The present disclosure relates to controllers automatically controlling devices. More specifically, to controllers using a self-healing controller network to control devices.
Controlling buildings is a huge problem because the amount of computing power required to determine the best device usage is so large. In the past, buildings react to actions that have already happened—for example, a thermometer drops to a low level, which turns on a furnace. To optimize a building, one must look into the future, not just react to the past. However, buildings are complex systems with hundreds of sensors, and hundreds of devices that can be controlled, which leads to billions or even trillions of possible states that must be accounted for. Trying to run such complex programs in a cloud computing environment, even if it can be done, leads to serious bandwidth and latency issues.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description section. This summary does not identify required or essential features of the claimed subject matter.
In general, one innovative embodiment comprises using distributed building automation controllers to compute building operating behavior within a defined space. The defined space has a device, a sensor, and an operating characteristic. The operating characteristic may a single state, such as temperature, or may be a combination of states, such as what temperature a human would perceive—a comfort state. The device has a device characteristic, which is the state its is in (on/off/intermediate), a state that it can be in, etc. The sensor has data associated with it. The defined space has at least two controllers in it, one of which can control the device and accept data from the sensor. The first controller and the second controller are operationally able to run the computer program. The computer program can determine desired device behavior based on the desired defined space operating characteristic, the sensor data, and the device characteristic.
In an innovative embodiment, distributed defined space controllers compute defined space operating behavior. The distributed defined space controllers each have at least one processor coupled with memory-stored instructions, one of the distributed defined space controllers is designated as a master controller. The defined space also has a sensor and a device, the device coupled to one of the controllers. The sensor senses a sensor state value then passes that value to the master controller. The master controller determines which device in the defined space will modify the state of defined space to meet a desired state in the defined space. To do so, the master controller determines which device must be modified to modify the state. It then determines which controller directly controls the device, and passes the information on to the controller. The controller can then send instructions to the device so that it modifies state of the defined space.
The foregoing and other embodiments can each optionally include one or more of the following features, alone or in combination. Additional features and advantages will become apparent from the following detailed description of illustrated embodiments, which proceeds with reference to accompanying drawings.
Non-limiting and non-exhaustive embodiments of the present embodiments are described with reference to the following FIGURES, wherein like reference numerals refer to like parts throughout the various views unless otherwise specified.
Corresponding reference characters indicate corresponding components throughout the several views of the drawings. Skilled artisans will appreciate that elements in the FIGURES are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments. Also, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments.
Disclosed below are representative embodiments of methods, computer-readable media, and systems having particular applicability to controllers using a self-chosen master controller and other controllers. Described embodiments implement one or more of the described technologies. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present embodiments. It will be apparent, however, to one having ordinary skill in the art that the specific detail need not be employed to practice the present embodiments. In other instances, well-known materials or methods have not been described in detail in order to avoid obscuring the present embodiments. “one embodiment”, “an embodiment”, “one example” or “an example” means that a particular feature, structure or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present embodiments. Thus, appearances of the phrases “in one embodiment”, “in an embodiment”, “one example” or “an example” in various places throughout this specification are not necessarily all referring to the same embodiment or example. Modifications, additions, or omissions may be made to the systems, apparatuses, and methods described herein without departing from the scope of the disclosure. For example, the components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses disclosed herein may be performed by more, fewer, or other components and the methods described may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order.
In addition, it is appreciated that the figures provided herewith are for explanation purposes to persons ordinarily skilled in the art and that the drawings are not necessarily drawn to scale. To aid the Patent Office and any readers of any patent issued on this application in interpreting the claims appended hereto, applicants wish to note that they do not intend any of the appended claims or claim elements to invoke 35 U.S.C. 112(f) unless the words “means for” or “step for” are explicitly used in the particular claim.
Embodiments in accordance with the present embodiments may be implemented as an apparatus, method, or computer program product. Accordingly, the present embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.), or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “module” or “system.” Furthermore, the present embodiments may take the form of a computer program product embodied in any tangible medium of expression having computer-usable program code embodied in the medium.
Any combination of one or more computer-usable or computer-readable media may be utilized. For example, a computer-readable medium may include one or more of a portable computer diskette, a hard disk, a random access memory (RAM) device, a read-only memory (ROM) device, an erasable programmable read-only memory (EPROM or Flash memory) device, a portable compact disc read-only memory (CDROM), an optical storage device, and a magnetic storage device. Computer program code for carrying out operations of the present embodiments may be written in any combination of one or more programming languages.
