GPS) receiver 664 and a camera(s) or other optical, thermal or electromagnetic sensors 666. Device 600 can include one camera / sensor 666, or a plurality of cameras / sensors 666, as understood by those of skill in the art. Power supply 626 provides power to Client device 600.

[0123] Client device 600 may optionally communicate with a base station (not shown), or directly with another computing device. In some embodiments, network interface 650 is sometimes known as a transceiver, transceiving device, or netw ork interface card (NIC).

[0124] Audio interface 652 is arranged to produce and receive audio signals such as the sound of a human voice in some embodiments. Display 654 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 654 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.

[0125] Keypad 656 may include any input device arranged to receive input from a user. Illuminator 658 may provide a status indication and / or provide light.

[0126] Client device 600 also includes input / output interface 660 for communicating with external. Input / output interface 660 can utilize one or more communication technologies, such as USB, infrared, Bluetooth™, or the like in some embodiments. Haptic interface 662 is arranged to provide tactile feedback to a user of the client device.

[0127] Optional GPS transceiver 664 can determine the physical coordinates of Client device 600 on the surface of the Earth, which typically outputs a location as latitude and longitude values. GPS transceiver 664 can also employ other geo-positioning mechanisms, including, but not limited to, triangulation, assisted GPS (AGPS), E-OTD. CI, SAI, ETA, BSS or the like, to further determine the physical location of client device 600 on the surface of the Earth. In one embodiment, however, Client device 600 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.

[0128] Mass memory 630 includes a RAM 632, a ROM 634, and other storage means. Mass memory 630 illustrates another example of computer storage media for storage of information such as computer readable instructions, data structures, program modules or other data. Mass memory 630 stores a basic input / output system (“BIOS”) 640 for controlling low-level operation of Client device 600. The mass memory also stores an operating system 641 for controlling the operation of Client device 600.Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT

[0129] Memory' 630 further includes one or more data stores, which can be utilized by Client device 600 to store, among other things, applications 642 and / or other information or data. For example, data stores may be employed to store information that describes various capabilities of Client device 600. The information may then be provided to another device based on any of a variety7of 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 of the capability information may also be stored on a disk drive or other storage medium (not shown) within Client device 600.

[0130] Applications 642 may include computer executable instructions which, when executed by Client device 600, transmit, receive, and / or otherwise process audio, video, images, and enable telecommunication with a server and / or another user of another client device. Applications 642 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 engine 200 and its affiliates.

[0131] As used herein, the terms “computer 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).

[0132] Examples of hardware elements may include processors, 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, microcontroller (MCU), 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). and so forth.

[0133] 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 codeResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT 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, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

[0134] 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.

[0135] 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,’7may 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 hardw are and / or computing software languages (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, and the like).

[0136] 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 softw are 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 software specifically programmed in accordance with one or more principles of the present disclosure may also be embodied as a software package installed on a hardware device.

[0137] In an aspect, the disclosure is directed to a system and methods that include one or more of a water heater, a temperature sensor, and one or more computers comprising one or more processors and one or more non-transitory computer readable media, the one or more non- transitory computer readable media including program instructions stored thereon that when executed by the one or more processors cause the one or more computer to execute one or moreResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT steps. Some embodiments includes a step of monitor the temperature sensor. Some embodiments includes a step to store one or more temperature changes over time as detected by the temperature sensor. Some embodiments includes a step to determine a gradient change for each of the one or more temperature changes. Some embodiments includes a step to compare each gradient change to one or more gradient ranges, the one or more gradient ranges each defining a gradient grouping. Some embodiments includes a step to assign each of the one or more temperature changes to the gradient grouping based on the comparison. Some embodiments includes a step to predict a water usage timeframe based on one or more gradient groupings. Some embodiments includes a step to initiate a heating element of the water heater before the predicted water usage time.

[0138] In some embodiments, the one or more non-transitory computer readable media further include program instructions stored thereon that when executed by the one or more processors cause the one or more computers to predict a water non-usage timeframe based on one or more gradient grouping. Some embodiments includes a step to prevent an initiating of the heating element during the water non-usage timeframe. Some embodiments includes a step to adjust a standby-temperature of the water heater based on the water non-usage timeframe. Some embodiments includes a step to adjust a standby -temperature of the water heater to a lower setting during the water non-usage timeframe. In some embodiments, the water usage timeframe includes a high water usage timeframe and a low water usage time frame. Some embodiments includes a step to predict the high water usage timeframe based on the one or more gradient groupings. Some embodiments includes a step to predict the low water usage timeframe based on the one or more gradient groupings, the low water usage timeframe including water usage that is less than the high water usage timeframe.

