HIGH EFFICIENCY WATER HEATER

FIELD

This invention relates to the field of providing hot water and more particularly to a system, apparatus, and method for predictively heating hot water.

BACKGROUND

It has been estimated that water heaters account for up to 30 percent of an average homes energy budget. Currently, there are two main classifications of home and industrial water heaters. The first main classification of water heater is known as a “convention storage tank” water heater. This type of water heater is commonly used in many homes and businesses and utilizes a source of heat and a storage tank. Heat from a source such as electrical heating elements or fossil fuel burners increases the temperature of water in the storage tank until it reaches a pre-determined temperature, at which time the heat source is shut off (conserving energy) until the temperature of the water in the storage tank drops to a second pre-determined temperature. Such systems provide for large, transient demands for hot water by providing large storage tanks or heating the water in the storage tanks to a high temperature, then mixing the hot water with unheated water before distributing the water to the end users. Although the storage tanks are typically thermally insulated, these systems lose efficiency due to heat loss due to conduction from the storage tanks to the ambient environment and through the plumbing that connects the storage tanks to the water supply and delivery plumbing, especially in situations where there are extended periods of time during which no hot water is used. Such situations occur, for example, in a residence when household members are at work or sleeping, or in an industrial facility when all of the workers are home.

The second main classification of water heater is known as an “on-demand” water heater. This type of water heater has no storage tank, instead having a very high energy output heating element that is capable of raising the temperature of the water from ground water temperature to the desired hot water temperature as the hot water is used. Typically, the on-demand water heater has a flow sensor and a heating element such that, as soon as demand for hot water occurs, the sensor detects that water is flowing through the hot water plumbing and enables the heating element, which heats the water as the water flows through the heating element. This class of water heaters is more efficient than storage-tank based water heaters because there is little or no energy loss when no hot water is being used. This class of water heaters has its own set of drawbacks. These water heaters require a large amount of energy during operation to enable the quick heating of water from ground water temperatures to the desired hot water temperature at a given flow rate. As a result of this, such on-demand water heaters are often rated for a certain rise in temperature for a given flow rate. For example, one on-demand water heater is capable of a 45 F rise in temperature at a flow rate of 8.5 gallons per minute, but only a 35 F rise in temperature at a 9.5 gallon per minute rate. Therefore, if several people are concurrently taking a shower, the hot water will not be as hot. Furthermore, because there is no storage tank containing pre-heated water, if there is a power failure, there will be no hot water until power is restored. Another setback of on-demand water heaters is supplying sufficient power for the heating elements. Natural gas is often used because it provides a high amount of BTUs, but not all homes have natural gas. Electricity is more widely available, but most existing buildings do not have sufficient power and/or wiring for whole-house on-demand water heaters.

The storage-tank based hot water system has many advantages such as supplying sufficient hot water for most anticipated demands, constant hot water temperatures, providing a fair amount of hot water during a power outage, etc. Therefore, storage-tank based hot water systems will continue to be used.

Prior attempts at making improvements involved simple timers that were manually set to prevent water heating during periods of no demand (e.g. when families are sleeping), systems that understand variable rate plans (e.g. electricity costs more during peak periods) to adjust heating patterns to the electricity costs, and systems that are remotely controlled by the electric companies to reduce electricity consumption during high demand periods, therefore preventing a grid overload.

What is needed is a system that will improve the overall efficiency of the storage-tank based hot water heater systems by predicting future usage patterns.

SUMMARY

In one embodiment, a high-efficiency water heating system is disclosed including at least one source of heat and a processor interfaced to the at least one source of heat, The processor controls the operation of the at least one source of heat (e.g. energizes the at least one heating element to provide heat to the water). At least one source of data related to a consumption of hot water from the high-efficiency water heating system is provided and software running on the processor analyzes the data and calculates a predicted demand for the hot water based upon the data, then controls the operation of the at least one source of heat responsive to the predicted demand.

In another embodiment, a method of providing hot water is disclosed including (a) calculating a predicted demand for hot water over a period of time, then, (b) prior to and during the period of time, energizing one or more sources of heat based upon the predicted demand, the sources of heat interfaced to a water supply such that the source of heat provide heat to water from the water supply and produce a quantity of the heated water. (c) A rate of flow of the heated water during the period of time is measured; and (d) the rate of flow of the heated water is fed back into the step of calculating to improve the accuracy of the step of calculating. Steps a-e are repeated.

In another embodiment, a high-efficiency water heating system is disclosed including at least one source of heat operatively coupled to a water storage tank. When the at least one source of heat is energized, heat is transferred into water within the storage tank. At least one on-demand heating device (e.g. gas burner or electric element) is connected to a supply of water such that, when the at least one on-demand heating element is energized, heat is transferred into water from the supply of water by the at least one on-demand heating device. Two valves control the flow of water: a first electrically controlled valve connecting the output of the storage tank to supply the heated water and a second electrically controlled valve connecting the output of the at least one on-demand heating device to supply the heated water. A processor is interfaced to the at least one source of heat, to the at least one on-demand heating device, to the first valve, and to the second valve, the processor controlling the operation of the at least one source of heat, the at least one on-demand heating device, the first valve, and the second valve. A flow sensor is operatively coupled to the processor, providing a measurement of current demand for the heated water. At least one source of data related to a consumption of the hot water from the high-efficiency water heating system is provided. Software running on the processor analyzes the data and calculates a predicted demand for the hot water and controls the operation of the at least one source of heat, the at least one on-demand heating device, the first valve, and the second valve, responsive to the predicted demand.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be best understood by those having ordinary skill in the art by reference to the following detailed description when considered in conjunction with the accompanying drawings in which:

FIG. 1 illustrates a schematic view of a high-efficiency hot water heater software system.

