[0001] The present disclosure generally relates to the field of monitoring and diagnostic solutions for appliances, and more particularly to a system and a method for management of appliances in a premise by analysing energy consumption, and determining diagnostic and prognostic measures therefor.

BACKGROUND
[0002] Consumers have become increasingly aware of the environmental impact of their energy usage, often expressed as a “carbon footprint.” Thus, reduction in energy usage translates into both economic and ecological benefits for energy users. But, in order to make choices on how best to reduce usage, the consumer needs relevant energy usage information about the appliances, particularly large appliances, such as air-conditioners, geysers, etc., in their homes and offices, and usage patterns about the energy usage. Current solutions for monitoring home and office energy usage include monitoring total, aggregate power usage of a plurality of electrical devices at a single power supply point, monitoring power usage at one or more wall outlet power supply points, controlling the state of individual devices through timed, programmatic control of devices, and manually controlling devices by observing that one or more electrical devices are on, but not in use, and switching the device(s) off.
[0003] For instance, the simplest, and most widespread, aggregate power usage monitoring system known is a power utility monitoring the total usage of a customer via a power meter located at the customer's supply point, located near the branch off of the main distribution grid. The power meter accumulates total power used, the meter is actually read once per fixed period, such as a month, and the customer is billed for the power that the customer used in that billing period. Utilities have billing rates which are tiered, where a higher rate is charged per kilowatt hour (KWH) after a certain usage threshold is reached. Some tariff schedules charge a higher rate per KWH based upon a peak demand time usage, also known as “time of day tariff.” Utility bills will often breakdown the total power usage in accordance with the tariff schedule to show customers how much power was charged at each tariff rate. With this information, there are few facts available to a customer with which to make power saving decisions.
[0004] Furthermore, traditional appliances used in home and offices are typically standalone devices, operating on their own without cooperation between or communication among other devices. As a result (as one example), it may be difficult to diagnose problems with such appliances, and thus may result in high expenditures of time and effort by repair personnel to take necessary corrective action to resolve the problems. As another example, the current and proper operation of an appliance generally could not be determined without being physically present at the appliance. Some modern appliances come with power monitoring and diagnostic means built therein, using Internet-of-Things (IoT) technology. But this requires the customer to replace existing appliances, and purchase and install new appliances with such functionality, which is an expensive affair and comes with a lot of hassles. There are some solutions known for retrofitting devices for power monitoring and diagnostic means in existing appliances, but such solutions have been unpopular because, in general, the procedure is invasive and may require destruction of interior surfaces, substantial rewiring, significant expense, and inconvenience to the customer. Moreover, herein, the said IoT based appliances solution as well as the retrofitting solution only work at single appliance level and thus could significantly add to the cost to implement such solutions for all of existing appliances in a home or office environment.
[0005] The present disclosure has been made in view of such considerations, and it is an object of the present disclosure to provide systems and methods for management of one or more appliances in a premise to overcome the limitations of the prior-art.

SUMMARY
[0006] In an aspect, a system for management of one or more appliances in a premise is disclosed. The system comprises a monitoring unit installed with a main electricity supply for the premise. The monitoring unit comprises a number of current transformers corresponding to a number of phases of the main electricity supply, with each of the current transformers electrically coupled with one of the phases of the main electricity supply. The monitoring unit is configured to measure electricity consumption parameters for each of the phases in the main electricity supply for the premise using the current transformers. The system further comprises a cloud server. The system further comprises a communication unit configured to receive the measured electricity consumption parameters from the monitoring unit and transmit the measured electricity consumption parameters as electricity consumption parameters data to the cloud server. Herein, the cloud server is configured to select a dataset from the electricity consumption parameters data for a running time-period of a predefined duration. Further, the cloud server is configured to analyse the selected dataset to determine instances of at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period. Further, the cloud server is configured to identify the one or more appliances and determine operational parameters of the identified one or more appliances based on the analysis. Furthermore, the cloud server is configured to determine one or more of diagnostic measures and prognostic measures to be implemented for the one or more appliances based on the determined operational parameters thereof.