The flowchart and block diagrams in the flow diagrams illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present embodiments. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It will also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, may be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, article, or apparatus.
Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present). “Program” is used broadly herein, to include applications, kernels, drivers, interrupt handlers, firmware, state machines, libraries, and other code written by programmers (who are also referred to as developers) and/or automatically generated. “Optimize” means to improve, not necessarily to perfect. For example, it may be possible to make further improvements in a program or an algorithm which has been optimized.
“Automatically” means by use of automation (e.g., general purpose computing hardware configured by software for specific operations and technical effects discussed herein), as opposed to without automation. In particular, steps performed “automatically” are not performed by hand on paper or in a person's mind, although they may be initiated by a human person or guided interactively by a human person. Automatic steps are performed with a machine in order to obtain one or more technical effects that would not be realized without the technical interactions thus provided.
Additionally, any examples or illustrations given herein are not to be regarded in any way as restrictions on, limits to, or express definitions of any term or terms with which they are utilized. Instead, these examples or illustrations are to be regarded as being described with respect to one particular embodiment and as being illustrative only. Those of ordinary skill in the art will appreciate that any term or terms with which these examples or illustrations are utilized will encompass other embodiments which may or may not be given therewith or elsewhere in the specification and all such embodiments are intended to be included within the scope of that term or terms. Language designating such nonlimiting examples and illustrations includes, but is not limited to: “for example,” “for instance,” “e.g.,” and “in one embodiment.”
Various alternatives to the implementations described herein are possible. For example, embodiments described with reference to flowchart diagrams can be altered, such as, for example, by changing the ordering of stages shown in the flowcharts, or by repeating or omitting certain stages.
In some implementations, a building's devices are run by a series of connected computers which are also controllers. The connected computers are controllers because some of the controllers have devices used to modify state of the building wired to them. For example, a controller may have furnaces, air conditioners, and other HVAC equipment attached to it that it controls. A controller may have security equipment, entertainment equipment, irrigation equipment, etc., which it controls by sending messages to the equipment through a hard-wired connection or a wireless connection, or both. The computer/controllers can be thought of as a compute swarm of control nodes because they behave as a distributed system to run the programs necessary to determine how to better run the devices. The computer/controllers may run programs as a distributed system that optimizes the devices for energy savings, equipment wear, rapid modification of building state, or some other optimization factor. The controllers have mechanisms of locating one another, as they are connected through a network.
In some embodiments, a sensor provides input data, that will trigger an action by a device. For example, the temperature may rise in a building zone, so an air conditioner should be turned on. In some embodiments, the temperature sensor tells a controller that the temperature at a specific zone is X. The controller can then pass this information on to a master controller which determines that a temperature of X requires device Y to be turned on. The master controller then sends a signal to the controller that is connected to device Y. The controller then turns on device Y. A given problem may only need a subset of the connected computer/controllers. Specifically, in some embodiments, the sensor and the device will be connected to the same controller, so that the controller can bypass the master controller to turn on device Y. In such a system the master computer find the triplet that is required—the sensor, the device that is to be controlled, and the controller that the device is connected to. This might entail a mechanism of self-federating around three pieces of equipment: sensor; device to be controlled, and the controller that can control the device.
The intelligence in the system is diffuse, such that there are multiple controllers that can be the master controller. The controllers can select their own master controller. If a master controller has problems (such as network problems), the remaining controllers can choose a new master controller.
With reference to
A sensor 120 with sensor data 125 is also in the defined space. The sensor 120 may be any type of sensor, such as a thermostat, a humidity sensor, a VOC sensor, a noise sensor, an occupancy sensor, or a sensor that has more than one function, such as a combined thermostat and humidity sensor. The sensor data 125 may be the current sensor value, may be a historical set of values, or may be a different sort of data, or timing of data that the sensor keeps.
The defined space 105 also has a defined space operating characteristic 130. This is a characteristic that the defined space is expected to have. For example, if the characteristic is a temperature characteristic, the characteristic may be a temperature that the defined space is expected to be at. If the temperature characteristic is 70 degrees, then the defined space operating characteristic is “keep the building at 70 degrees.” In some embodiments, this characteristic may be a time curve of the characteristic. For example, the building may be expected to be at 60 degrees between the hours of 6 pm and 7 am, and at 70 degrees between 7 am and 6 pm, in which case the defined space operating characteristic is a 24 hour time curve with the temperature 70 degrees between 7 am and 6 pm, and the temperature 60 degrees between the hours 6 pm and 7 am.