FIG. 2 illustrates a schematic view of the high-efficiency hot water heater decision process.

FIG. 3 illustrates a schematic view of a typical computer system of the high-efficiency hot water heater.

FIG. 4A illustrates a schematic view of a typical storage-tank hot water heater of the prior art.

FIG. 4B illustrates a schematic view of a typical on-demand hot water heater of the prior art.

FIG. 5 illustrates a simplified schematic view of the high-efficiency hot water heater.

FIG. 6 illustrates a second schematic view of the high-efficiency hot water heater.

FIG. 7 illustrates a flow chart of a storage-tank hot water heater of the prior art.

FIG. 8 illustrates a flow chart of an on-demand hot water heater of the prior art.

FIG. 9 illustrates a flow chart of a high-efficiency hot water heater.

FIG. 10 illustrates a schematic diagram of a neural-network-based high-efficiency hot water heater.

DETAILED DESCRIPTION

Reference will now be made in detail to the presently preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings. Throughout the following detailed description, the same reference numerals refer to the same elements in all figures.

Throughout this description, the term “heating element” refers to any type of heating element, including, but not limited to, electric heating elements, gas burners, oil burners, etc. The heating element(s), when energized, provide heat to a body of water. Note that the colloquial meaning of water includes H₂0 and any impurities present in a water supply such as other chemicals, minerals, solids, and dissolved gasses.

Throughout the description, the terms “hot water” and “heated water” refers to water that has been heated above the ambient water temperature as one would expect to find when opening a left spigot on a sink or one that is marked ‘H’ in English speaking countries like the United States (note, other marks exist for other countries such as ‘C’ for Caldo in Italy).

Throughout the description, embodiments are shown having a certain number and type/location of storage tanks, heating elements and valves. The high-efficiency hot water heater is not restricted in any way to any particular number of storage tanks, heating elements or heat sources, and/or valves. For example, in some embodiments, there are no valves and in some embodiments, there is only one “mixing” valve. The high-efficiency hot water heater is not restricted in any way to any specific source of heat configuration/combination. For example, some storage tank systems have upper and lower heating elements while others have only a single gas burner, while still other systems have heat sources (e.g. boilers, solar panels), that are external to the storage tanks. There are no restrictions as to the types of heat sources and/or locations of the heat sources.

Although described as a processor-based system, as known, any processor-based system is capable of being made using discrete components (e.g. gates) and such implementations are anticipated and included here within.

Any form of control of the valves and heating elements is anticipated, including, but not limited to, electrical control through individual wiring, electrical control through a wired network, electrical control through a wireless system, pneumatic control, etc.

Throughout this description, various examples of home and/or industrial water heating scenarios are provided. The disclosed and claimed invention is not limited in any way to a particular application and is intended for use in any water heating application.

Referring to FIG. 1, a schematic view of a high-efficiency hot water heater software system is shown. The system shown in FIG. 1 applies to some embodiments of the high-efficiency hot water heater software system. Although the high-efficiency hot water heater software system is intended to operate with or without a network 10, it is shown interfaced to the Internet 10 (a.k.a. the World Wide Web).

In this embodiment, one or more heater controllers 20 are connected to the network 10 as known in the industry. One or more servers 40 are also connected to the network 10 as known in the industry. The high-efficiency hot water heater software system includes data 42 for authentication as well as history, etc. It is anticipated that any or all of the storage areas 42 are locally interfaced to the server 40, remotely interfaced to the server 40 (e.g., Network Attached Storage—NAS) and/or remotely interfaced to the server 40 over a network, either a local area network or wide area network.

In some embodiments, the server 40 or the individual heater controllers 20 also interfaces to various providers of external data 50. The information providers 50 are interfaced to the server 40 and/or heater controllers 20 with any known network or direct connection, as known in the industry. As shown in the example of FIG. 1, the information providers 50 are interfaced to the server 40 through the Internet 10. When used, the information providers 50 deliver external data to the server 40 and/or heater controllers 20 related to hot water demand such as weather predictions, sunrise and sunset times, etc. The high-efficiency hot water heater uses this data, when available, to help predict future hot water demand. For example, if it will be cloudy, more hot water is likely to be needed because people taking showers feel colder on cloudy days.