[0139] In some embodiments, the one or more non-transitory computer readable media further include program instructions stored thereon that when executed by the one or more processors cause the one or more computers to control the heating element differently based on whether the predicted water usage includes a high water usage timeframe or a low water usage timeframe. In some embodiments, the high water usage timeframe includes a drop in temperature that is at least double a drop in temperature of a low water usage timeframe.

[0140] 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 who receives data provided by the data or sendee provider over the Internet in a browser session, or can refer to an automatedResideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT 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 exemplary embodiments 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.

[0141] 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.

[0142] Furthermore, the embodiments of methods presented and described as flow charts 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.

[0143] 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

Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCTCLAIMSWhat is claimed is:

1. A method comprising steps for: monitoring a temperature sensor coupled to a water heater; storing one or more temperature changes over time as detected by the temperature sensor; determining a gradient change for each of the one or more temperature changes; comparing each gradient change to one or more gradient ranges, the one or more gradient ranges each defining a gradient grouping; assigning each of the one or more temperature changes to a gradient grouping based on the comparing; predicting a water usage timeframe based on one or more gradient groupings; and initiating a heating element of the water heater before the predicted water usage timeframe.

2. The method of claim 1, further comprising: predicting a water non-usage timeframe based on one or more gradient grouping.

3. The method of claim 2. further comprising: preventing an initiating of the heating element during the water non-usage timeframe.

4. The method of claim 2, further comprising: adjusting a standby-temperature of the water heater based on the water non-usage timeframe.

5. The method of claim 2, further comprising: adjusting a standby -temperature of the water heater to a lower setting during the water non-usage timeframe.

6. The method of claim 5, wherein the water usage timeframe includes a high water usage timeframe and a low water usage timeframe, wherein the steps further comprise: predicting the high water usage timeframe based on the one or more gradient groupings; andResideo Ref. No. R214373-WO Attorney Docket No. 203863-017802 / PCT predicting the low water usage timeframe based on the one or more gradient groupings, the low water usage timeframe including water usage that is less than the high water usage timeframe.

7. The method of claim 6, further comprising: controlling the heating element differently based on whether the predicted water usage timeframe is the high water usage timeframe or a low water usage timeframe.

8. The method of claim 7, wherein the high water usage timeframe includes a drop in temperature that is at least double a drop in temperature of the low water usage timeframe.

9. A system comprising: a water heater, a temperature sensor coupled to the water heater, and a processor configured to: monitor the temperature sensor; store one or more temperature changes over time as detected by the temperature sensor; determine a gradient change for each of the one or more temperature changes; compare each gradient change to one or more gradient ranges, the one or more gradient ranges each defining a gradient grouping; assign each of the one or more temperature changes to the gradient grouping based on the comparison; predict a water usage timeframe based on one or more gradient groupings; and initiate a heating element of the water heater before the predicted water usage time.

10. The system of claim 9, wherein the processor is further configured to: predict a water non-usage timeframe based on one or more gradient grouping.

11. The system of claim 10, wherein the processor is further configured to: prevent an initiating of the heating element during the water non-usage timeframe.Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT12. The system of claim 10, wherein the processor is further configured to: adjust a standby-temperature of the water heater based on the water non-usage timeframe.

13. The system of claim 10, wherein the processor is further configured to: adjust a standby-temperature of the water heater to a lower setting during the water non-usage timeframe.

14. The system of claim 13, wherein the water usage timeframe includes a high water usage timeframe and a low water usage time frame: wherein the processor is further configured to: predict the high water usage timeframe based on the one or more gradient groupings; and predict the low water usage timeframe based on the one or more gradient groupings, the low water usage timeframe including water usage that is less than the high water usage timeframe.

15. The system of claim 14, wherein the processor is further configured to: control the heating element differently based on whether the predicted water usage includes a high water usage timeframe or a low water usage timeframe.

16. The system of claim 15, wherein the high water usage timeframe includes a drop in temperature that is at least double a drop in temperature of a low water usage timeframe.

17. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a processor, perform a method comprising: monitoring a temperature sensor coupled to a water heater; storing one or more temperature changes over time as detected by the temperature sensor; determining a gradient change for each of the one or more temperature changes; comparing each gradient change to one or more gradient ranges, the one or more gradient ranges each defining a gradient grouping;Resideo Ref. No. R214373-WO Attorney Docket No. 203863-017802 / PCT assigning the each of the one or more temperature changes to the gradient grouping based on the comparing; predicting a water usage timeframe based on one or more gradient groupings; and initiating a heating element of the water heater before the predicted water usage time.

18. The non-transitory computer-readable storage medium of claim 17, further comprising instructions for: predicting a water non-usage timeframe based on one or more gradient grouping.