In some embodiments, this characteristic may be a humidity characteristic, a radiant heat characteristic, desired entertainment, a sound characteristic (how loud the sound is, what type of sound is being transmitted, etc., grounds feature characteristic (whether or not sprinklers are on, etc), or security characteristic (which parts of the building are locked, which parts should not register having any occupants, etc.). Some of these characteristics may require modifying more than one state, such as temperature and humidity; some of these characteristics may require knowledge of more than one state to determine what state should be modified most efficiently. For instance, heat is perceived by humans as a complex interaction between radiant heat, temperature, humidity and breeze, at least. All of these may be taken into account to determine an operating space characteristic.
In some cases, the defined space operating characteristic may be a comfort characteristic that is a combination of multiple characteristics, which estimates individual occupant comfort based on, e.g., physics principles; and/or may be a characteristic that manages comfort for a group of occupants for the best group satisfaction. In some embodiments, this comprises a human comfort model represented by a mathematical equation based on environmental variables, occupant characteristics and/or occupant preferences. In some embodiments, one or more occupant profiles may be used to calculate the comfort characteristic. The occupant profile comprises information specific to the occupant, such as body weight, gender, age, clothing insulation value, metabolic rate, body mass-index, schedule, preferred temperature, etc. Current environment variables, such as, e.g., temperature, humidity, etc., may also be included. A mathematical equation of human comfort may be used to determine the comfort characteristic. Any human comfort model as known by those of skill in the art may be used to determine the comfort characteristic, such as the Adaptive Comfort Model, Standard Effective Temperature Model, etc. An occupant comfort mean function may be used to calculate the comfort characteristic based on current occupants in the defined space. A combination of any of theses discussed methods may be used to determine the comfort characteristic.
The system further comprises a first controller 135 with computing hardware 140 and programmable memory 145; and a second controller 150 with computing hardware 155 and programmable memory 160. The memory 145, 160 can be any appropriate volatile or non-volatile storage subsystem. For example, the external memory can be volatile memory, e.g., static memory cells, as in FPGAs and some CPLDs; or non-volatile memory, e.g., FLASH memory, as in some CPLDs, or in any other appropriate type of memory cell.
The programmable memory 145, 160 may comprise a computer program 165, 170, with which calculations, such as the defined space operating characteristic 130 may be calculated. The first controller and the second controller are operationally able to run the computer program, either separately, or by the computer program being distributed between the first controller and the second controller. The computer program itself is operationally able to use thermodynamic characteristics of the defined space to determine desired device behavior based on the desired defined space operating characteristic, the sensor data, and the device characteristic.
The controllers 135, 150 also have networks 175, 180 that allow them to communicate with each other and with devices 110 and sensors 120. This network may be a hardwired network, e.g., ethernet, a wireless network, or a combination of both. Many devices may be hardwired to the controller, and communicate with the controller through their hard-wired connection.
As an illustrative example, and with reference to
In an embodiment, thermodynamic characteristics of the defined space are used to determine desired device behavior. Such thermodynamic characteristics may comprise a physical equation that uses characteristics of the air in the defined space, such as the resistance and conductivity of the air, thermodynamic characteristics of building material in the defined space, a neural network that defines the defined space using thermodynamic characteristics, etc.
With reference to
In some embodiments, when there are three controllers: a master controller 305 (which might be a first controller), a second controller 315, and a satellite controller 320, the master controller can divide the program into three chunks operationally able to determine to run the first chunk 310 on the second controller 315, the second chunk on the satellite controller 320, and a third chunk on itself 305. In some embodiments, the satellite controllers are not used to run programs. In such a case, the master controller will only divide the program into chunks to run on, e.g., the master 305, and the second controller 315. The controllers (e.g., for this example a master controller 305, a second controller 315, and a satellite controller 320) can then assemble into a network using either wired communication (e.g., ethernet), wireless communication, or a combination of the two.
With reference to
Many different situations can trigger an error function; for example, without limitation, a network can become bisected because a network link gets cut, the network becomes inoperable, there is a property of the network that fails independently, such as: the I/O portion of the network fails, even though the other portions appear to be working; for an extended period of time packets are getting dropped; when a master device loses ability to communicate via a network port, etc.
In such an instance, ad-hoc networks may be formed out of the controllers that are currently working. Master or leader elections may happen in these distributed systems, in various embodiments.
These systems may be thought of as self-federating. A federation of distributed components can be moved and changed over a distributed system in a self-organizing manner. In some embodiments, the smallest system that can federate comprises a sensor, a control point (a point on a device that controls the device) and a controller. There are many other levels of components that can federate, such as having each floor of a building self-federating, or some other grouping, which may be physical (all within a certain area), by type (all connected to to a certain device), size (this many devices and sensors per controller) or may be arbitrary.