The architecture shown in FIG. 1 is flexible, in that, the decision process is made either locally at the hot water heater controllers 20 (see FIGS. 3, 5 and 6) or centrally at a central computing system (server) 40 or jointly. Therefore, several high-efficiency hot water heater system architectures/configurations are anticipated. In some high-efficiency hot water heater systems, a controller 20 (see FIG. 3) is directly interfaced to the heating elements 120/122 (see FIGS. 5 and 6), obtaining data locally and/or remotely (e.g. from Information Providers 50) and making all decisions on when and how to heat water. In some high-efficiency hot water heater systems, the controller 20 or other discrete logic is interfaced to the heating elements 120/122 (see FIGS. 5 and 6) and is controlled by software running on a remote system (e.g. server 40). In this, the remote system (server 40) optionally receives data from the local controller 20 and/or remotely (e.g. from Information Providers 50) and makes all decisions on when and how to heat water, instructing the controller 20 to turn on/off the heating elements 120/122. In another type of high-efficiency hot water heater system, a combination of the prior architectures is used. In this, the controller 20 (see FIG. 3) or other discrete logic is directly interfaced to the heating elements 120/122 (see FIGS. 5 and 6) and makes part of the decisions as to when and how to heat the water, but the processor 70 is also in communication and cooperating with software running on a remote system (e.g. server 40). In this, the remote system (server 40) optionally receives data from the local controller 20 and/or remotely (e.g. from Information Providers 50) and jointly, with the controller 20, makes decisions on when and how to heat water.

Referring to FIG. 2, a schematic overview of the high-efficiency hot water heater decision process is shown. In this example, one or more inputs 32/33/34/35/36 are analyzed by analysis algorithms 30 to determine how to control the high-efficiency hot water heater heating elements 120/122 and or valves 130/132/134 (see FIGS. 5 and 6). Any or all of the inputs 32/33/34/35/36 are used to predict when different amounts of hot water will be needed. For example, ambient conditions 32 are used by the analysis algorithms 30 to predict hot water needs. For example, a person will adjust the water temperature of their shower dependent upon internal building ambient temperature and humidity. A higher internal building ambient temperature and humidity tends to make a person lower their shower temperature while a lower internal building ambient temperature and humidity tends to make a person increase their shower temperature. Likewise, external ambient conditions tend to further influence a person's shower temperature. For example, when it is cloudy out, a person tends to take warmer showers and when it is hot outside, after exercise outdoors, a person tends to take cooler showers. Ambient conditions 32 are measured by, for example, temperature sensors 100/104, humidity sensors 110, and light sensors 112 (see FIG. 3).

Schedule data 33 is also optionally considered by the analysis algorithms 30 to predict when demand will occur. For example, in a dormitory, by knowing the schedule of students, the analysis algorithms 30 control the heater(s) 120/122 and valves 130/132/134 based upon the schedule data 33 such that, knowing that lights out starts at 10:00 PM and classes start at 8:00 AM, the analysis algorithms 30 predict very low hot water usage after 10:00 PM when the students are asleep, high hot water usage prior to 8:00 AM when students are waking and taking showers, and low hot water usage when students are in class after 8:00 AM, etc. In another example, by knowing the schedule data 33 for people in a home, the analysis algorithms 30 make similar predictions. For example, in a two person household, knowing that both occupants leave work at 7:30 AM and return home at 6:00 PM on Monday, Tuesday and Friday, the analysis algorithms 30 predict low or no hot water usage starting at 7:30 AM and pre-heat the hot water in the storage tank 140 (see FIG. 5) to reach a certain temperature at 6:00 PM when the occupants arrive home (e.g. for dish washing, showers, etc.). In some examples, schedule data includes resource levels such as: there are 40 residents in the dormitory, twenty start classes at 8:00 AM and 40 residents start classes at 9:15 AM. In this example, the number of students is also used to determine hot water demand (e.g. if there were more than 40 students, more hot water is needed).

Current data 35 is also (optionally) considered by the analysis algorithms 30 to predict when demand will occur. Current data includes, for example, current flow rates, current hot water output temperature, and current system input water temperature. The analysis algorithms 30 use the current data 35 to determine how well predictions match actual and if any boosting or setback is needed based upon this current data 35. For example, if the analysis algorithms 30 predict an overall demand of 600 gallons of hot water per hour (10 gallons per minute) and the current data 35 indicates that in the last 5 minutes, 30 gallons per minute have been consumed, then the analysis algorithms will control the water heating elements 120/122 to energize and heat the water temperature to a higher temperature than was predicted to be required. Likewise, if for some reason the temperature of the input water is a few degrees less than it has been; more heat needs to be added to the system to account for colder water being mixed with the heated water in the storage tank 140 (see FIGS. 5 and 6).

Another optional source of data to the analysis algorithms 30 is external data 36. External data includes any data feed that has information regarding the future demand for hot water. This data includes, but is not limited to, weather predictions, data from an almanac (e.g. sunrise and sunset times), local news information, school information, lunch menus, school events, local events, etc. For example, if the dormitory dinner menu includes cold sandwiches, there is likely to be a higher demand for hot water that evening then if the menu includes hot soup. If the forecast for tomorrow is rain and sleet, depending on the users, such weather will change demand. For example, some hot water users will forego showering/bathing until they return from classes so as to not be as cold walking to classes. Other events will affect hot water consumption. For example, if Friday night is Prom Night, then extra hot water will be needed between, say, 5:00 PM and 7:00 PM for prom goers to shower/bathe.