19. The non-transitory computer-readable storage medium of claim 18, further comprising instructions for: preventing an initiating of the heating element during the water non-usage timeframe.

20. The non-transitory computer-readable storage medium of claim 19, further comprising instructions for: adjusting a standby-temperature of the water heater based on the water non-usage timeframe.

21. A method comprising: detecting an event related to usage of a water heater at a location, the event corresponding to at least one of a real-world condition and action of the water heater; identifying, based on the event, a pattern of activity of the water heater, the pattern corresponding to previous actions performed by the water heater for at least one event that involved a similar real-world condition or action of the water heater; determining, based on an analysis of the event and the pattern of activity, operational values of the water heater; and causing operation of the water heater via the operational values.

22. The method of claim 21, further comprising: analyzing information related to the event based on the pattern of activity' of the water heater; and performing the determination based on the analysis of the information related to the event.Resideo Ref. No. R214373-WOAttorney Docket No. 203863-017802 / PCT23. The method of claim 21, further comprising: monitoring, for at least one time period, activity of the water heater at the location; collecting, based on the monitoring, usage data of the water heater; analyzing the usage data; and determining a set of patterns of activity' for the water heater.

24. The method of claim 23. wherein the identified pattern of activity is one of the set of patterns of activity.

25. The method of claim 23, further comprising: storing the set of patterns of activity as a data structure in a profile related to the water heater; and retrieving, from the profile, a data structure related to the identified pattern of activity.

26. The method of claim 21, wherein the operational values of the water heater corresponds to at least one of standby water temperature levels, standby timing, heating cycle timing, R-values, thermal values and power usage.

27. The method of claim 21, further comprising: monitoring, upon the operation of the water heater via the operational values, temperature values of water in a tank of the water heater; determining temperature values are changing at a threshold rate of change; and causing release of the water via the water heater via pipes in the location.

28. The method of claim 21, wherein the event corresponds to a request for the water heater to enter a mode to heat water to a requested temperature.

29. A device comprising: a processor configured to: detect an event related to usage of a water heater at a location, the event corresponding to at least one of a real-world condition and action of the water heater; identify, based on the event, a pattern of activity of the water heater, the pattern corresponding to previous actions performed by the water heater for at least one event that involved a similar real-world condition or action of the water heater;Resideo Ref. No. R214373-WO Attorney Docket No. 203863-017802 / PCT determine, based on an analysis of the event and the pattern of activity, operational values of the water heater; and cause operation of the water heater via the operational values.

30. The device of claim 29, wherein the processor is further configured to: analyze information related to the event based on the pattern of activity of the water heater; and perform the determination based on the analysis of the information related to the event.

31. The device of claim 29, wherein the processor is further configured to: monitor, for at least one time period, activity of the water heater at the location; collect, based on the monitoring, usage data of the water heater; analyze the usage data; and determine a set of patterns of activity for the water heater.

32. The device of claim 31, wherein the identified pattern of activity is one of the set of patterns of activity7.

33. The device of claim 31, wherein the processor is further configured to: store the set of patterns of activity as a data structure in a profile related to the water heater; and retrieve, from the profile, a data structure related to the identified pattern of activity.

34. The device of claim 29. wherein the operational values of the water heater corresponds to at least one of standby water temperature levels, standby timing, heating cycle timing, R-values, thermal values and power usage.

35. The device of claim 29, wherein the processor is further configured to: monitor, upon the operation of the water heater via the operational values, temperature values of water in a tank of the water heater; determine temperature values are changing at a threshold rate of change; and cause release of the water via the water heater via pipes in the location.Resideo Ref. No. R214373-WO Attorney Docket No. 203863-017802 / PCT36. The device of claim 29, wherein the event corresponds to a request for the water heater to enter a mode to heat water to a requested temperature.

37. The device of claim 29, wherein the device is a water heater.

38. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a processor, perform a method comprising: detecting an event related to usage of a water heater at a location, the event corresponding to at least one of a real-world condition and action of the water heater; identifying, based on the event, a pattern of activity of the water heater, the pattern corresponding to previous actions performed by the water heater for at least one event that involved a similar real-world condition or action of the water heater; determining, based on an analysis of the event and the pattern of activity, operational values of the water heater; and causing operation of the water heater via the operational values.

39. The non-transitory computer-readable storage medium of claim 38, further comprising: analyzing information related to the event based on the pattern of activity of the water heater; and performing the determination based on the analysis of the information related to the event.

40. The non-transitory computer-readable storage medium of claim 38, further comprising: monitoring, for at least one time period, activity of the water heater at the location; collecting, based on the monitoring, usage data of the water heater; analyzing the usage data: determining a set of patterns of activity for the water heater; and storing the set of patterns of activity as a data structure in a profile related to the water heater.

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