In an embodiment, the network is self-healing. This means the network can heal around sensors (sensor fusion), and heal around controllers. The network may have redundancy in the sensors. Examining the sensor data around a broken sensor can provide likely data for that sensor. Controllers may also have redundancy so that sensors can find a new controller to talk to and send its sensor data to when the controller they are currently associated with goes down. The new controller will then understand what to do with the sensor data from this specific sensor.
In some instances, there may not be a single master controller; rather, there may be one master and multiple followers. Further the controllers could be organized as multiple masters or completely distributed, with no masters. In which case, there are ad-hoc controls that find one another. Work could thus be organized across an ad-hoc network.
With reference to
With reference to
A full HVAC system can be thought of as one big state changer, that produces cold or hot air. That air should be distributed throughout a building. To control how much of that hot/cold air goes into each rooms, a damper can be inserted in-line with the air distribution pipes. The damper is a plate inside the pipe, that has a motor on the side to open and close it. With reference to the damper picture, a motor 710 actuates the plate inside. A controller, which may be a satellite controller, may have a differential pressure sensor that can measure pressure difference between two tubes. One tube will go to the left, the other to the right of that damper. Using the difference in pressure between the tubes, how much air flows through that specific piece of pipe can now be calculated. Using sensors inside the room, given other states, such as temperature/humidity, etc, the controller system can now employ an algorithm to determine how much air it needs to pump into the room to achieve the optimal temperature, for example. Controllers, including satellite controllers, can interface with a multitude of different sensor types. In some implementations, the controller uses a satellite to modify defined space state on the fly to achieve comfort goals.
Within a defined space, sensors sense state values 1005, e.g., temperature values, humidity values, atmospheric pressure values, sound pressure values, occupancy values, indoor air quality values, CO2 concentration, light intensity, etc. These values are then sent, (e.g., at intervals, continually, etc.), to a controller. The controller the values are sent to may be a designated controller for a specific sensor. In some embodiments, sensors may send their values to a controller designated at the controller to receive their messages. If this controller is down, the sensors may send their messages to an alternate controller, and so on, until a controller is found that accepts their input. In some implementations, the sensor passes its state value to a master controller 1010. In some implementations, the sensor passes its value to a controller which then passes its value to a master controller. In some implementations, a sensor passes its value to its designated controller, and the controller does not pass the value to a master controller.
In some instances, the master controller determines that the state value indicates that an action needs to take place to ensure that the defined space stays within a set of defined operating characteristics. In such a case, the master controller may determine which device 1015 operating characteristics could be modified to have the defined space move back into the defined operating characteristics. This may require changing state of more than one device, or only changing state of a single device. The master controller may then pass the state value or a value that uses the state value to the controller 1020 to instruct it to activate the device 1025 (or devices) so it/they modifies the state of the defined space. In some embodiments, the master controller sends the device controller minimal information, with the device controller determining which device operating characteristics should be modified so the defined space state meets the defined operating characteristics.
In some embodiments, defined operating characteristics are a set of comfort goals that a space should reach within certain parameters. In the simplest form, comfort goals are state values, such as “the temperature should be between 70° and 72°. Occupancy comfort can be atomized to the comfort of specific humans. Parameters that can be determined on a person-specific level comprise: heat of person, convection, sweat, activity levels, metabolic rate, location, coo (the insulation value of the clothing a person is wearing),
The comfort goal may be used to determine permissible comfort values. In some embodiments the comfort goal is expressed as a number value (such as a value between 0 and 3) that defines how close to absolute comfort we hope the model to get. The comfort goal, in some implementations, gives an allowable error range for the specific state values allowed; a low value may indicate that the comfort value must be closely matched, while a higher number may indicate that there is more leeway allowed. The values may be reversed, such that a low value indicates a higher tolerance for error, etc. Some collection of these features may be used to determine how a device or devices should be modified to reach a stated comfort goal. The comfort level may consider: 1) Building comfort, 2) occupancy comfort, and/or 3) specific time-based issues.
Building comfort: Theses are basic building states to ensure that the building zone state does not cause problems for the actual building zone structure. Examples of this are: keeping building zones warm enough to not freeze the pipes, keeping the humidity at a level to not cause mildew, or to keep musical instruments and furniture from suffering damage, or to keep steel from rusting.