Although similar to schedule data 33, historic data 34 differs, in that demand measurements from the past are used to predict future demand by the analysis algorithms 30. For example, after installation of the high-efficiency hot water heater system, the analysis algorithms 30 initially relies on ambient data 32, schedule data 33, external data 36 and current data 35 to predict hot water demand and control the hot water heaters 60. After a number of days of controlling the high-efficiency hot water heater based upon these data, the analysis algorithms now have access to historical data 34. For example, one set of 40 students may have hot water usage patterns that differ from another set of the same number of students, perhaps due to differences in gender and other backgrounds. Initially, the analysis algorithms 30 make an assumption of the hot water needs per person, for example, making an average consumption prediction or a worst-case consumption prediction. As time goes on and usage patterns start repeating, the analysis algorithms 30 consult the historic data 34 to determine how the operation of the high-efficiency hot water heater needs to be adjusted based upon the historic data 34. For example, the first Monday after installation, the high-efficiency hot water heating system preheats the water in storage to a certain temperature based upon 40 unknown people. By measuring the flow rates, input water temperature and output water temperature, the analysis algorithms determine that the water in the storage tank 140 does not need to be heated as much as it was, so the next Monday, the water in the storage tank 140 is heated a percentage less (e.g. 10% less) and the flow rates, input water temperature and output water temperature are again measured to determine how well the high-efficiency hot water heating system is meeting the demands of users.

Although not required, it is anticipated that the analysis algorithms 30 use neural networks 430 (see FIG. 10) or some other form of artificial intelligence to analyze the data 32/33/34/35/36 and make appropriate decisions on when and how much to pre-heat the water based upon predictions of such analysis algorithms. A neural network 430 accepts data (as described above) and determines actions (as described above), with the added benefit that the neural network 430 learns. The neural networks 430 find patterns in the data as well as filter the data. For example, over time, the neural network 430 will find that every Friday morning between 6:00 and 6:15, there is a high demand for hot water and, therefore, will make assumptions on how the water heater needs to be controlled based upon the predicted demand. The neural network 430 will filter its data to ignore irregular data. For example, when users are on vacation and during one week, there is no demand for hot water on that day.

In the neural network 430 implementation, each input is considered with a weighing factor. For example, last week's usage history has a high weighing factor, the week before usage history has a lower weighing factor, and the external weather (e.g. cloudy, raining) has even a lower weighing factor. As the neural network system 430 continues to predict hot water demand, hot water usage (e.g. flow rates) is measured and fed back into the neural network 430 and the neural network 430 makes adjustments. For example, if, over time, the neural network 430 recognizes that hot water demand is 10% higher on cloudy days, the neural network 430 will increase the weight given to external weather.

Being that neural networks 430 are well known, it is anticipated that for some high-efficiency hot water heaters, the basic neural network 430 software is provided as a package from a provider of such and is programmed based upon the range of inputs available to the high-efficiency hot water heater (e.g., schedule, ambient/weather, history, current data, external data, etc.) to control the available heating elements 120/122 (see FIGS. 5 and 6) and/or valves 130/132/134 (see FIGS. 6 and 7). For some high-efficiency hot water heater systems, the neural network software is not an off-the-shelf pre-programmed package.

In some high-efficiency hot water heaters, instead of using true neural networks, heuristic algorithms or static logic is used in the prediction algorithms 30. A simple example in a dormitory in which all students leave for breakfast and class at the same time and there are n students registered for that dormitory, an exemplary heuristic algorithm is: if n is less than 30, preheat the water to t1 at time T1; if n is greater than 30 and less than 60, preheat the water to t2 at time T2; and if n is greater than 60, preheat the water to t3 at time T3. In this, the schedule data 33 is used to determine when the students will be using the hot water (e.g. before class/breakfast) and how many students are present. The more students present, the hotter the water in the storage tank needs to be, therefore, heating starts earlier and ends when the water reaches this higher temperature. This is but an example and a complete heuristic algorithm will consider other data in the algorithm's decision tree.

Referring to FIG. 3, a schematic view of a typical computer system is shown. The example computer system represents a typical computer system used as the individual heater control devices 20, though a similar computer system is anticipated for the server 40. The exemplary computer system 20 is shown in its simplest form, having a single processor 70. Many different computer architectures are known that accomplish similar results in a similar fashion and the present invention is not limited in any way to any particular computer system. The present invention works well utilizing a single processor system, as shown in FIG. 3, a multiple processor system where multiple processors share resources such as memory and storage, a multiple server system where several independent servers operate in parallel (perhaps having shared access to the data or any combination). In any of these systems, a processor 70 executes or runs stored programs that are generally stored for execution within a memory 74. The processor 70 is any processor or a group of processors, for example an Intel Pentium-4® CPU, 80C51, or the like. The memory 74 is connected to the processor by a memory bus 72 and is any memory 74 suitable for connection with the selected processor 210, such as SRAM, DRAM, SDRAM, RDRAM, DDR, DDR-2, etc. It is also anticipated that the processor 70, bus 72 and memory 74 are integrated into a single component.

Also connected to the processor 70 is a system bus 82 for connecting to peripheral subsystems such as a network interface 80, persistent storage (e.g. a hard disk, flash memory) 88, removable storage (e.g. DVD, CD, flash drive) 90, a graphics adapter 84 and a keyboard/mouse 92. The graphics adapter 84 receives commands and display information from the system bus 82 and generates a display image that is displayed on the display 86 (e.g. monitor, LEDs, graphic display, etc.).

Various input devices, sensors, and control drivers 100/104/110/112/114/116/118 are optionally connected to the bus. The following inputs are representative of inputs for the high-efficiency hot water system, though more or less inputs are anticipated: one or more outside ambient temperature sensors (t_a) 100, one or more indoor building temperature sensors (t_b) 104, one or more relative humidity sensors 110, one or more outdoor ambient light sensors (e.g. cloud cover) 112, cold water supply temperature sensor (t_i) 115, hot water output temperature sensor (t_o) 117, and a water flow sensor 114.

The exemplary control outputs include one or more heater controls 118 and one or more valve controls 116. The heater controls 118 energize one or more water heating elements 120/122 (see FIGS. 5 and 6). The valve controls 116 energize (open or close or partially open) one or more water control valves 130/132/134 (see FIGS. 5 and 6). Although shown as discrete inputs 100/104/110/112/114/115/117 and discrete outputs 116/118, it is anticipated that in some systems, the sensors and controls 100/104/110/112/114/115/117/116/118 are signaled through a bus, wired and/or wireless, such as Inter-Integrated Circuit (I²C), car-area network bus (CAN), vehicle area network bus (VAN), Ethernet, Bluetooth, IEEE 488, USB, FireWire (1394), RS232 bus, Wi-Fi, etc.

In general, the persistent storage 88 is used to store programs, executable code and data such as user financial data in a persistent manner. The removable storage 90 is used to load/store programs, executable code, images and data onto the persistent storage 88. These peripherals are just examples of input/output devices 80/84/92, persistent storage 88 and removable storage 90. Other examples of persistent storage include core memory, FRAM, flash memory, etc. Other examples of removable media storage include CDRW, DVD, DVD writeable, Blu-ray, compact flash, other removable flash media, floppy disk, ZIP®, etc. In some embodiments, other devices are connected to the system through the system bus 82 or with other input-output connections/arrangements as known in the industry. Examples of these devices include printers; graphics tablets; joysticks; and communications adapters such as modems and Ethernet adapters. Any configuration of input/output devices is anticipated and the high-efficiency hot water heater system is not limited to any particular architecture and/or configuration.

In some high-efficiency hot water heater systems that communicate with a central server 40 and/or external information providers 50, a network interface 80 connects the processor 70 to the network 10 through a link 78 which is any known network media such as a cable broadband connection, a Digital Subscriber Loop (DSL) broadband connection, a T1 line, a T3 line, or a wireless link such as Wi-Fi, or a cellular data connection.

Referring to FIG. 4A, a schematic view of a typical storage-tank hot water heater of the prior art is shown. In this example of the prior art, cold water enters the tank 1 and is heated by a heating element 2 that is controlled by a thermostat to attempt to maintain the temperature of the water in the tank 1 to a certain temperature (e.g. 125° F.). This example of the prior art always tries to maintain that certain temperature, even during the evening or when the covered building is vacant and there is no demand for hot water. Because it is constantly maintaining that certain temperature, as heat is lost through the insulation that typically surrounds the tank 1 and through the supply and output connections, energy is consumed by the heating element 2, even though there is no actual use of hot water.

Referring to FIG. 4B, a schematic view of a typical on-demand hot water heater of the prior art is shown. In this example of the prior art, cold water is heated instantaneously by one or more heating elements 3 that is/are controlled by a flow sensor such that, as soon as demand for hot water occurs (e.g. a tap is opened), the heating elements 3 are energized to heat water from the cold water supply to a desired output temperature. On-demand hot water heaters of the prior art are typically more efficient that storage-tank systems as shown in FIG. 4B, but due to the maximum capabilities of heating elements 3 in raising the temperature of the cold water supply, when multiple demands are placed on these on-demand hot water heaters, they often do not provide hot water that is sufficiently hot. On-demand hot water heaters will deliver cooler hot water when several people are filling bathtubs, showering, doing laundry and/or washing dishes. Additionally, because there is no hot water storage, on-demand hot water heaters will not supply hot water during a power failure.

Referring to FIG. 5, a simplified schematic view of the high-efficiency hot water heater is shown. The exemplary high-efficiency hot water heater has a hot water storage tank 140 with one or more sources of heat such as heating element(s) 122 (or main heater) and one or more on-demand heaters 120. Any sources of heat are anticipated for the heating elements 122 associated with the tank 140 and for the on-demand heaters 120, including, but not limited to, electric heating elements, gas flames, oil flames, hot fluids from a boiler, hot fluids from solar panels, etc.

Valves 130/132/134 control the flow of water such that the output (HW) is supplied either directly from the hot water tank (tank input valve 130 is open, tank output valve 132 is open and tank bypass valve 134 is closed) or directly from the on-demand heaters 120 (tank input valve 130 is closed, tank output valve 132 is closed and tank bypass valve 134 is open) or a combination of both through partial operation of the valves 130/132/134.