Occupancy Comfort: In one embodiment, some portion of the building may have a file of person (height, weight). The building may also be able to monitor activity level by, for example accessing a wearable fitness device or a phone associated with someone in the building. Through this information automation processes in the the building may be able to infer metabolic rate. Using the metabolic rate the master controller 905, and/or the device controller 935 may be able to make a good guess as to what temperature (or other states) the person with the wearable fitness device would prefer. A person could also have temperature and other building state preferences on file that the automation system then attempts to meet (within the other competing needs within the building.)
Specific time-based issues. A defined space, such as a building, may have set times when certain states should be met. For example, the times a building zone is closed to humans, it can have a different comfort level than when people are in it. When an office is scheduled to be empty, it might be assigned a different comfort level than when it is full of workers. If an office has a meeting scheduled at 1:00 pm with 25 attendees, at 1:00 pm 100 watts of energy (approx.) for each person will be in that office and so the temperature schedule may account for this.
The controller (or controller then activates the device 1025 (or devices) necessary to reach the comfort value and/or the defined operating characteristic. Activating may comprise turning a device on, turning a device off, turning a device to an intermediate setting, etc.
With reference to
A computing environment may have additional features. For example, the computing environment 1100 includes storage 1140, one or more input devices 1150, one or more output devices 1160, and one or more communication connections 1170. An interconnection mechanism (not shown) such as a bus, controller, or network interconnects the components of the computing environment 1100. Typically, operating system software (not shown) provides an operating environment for other software executing in the computing environment 1100, and coordinates activities of the components of the computing environment 1100. The computing system may also be distributed; running portions of the software 1185 on different CPUs.
The storage 1140 may be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, CD-RWs, DVDs, flash drives, or any other medium which can be used to store information and which can be accessed within the computing environment 1100. The storage 1140 stores instructions for the software to implement methods of neuron discretization and creation.
The input device(s) 1150 may be a touch input device such as a keyboard, mouse, pen, or trackball, a voice input device, a scanning device, touchscreen, or another device that provides input to the computing environment 1100. For audio, the input device(s) 1130 may be a sound card or similar device that accepts audio input in analog or digital form, or a CD-ROM reader that provides audio samples to the computing environment. The output device(s) 1135 may be a display, printer, speaker, CD-writer, or another device that provides output from the computing environment 1100.
The communication connection(s) 1140 enable communication over a communication medium to another computing entity. The communication medium conveys information such as computer-executable instructions, compressed graphics information, or other data in a modulated data signal. These connections may include network connections 1150, which may be wireless connections, may include dial-up connections, and so on. The other computing entity may be a portable communications device such as a wireless handheld device, a cell phone device, and so on. Users 1145 may interact with the communications devices 1140, the peripherals 1155, etc.
Computer-readable media are any available non-transient tangible media that can be accessed within a computing environment. By way of example, and not limitation, with the computing environment 1100, computer-readable storage media 1160 may include memory 1115, 1125, communication media, and combinations of any of the above. Configurable media 1160 which may be used to store computer readable media comprises instructions 1170 and data 1165.
Moreover, any of the methods, apparatus, and systems described herein can be used in conjunction with combining abstract interpreters in a wide variety of contexts.
Although the operations of some of the disclosed methods are described in a particular, sequential order for convenient presentation, it should be understood that this manner of description encompasses rearrangement, unless a particular ordering is required by specific language set forth below. For example, operations described sequentially can be rearranged or performed concurrently. Moreover, for the sake of simplicity, the attached figures may not show the various ways in which the disclosed methods, apparatus, and systems can be used in conjunction with other methods, apparatus, and systems. Additionally, the description sometimes uses terms like “determine,” “build,” “optimize,” and “identify” to describe the disclosed technology. These terms are high-level abstractions of the actual operations that are performed. The actual operations that correspond to these terms will vary depending on the particular implementation and are readily discernible by one of ordinary skill in the art.
Further, data produced from any of the disclosed methods can be created, updated, or stored on tangible computer-readable media (e.g., tangible computer-readable media, such as one or more CDs, volatile memory components (such as DRAM or SRAM), or nonvolatile memory components (such as hard drives) using a variety of different data structures or formats. Such data can be created or updated at a local computer or over a network (e.g., by a server computer), or stored and accessed in a cloud computing environment.
In view of the many possible embodiments to which the principles of the disclosed invention may be applied, it should be recognized that the illustrated embodiments are only examples of the invention and should not be taken as limiting the scope of the invention. Rather, the scope of the invention is defined by the following claims. We therefore claim as our invention all that comes within the scope and spirit of these claims.