Note that, in some embodiments, there are no internal heating elements 122 and all water heating is performed, for example, by the one or more on-demand heaters 120 or provided by other heat sources such as boilers and solar collectors.

In this example, cold water from the supply (CW) is heated by the on-demand heating elements 120 to feed the tank 140 while the temperature within the tank 140 is maintained by an internal heating element 122. The valves 130/132/134 and heating elements 120/122 are controlled by the controller based upon the analysis algorithms 30. For example, in a dormitory situation, at 1:00 AM (low demand period), the internal heating element 122 maintains a lower water temperature in the tank 140 and, should demand for hot water occur, one or more of the on-demand heating elements 120 is energized and hot water is routed directly from the on-demand heating elements 120 (tank input valve 130 is closed, tank output valve 132 is closed and tank bypass valve 134 is open). At a time later in the morning, based upon a prediction of the analysis algorithms 30, the internal heating elements 122 are energized to bring the water temperature in the storage tank 140 to a higher temperature to meet a high demand as students start to wake.

Note that in some embodiments, there are no on-demand heating elements 120 and, therefore, no need for valves 130/132/134. In such a minimal system, the controller 20 predictively controls operation of the internal heating element(s) 122, but such a system lacks on-demand heating elements 120, during periods where demand is not expected demand results in lower temperature hot water.

Referring to FIG. 6, a second schematic view of the high-efficiency hot water heater is shown. In this example, the controlled devices 120/122/130/132/134 and sensors/inputs 100S/104S/110S/112S/114S/115S/117S are connected to the controller 20 through a bus 125 to simplify wiring. Alternatively, one or more of the controlled devices 120/122/130/132/134 and/or sensors/inputs 100S/104S/110S/112S/114S/115S/117S are directly connected to the outputs 116/118 and inputs 100/104/110/112/114/115/117 of the controller 20 (e.g. temperature sensor 100S is directly connected to input 100). In this example, the controller 20 is also connected to the network 10 (e.g. Internet) for communication with the server 40 and/or one or more information providers 50.

This exemplary high-efficiency hot water heater has a hot water storage tank 140 with internal heating element(s) 122 (or main heater) and one or more on-demand heaters 120. Valves 130/132/134 control the flow of water such that the output (HW) is supplied either directly from the hot water tank (tank input valve 130 is open, tank output valve 132 is open and tank bypass valve 134 is closed) or directly from the on-demand heaters 120 (tank input valve 130 is closed, tank output valve 132 is closed and tank bypass valve 134 is open) or a combination of both through partial operation of the valves 130/132/134. Operation of the heating elements 120/122 and valves 130/132/134 is controlled by the processor 70 of the controller 20 sending and receiving signals over the bus 125.

In this example, cold water from the supply (CW) is heated by the on-demand heating elements 120 to feed the tank 140 while the temperature within the tank 140 is maintained or increased by heating element(s) 122 associated with the storage tank 140 (e.g. internal heating element(s) 122). The valves 130/132/134 and heating elements 120/122 are controlled by the controller 20 based upon the analysis algorithms 30 as previously described.

Note that in some embodiments, there are no on-demand heating elements 120 and, therefore, no need for valves 130/132/134. In such a minimal system, the controller 20 predictively controls operation of the heating elements 122 that provide heat to water within the storage tank 140, but such a system without on-demand heating elements 120 will not provide properly heated water during periods where demand is not expected because the water temperature in the storage tank 140 is typically allowed to decrease during such periods.

Referring to FIG. 7, a flow chart of a storage-tank hot water heater of the prior art is shown. In the prior art, the typical water heater (e.g. 50 gallon home water heaters) has simple, thermostatically controlled heating elements 2 within the storage tank 1 (see FIG. 4A). In such heaters, the heating element is initially de-energized 200, then the following steps repeated (though this is typically a hardware implementation rather than a software implementation). The water temperature within the storage tank 1 is measured 202 and compared to a low-threshold 204. If the water temperature within the storage tank 1 is below the low-threshold 204, the heating element is energized 206. The water temperature within the storage tank 1 is then compared to a high-threshold 208 and, if the water temperature within the storage tank 1 is higher than the high-threshold 208, the heating element is disabled 210. In this exemplary system of the prior art, the heating elements continue to maintain the water temperature within the storage tank 1 at a temperature between the low-threshold and the high-threshold, even when the occupants of the building being served are not present (e.g. at work, on vacation) or have a low probability of using hot water (e.g. asleep).

Referring to FIG. 8, a flow chart of an on-demand hot water heater of the prior art is shown. In the prior art, the typical on-demand water heater has a simple, flow-detection controlled heating element(s) 3 (see FIG. 4B). In such heaters, the heating element is initially de-energized 220, then the following steps repeated (though this is typically a hardware implementation rather than a software implementation). The flow of water is measured 222 and compared to a threshold 224. If the flow of water is above the threshold 224, the heating element is energized 226. If the flow of water is below the threshold 224, the heating element is de-energized 228. In this exemplary system of the prior art, the heating elements 3 must have sufficient power output to heat the water as it flows around the heating elements 3. The amount of heat needed to raise the temperature from ground water temperature to the desired hot water temperature is directly proportional to the flow rate and, therefore, if the flow rate exceeds the design limits of the heating element(s) 3, all users will experience water that is not as hot as desired. Furthermore, because there is no storage tank 1 (as in FIG. 4A), if there is a power failure, this water heater of the prior art will produce no hot water. This is especially bad if there is a power failure in the morning before occupants of the affected building leave for work, assuming that their alarm clocks wake them during the power failure.

Referring to FIG. 9, a flow chart of an exemplary high-efficiency hot water heater is shown. This flow chart is greatly simplified to provide a basic understanding of the operation of the high-efficiency hot water heater. This example uses heating elements (e.g. electric heating elements) as the sources of heat for simplicity purposes. The heating elements 120/122 are initially de-energized 300 then the following steps repeated: (a) The flow rate is measured 302, then (b) the flow rate is compared to a first threshold 304 and if greater than the first threshold THR1304 (e.g. there is at least some flow of water indicating demand for heated water), the flow rate is compared 306 to THR2, a second, higher flow rate (i.e. is there low demand or high demand?). If the flow rate is higher than the THR2 (high demand), then the on-demand heating element 120 is energized (H=1) and all valves are open (V1=1, V2=1, V3=1) 308, utilizing any already heated water from the storage tank 140 to provide the greatest amount of hot water as possible. If the flow rate is lower than the THR2 (low demand), then the on-demand heating elements 120 are energized (H=1), the valve between the on-demand heating elements 120 is opened (V3=1), and the valves leading to and from the storage tank 140 are closed (V1=0, V2=0) 310 providing sufficient water heating for the intermediate demand because the on-demand heating elements 120 are sized to supply the intermediate demand.

Next, the history file is consulted 322. The history file contains, for example, historical usage patterns sorted by meaningful calendar periods (e.g. days of the week). For example, an exemplary history file for a dormitory might show the following:

Day
Hour
Usage

Monday
Midnight-6:00 AM
Low

Monday
6:00 AM-6:45 AM
Moderate

Monday
6:45 AM-8:00 AM
High

Monday
8:00 AM-5:45 PM
Low

Monday
5:45 PM-11:59 PM
Moderate

The above example only shows one day of the exemplary history file for brevity purposes. As hot water is used, the high-efficiency hot water heater updates the history file, with or without data smoothing to ignore unusually high or low demands. In this example, there is little or no need to heat the water in the storage tank 140 between the hours of midnight and 6:00 AM, being that there is low demand and the on-demand heating elements 120 are capable of supplying any predicted demand during that period. In such cases, the need predicted test 324 results in not needed and the storage tank heater is set to off (I−H=0) 326. At 6:00, it is predicted that moderate usage will start and last for 45 minutes. Again, the on-demand heating elements 120 are capable of supplying any predicted demand during that period, but the analysis algorithms 30 recognize that starting at 6:45 AM, the demand will be high (e.g. greater than THR2), so high that the on-demand heating elements 120 will not be capable of supplying the predicted demand from 6:45 AM to 8:00 AM. Therefore, the need predicted test 324 determines a high need (flow>THR2) and the storage tank heater is energized (I−H=1) 328 to preheat the water in the storage tank 140 to meet the expected high demand period that starts at 6:45 AM. At around 7:40 AM, the need predicted test 324 determines that sufficient hot water is already available within the storage tank 140, enough to supplement the on-demand heating elements 120. The storage tank heating element(s) 122 is again de-energized 328 and the demand for the next 20 minutes or so (until 8:00 AM) is met by the hot water already in the storage tank 140 and the on-demand heating elements 122. Because the remainder of the day, only low or moderate usage is predicted, there is no need to energize the storage tank heating elements 122 being that the on-demand heating elements are capable of supplying all predicted demand for that period.

The above example is for illustrative purposes and it is known that a system would generally be much more complicated and have more or less data.

Referring to FIG. 10, a schematic diagram of a neural-network-based high-efficiency hot water heater is shown. The data available as inputs (ambient 32, schedule data 33, external data 36, current data 35 and history 34) are examples of input data for the neural network 430 and it is anticipated that high-efficiency hot water systems will have more, less, and/or different inputs. In this example, one or more inputs 32/33/34/35/36 are analyzed by a neural network 430, controlling the heating elements 120/122 (sources of heat). Any or all of the inputs 32/33/34/35/36 are used by the neural network 430 to predict when different amounts of hot water will be needed. For example, ambient conditions 3 (e.g. weather) are used by the neural network 430 to predict hot water needs based upon internal weighing factors and other inputs. For example, if previously, users adjusted the water temperature of their shower dependent upon outside temperature and humidity, the neural network will “learn” and add intelligence that uses the ambient data 32 to predict future hot water demands. In another example, internal ambient conditions tend to influence a person's shower temperature. For example, when there is a low humidity in the building, a person tends to take warmer showers. The neural network 430 monitors such inputs and makes inferences based upon previous ambient data 32 to predict future hot water demands.