The present application hereby incorporates by reference the entirety of, and claims priority to, U.S. Provisional Patent Application Ser. No. 63/070,460 filed 26 Aug. 2020.
Number | Name | Date | Kind |
---|---|---|---|
4353653 | Zimmerman | Oct 1982 | A |
5208765 | Turnbull | May 1993 | A |
5530643 | Hodorowski | Jun 1996 | A |
6275962 | Fuller et al. | Aug 2001 | B1 |
6301341 | Gizara et al. | Oct 2001 | B1 |
6437692 | Petite | Aug 2002 | B1 |
6606731 | Baum et al. | Aug 2003 | B1 |
6645066 | Gutta et al. | Nov 2003 | B2 |
6813777 | Weinberger et al. | Nov 2004 | B1 |
6891838 | Petite | May 2005 | B1 |
7102502 | Autret | Sep 2006 | B2 |
7578135 | Mattheis | Aug 2009 | B2 |
7587250 | Coogan et al. | Sep 2009 | B2 |
7729882 | Seem | Jun 2010 | B2 |
7734572 | Wiemeyer et al. | Jun 2010 | B2 |
7835431 | Belge | Nov 2010 | B2 |
7917232 | McCoy et al. | Mar 2011 | B2 |
8024054 | Mairs et al. | Sep 2011 | B2 |
8099178 | Mairs et al. | Jan 2012 | B2 |
8503943 | Spanhake | Aug 2013 | B2 |
8643476 | Pinn et al. | Feb 2014 | B2 |
8749959 | Riley et al. | Jun 2014 | B2 |
8782619 | Wu et al. | Jul 2014 | B2 |
8925358 | Kasper | Jan 2015 | B2 |
9441847 | Grohman | Sep 2016 | B2 |
9521724 | Berry et al. | Dec 2016 | B1 |
9544209 | Gielarowski et al. | Jan 2017 | B2 |
9602301 | Averitt | Mar 2017 | B2 |
9664400 | Wroblewski et al. | May 2017 | B2 |
9678494 | Hyde et al. | Jun 2017 | B2 |
9740385 | Fadell et al. | Aug 2017 | B2 |
9791872 | Wang et al. | Oct 2017 | B2 |
9857238 | Malhotra et al. | Jan 2018 | B2 |
9860961 | Chemel et al. | Jan 2018 | B2 |
9952573 | Sloo et al. | Apr 2018 | B2 |
10094586 | Pavlovski et al. | Oct 2018 | B2 |
10223721 | Bhatia | Mar 2019 | B1 |
10334758 | Ramirez et al. | Jun 2019 | B1 |
10512143 | Ikehara et al. | Dec 2019 | B1 |
10528016 | Noboa | Jan 2020 | B2 |
10557889 | Montoya et al. | Feb 2020 | B2 |
10558183 | Piaskowski et al. | Feb 2020 | B2 |
10627124 | Walser et al. | Apr 2020 | B2 |
10640211 | Whitten et al. | May 2020 | B2 |
10672293 | Labutov et al. | Jun 2020 | B2 |
10892946 | Costa et al. | Jan 2021 | B2 |
10900489 | Rendusara et al. | Jan 2021 | B2 |
10942871 | Cawse et al. | Mar 2021 | B2 |
10943444 | Boyd et al. | Mar 2021 | B2 |
10966068 | Tramiel et al. | Mar 2021 | B2 |
10966342 | Lairsey | Mar 2021 | B2 |
10969133 | Harvey | Apr 2021 | B2 |
11088989 | Gao et al. | Aug 2021 | B2 |
11294254 | Patterson et al. | Apr 2022 | B2 |
20040236547 | Rappaport et al. | Nov 2004 | A1 |
20050040247 | Pouchak | Feb 2005 | A1 |
20070096902 | Seeley et al. | May 2007 | A1 |
20070162288 | Springhart et al. | Jul 2007 | A1 |
20080222584 | Habib et al. | Sep 2008 | A1 |
20080270951 | Anand et al. | Oct 2008 | A1 |
20080277486 | Seem et al. | Nov 2008 | A1 |
20090189764 | Keller et al. | Jul 2009 | A1 |
20100005218 | Gower et al. | Jan 2010 | A1 |
20100025483 | Hoeynck et al. | Feb 2010 | A1 |
20100131933 | Kim et al. | May 2010 | A1 |
20100162037 | Maule et al. | Jun 2010 | A1 |
20100237891 | Lin et al. | Sep 2010 | A1 |
20110087988 | Ray et al. | Apr 2011 | A1 |
20110125930 | Tantos et al. | May 2011 | A1 |
20120102472 | Wu et al. | Apr 2012 | A1 |
20120221986 | Whitford et al. | Aug 2012 | A1 |
20130343207 | Cook et al. | Dec 2013 | A1 |
20130343388 | Stroud et al. | Dec 2013 | A1 |
20130343389 | Stroud et al. | Dec 2013 | A1 |
20130343390 | Moriarty et al. | Dec 2013 | A1 |