Schedule data 33 is also considered by the neural network 430 to predict when demand will occur. For example, in a dormitory, by knowing weigh values for various aspects of the schedule of students along with the other data, the neural network 430 controls the on-demand heating elements 120 and the heating elements 122 associated with the storage tank 140 based upon the data 32/33/34/35/36. With this data, the neural network 430 will learn that lights out starts at 10:00 PM and classes start at 8:00 AM and, therefore, the neural network 430 will learn to predict very low hot water demand after 10:00 PM when the students are asleep, high hot water usage prior to 8:00 AM when students are waking and taking showers, and low hot water usage when students are in class after 8:00 AM, etc.

Current data 35 is also (optionally) considered by the neural network 430 to predict when demand will occur. Current data includes, for example, current flow rates, current hot water output temperature, and current system input water temperature, etc. The neural network 430 also uses the current data 35 to determine how well predictions match actual usage and if any boosting or setback is needed based upon the current data 35. For example, if low usage is predicted for the upcoming timeframe but the current data 35 shows that high usage is occurring, then the neural network 430 reacts to such by energizing heating elements 120/122 and controlling valves 130/132/134 to provide maximum hot water output.

Another optional source of data to the analysis algorithms 30 is external data 36. External data includes any data feed that has the potential to effect hot water usage. This data includes, but is not limited to, weather predictions, data from an almanac (e.g. sunrise and sunset times), local news information, school information, lunch menus, school events, local events, etc. When such external data 36 is available, the neural network 430 also considers such in making predictions.

Although similar to schedule data 33, historic data 34 differs, in that, demand measurements from the past are used by the neural network 430 to predict future demand. For example, after installation of the high-efficiency hot water heater system, the neural network 430 is taught to predict demand based upon on ambient data 32, schedule data 33, external data 36 and current data 35, saving historical data and data related to its own performance in the history data 34. After a number of days of controlling the heating elements 120/122 and valves 130/132/134 based upon these data 32/33/35/36, the neural network 430 now has access to historical data 34. For example, one set of 40 students may have different hot water usage patterns that differ from another set of the same number of students, perhaps due to differences in gender and other backgrounds. Initially, the neural network 430 is taught to make an assumption of the hot water needs per person, for example, making an average consumption prediction or a worst-case consumption prediction. As time goes on and history data 34 is recorded, usage patterns start repeating, and the neural network 430 consults the historic data 34 to learn how the settings of the heating elements 120/122 and valves 130/132/134 need be adjusted based upon the historic data 34. For example, the first Monday after installation, the neural network 430 has no historic data 34 and controls the heating elements 120/122 and valves 130/132/134 to preheat the water in storage to a certain temperature based upon 40 unknown people. By measuring the flow rates, input water temperature and output water temperature, the neural network 430 determines that the water in the storage tank 140 does not need to be heated as much as it was, so the next Monday, the water in the storage tank 140 is heated 10% less and the flow rates, input water temperature and output water temperature are again measured to determine how well the high-efficiency hot water heating system is meeting the demands of users.

The neural network 430 accepts data (as described above) and determines actions (as described above), with the added benefit that the neural network learns. The neural networks find patterns in the data as well as filter the data. For a home installation example, the neural network 430 is initially taught that morning hot water demand starts at 7:00 AM and ends at 8:00 AM. Over time, the neural network 430 finds that every Friday morning between 6:00 and 6:15, there is a high demand for hot water and, therefore, will make adjustments (e.g. learns) to better satisfy that early demand for hot water. The neural network 430 also filters its data to ignore irregular data. For example, when users in the prior example are on vacation and there is no demand for hot water on that day, the neural network 430 filters out the data from the vacation period.

In some high-efficiency hot water heater systems, the basic neural network software 430 is provided as a package from a provider of such and is programmed based upon the range of inputs available to the high-efficiency hot water heater (e.g., schedule, ambient/weather, history, current data, external data, etc.) to control the available heating elements 120/122 (see FIGS. 5 and 6) and/or valves 130/132/134. For some high-efficiency hot water heater, the neural network software 430 is programmed from scratch.

In some high-efficiency hot water heaters, instead of using true neural networks 430, heuristic algorithms or static logic is used in the prediction algorithms 30. A simple example in a dormitory in which all students leave for breakfast and class at the same time and there are n students registered for that dormitory, an exemplary heuristic algorithm is: if n is less than 30, preheat the water to t1 at time T1; if n is greater than 30 and less than 60, preheat the water to t2 at time T2; and if n is greater than 60, preheat the water to t3 at time T3. In this, the schedule data 33 is used to determine when the students will be using the hot water (e.g. before class/breakfast) and how many students are present. The more students present, the hotter the water in the storage tank needs to be, therefore, heating starts earlier and ends when the water reaches this higher temperature.

Equivalent elements can be substituted for the ones set forth above such that they perform in substantially the same manner in substantially the same way for achieving substantially the same result.

It is believed that the system and method as described and many of its attendant advantages will be understood by the foregoing description. It is also believed that it will be apparent that various changes may be made in the form, construction and arrangement of the components thereof without departing from the scope and spirit of the invention or without sacrificing all of its material advantages. The form herein before described being merely exemplary and explanatory embodiment thereof. It is the intention of the following claims to encompass and include such changes.

HIGH EFFICIENCY WATER HEATER

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CPC

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