20130346987 | Raney et al. | Dec 2013 | A1 |
20140088772 | Lelkens | Mar 2014 | A1 |
20140101082 | Matsuoka et al. | Apr 2014 | A1 |
20140215446 | Araya et al. | Jul 2014 | A1 |
20140277757 | Wang et al. | Sep 2014 | A1 |
20140358291 | Wells | Dec 2014 | A1 |
20140364985 | Tiwari et al. | Dec 2014 | A1 |
20150081928 | Wintzell et al. | Mar 2015 | A1 |
20150198938 | Steele et al. | Jul 2015 | A1 |
20150234381 | Ratilla et al. | Aug 2015 | A1 |
20160016454 | Yang et al. | Jan 2016 | A1 |
20160062753 | Champagne | Mar 2016 | A1 |
20160086242 | Schafer et al. | Mar 2016 | A1 |
20160088438 | O'Keeffe | Mar 2016 | A1 |
20160092427 | Bittmann | Mar 2016 | A1 |
20160132308 | Muldoon | May 2016 | A1 |
20160195856 | Spero | Jul 2016 | A1 |
20160285715 | Gielarowski et al. | Sep 2016 | A1 |
20160295663 | Hyde et al. | Oct 2016 | A1 |
20170075323 | Shrivastava et al. | Mar 2017 | A1 |
20170097259 | Brown et al. | Apr 2017 | A1 |
20170131611 | Brown et al. | May 2017 | A1 |
20170149638 | Gielarowski et al. | May 2017 | A1 |
20170159962 | Walser | Jun 2017 | A1 |
20170169075 | Jiang et al. | Jun 2017 | A1 |
20170176034 | Hussain et al. | Jun 2017 | A1 |
20170322579 | Goparaju et al. | Nov 2017 | A1 |
20170365908 | Hughes et al. | Dec 2017 | A1 |
20180005195 | Jacobson | Jan 2018 | A1 |
20180075168 | Tiwari et al. | Mar 2018 | A1 |
20180089172 | Needham | Mar 2018 | A1 |
20180202678 | Ahuja et al. | Jul 2018 | A1 |
20180224819 | Noboa | Aug 2018 | A1 |
20180266716 | Bender et al. | Sep 2018 | A1 |
20180307781 | Byers et al. | Oct 2018 | A1 |
20190087076 | Dey et al. | Mar 2019 | A1 |
20190138704 | Shrivastava et al. | May 2019 | A1 |
20190156443 | Hall | May 2019 | A1 |
20190173109 | Wang | Jun 2019 | A1 |
20190294018 | Shrivastava et al. | Sep 2019 | A1 |
20200003444 | Yuan et al. | Jan 2020 | A1 |
20200018506 | Ruiz et al. | Jan 2020 | A1 |
20200050161 | Noboa | Feb 2020 | A1 |
20200133257 | Cella et al. | Apr 2020 | A1 |
20200150508 | Patterson et al. | May 2020 | A1 |
20200167442 | Roecker et al. | May 2020 | A1 |
20200187147 | Meerbeek et al. | Jun 2020 | A1 |
20200221269 | Tramiel | Jul 2020 | A1 |
20200226223 | Reichl | Jul 2020 | A1 |
20200228759 | Ryan et al. | Jul 2020 | A1 |
20200255142 | Whitten et al. | Aug 2020 | A1 |
20200279482 | Berry et al. | Sep 2020 | A1 |
20200287786 | Anderson et al. | Sep 2020 | A1 |
20200288558 | Anderson et al. | Sep 2020 | A1 |
20200342526 | Ablanczy | Oct 2020 | A1 |
20200379730 | Graham et al. | Dec 2020 | A1 |
20200387041 | Shrivastava et al. | Dec 2020 | A1 |
20200387129 | Chandaria | Dec 2020 | A1 |
20210073441 | Austern | Mar 2021 | A1 |
20210081504 | Mccormick et al. | Mar 2021 | A1 |
20210081880 | Bivins et al. | Mar 2021 | A1 |
20210157312 | Cella et al. | May 2021 | A1 |
20210182660 | Amirguliyev et al. | Jun 2021 | A1 |
20210366793 | Hung et al. | Nov 2021 | A1 |
20210383041 | Harvey et al. | Dec 2021 | A1 |
20210400787 | Abbo et al. | Dec 2021 | A1 |
20220070293 | Harvey et al. | Mar 2022 | A1 |
Number | Date | Country |
---|---|---|
103926912 | May 2014 | CN |
103926912 | Jul 2014 | CN |
206002869 | Aug 2016 | CN |
206002869 | Mar 2017 | CN |
206489622 | Sep 2017 | CN |
206489622 | Sep 2017 | CN |
6301341 | Mar 2018 | JP |
2008016500 | Mar 2008 | WO |
2012019328 | Feb 2012 | WO |
Entry |
---|
Gungor, Vehbi C., and Gerhard P. Hancke. “Industrial wireless sensor networks: Challenges, design principles, and technical approaches.” IEEE Transactions on industrial electronics 56.10 (2009): pp. 4258-4265. (Year: 2009). |
Amin, Massoud. “Toward self-healing energy infrastructure systems.” IEEE Computer Applications in Power 14.1 (2001): pp. 20-28. (Year: 2001). |
Guo, Wenqi, and Mengchu Zhou. “An emerging technology for improved building automation control.” 2009 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2009.pp.337-342 (Year: 2009). |
Wenqi Wendy, G. U. O., Willam M. Healy, and Z. H. O. U. Mengchu. “Wireless mesh networks in intelligent building automation control: A survey.” International Journal of Intelligent Control and Systems 16.1 (2011): pp. 28-36. (Year: 2011). |
Shailendra, Eshan, and Praneet Kaur Bhatia. “Analyzing home automation and networking technologies.” IEEE Potentials 37.1 (2018): pp. 27-33. (Year: 2018). |
Kastner, Wolfgang, et al. “Building Automation Systems Integration into the Internet of Things The IoT6 approach, its realization and validation.” Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA). IEEE, 2014.pp. 1-9 (Year: 2014). |
Wang et al., “A Practical Multi-Sensor Cooling Demand Estimation Approach Based on Visual Indoor and Outdoor Information Sensing,” Sensors 2018, 18, 3591; doi:10.3390. |
De Meester et al., SERIF:A Semantic ExeRcise Interchange FormatConference: Proceedings of the 1st International Workshop on LINKed EDucation, Oct. 2015. |
Kalagnanam et al., “A System For Automated Mapping of Bill-of_Materials Part Numbers”, KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, Aug. 2004, pp. 805-810. |
Mouser Electronics News Release, Aug. 16, 2018. |
Ouf et al., Effectiveness of using WiFi technologies to detect and predict building occupancy, Sust. Buildi. 2, 7 (2017). |
RadioMaze, Inc., “WiFi signals enable motion recognition throughout the entire home,” Dec. 4, 2017. |
Sensorswarm, 2018. |
Serale G., et al., Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities, Energies 2018, 11, 631; doi:10.3390, Mar. 12, 2018. |
Siano, P, “Demand response and smart grids—A survey”, Renewable and Sustainable Energy Reviews 30 (2014) 461-478. |
Yegulap, Serdar, “What is LLVM? The power behind Swift, Rust, Clang, and more,” Infoworld, Mar. 11, 2020. |
BigLadder Software Full Ref, Occupant Thermal Comfort: Engineering Reference, 2014, The Board of Trustees of the University of Illinois and the Regents of the University of California through the Ernest Orlando Lawrence Berkeley National Laboratory (Year: 2014). |
Hagentoft et al. Full Reference, Assessment Method of Numerical Prediction Models for Combined Heat, Air and Moisture Transfer in Building Components: Benchmarks for One-dimensional Cases, Journal of Thermal Env. & Bldg. Sci., vol. 27, No. 4, Apr. 2004. |
U.S. Appl. No. 15/995,019 (7340.2.2) Office Action dated Jul. 26, 2019. |
U.S. Appl. No. 15/995,019 (7340.2.2) Office Action dated Oct. 8, 2020. |
U.S. Appl. No. 15/995,019 (7340.2.2) Office Action dated Apr. 15, 2020. |
Assad, et al., Back Propagation Neural Networks (BPNN) and Sigmoid Activation Function in Multi-Layer Networks, Academic Journal of Nawroz University 8(4):216, Nov. 2019. |
Number | Date | Country | |
---|---|---|---|
20220066402 A1 | Mar 2022 | US |
Number | Date | Country | |
---|---|---|---|
63070460 | Aug 2020 | US |