[0007] In one or more embodiments, the cloud server is further configured to count number of instances of the at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period, in the analysis of the selected dataset.
[0008] In one or more embodiments, the cloud server is further configured to filter datapoints in the electricity consumption parameters data with one or more of the electricity consumption parameters outside of corresponding normal usage range, before selecting the dataset.
[0009] In one or more embodiments, in case the appliance is an air-conditioner appliance: the determined operational parameters of the one or more appliances comprise one or more of compressor cycle ON time, compressor cycle OFF time, fan cycle ON time, fan cycle OFF time and compressor average power consumption; and the determined diagnostic measures and prognostic measures comprise one or more of gas leakage in the air-conditioner appliance based on the compressor average power consumption in relation to average outside temperature, clogging of filter in the air-conditioner appliance based on the fan cycle ON time and the fan cycle OFF time in relation to available pollution level for location of the premise, and fault in motor and/or compressor in the air-conditioner appliance based on the compressor cycle ON time in relation to average outside temperature.
[0010] In one or more embodiments, the electricity consumption parameters comprise one or more of voltage, current, energy, power, frequency and power factor for the corresponding phase of the main electricity supply.
[0011] In one or more embodiments, the cloud server is further configured to perform real-time analytics on the electricity consumption parameters data to identify issues with the main electricity supply, including one or more of poor power factor, low voltage, high voltage, voltage fluctuation, high current and irregular frequency.
[0012] In another aspect, a method for management of one or more appliances in a premise is disclosed. The method comprises measuring electricity consumption parameters for each of phases in a main electricity supply for the premise. The method further comprises transmitting the measured electricity consumption parameters as electricity consumption parameters data. The method further comprises selecting a dataset from the electricity consumption parameters data for a running time-period of a predefined duration. The method further comprises analysing the selected dataset to determine instances of at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period. The method further comprises identifying the one or more appliances and determining operational parameters of the identified one or more appliances based on the analysis. The method further comprises determining one or more of diagnostic measures and prognostic measures to be implemented for the one or more appliances based on the determined operational parameters thereof.
[0013] In one or more embodiments, the step of analysing the selected dataset, further comprises counting number of instances of the at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period.
[0014] In one or more embodiments, the method further comprises filtering datapoints in the electricity consumption parameters data with one or more of the electricity consumption parameters outside of corresponding normal usage range, before selecting the dataset.
[0015] In one or more embodiments, the electricity consumption parameters comprise one or more of voltage, current, energy, power, frequency and power factor for the corresponding phase of the main electricity supply, and wherein the method further comprises performing real-time analytics on the electricity consumption parameters data to identify issues with the main electricity supply, including one or more of poor power factor, low voltage, high voltage, voltage fluctuation, high current and irregular frequency.
[0016] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE FIGURES
[0017] For a more complete understanding of example embodiments of the present disclosure, reference is now made to the following descriptions taken in connection with the accompanying drawings in which:
[0018] FIG. 1 illustrates a system that may reside on and may be executed by a computer, which may be connected to a network, in accordance with one or more exemplary embodiments of the present disclosure;
[0019] FIG. 2 illustrates a diagrammatic view of a server, in accordance with one or more exemplary embodiments of the present disclosure;
[0020] FIG. 3 illustrates a diagrammatic view of a user device, in accordance with one or more exemplary embodiments of the present disclosure;
[0021] FIG. 4 is a schematic illustration of an architecture of the system for management of one or more appliances in a premise, in accordance with one or more exemplary embodiments of the present disclosure;
[0022] FIG. 5 is a schematic illustration of an environment in which the monitoring device of the system of FIG. 4 is implemented for managing multiple appliances, in accordance with an exemplary embodiment of the present disclosure;
[0023] FIG. 6 is a schematic block diagram of implementation of the monitoring unit in the system of FIG. 4, in accordance with one or more exemplary embodiments of the present disclosure;
[0024] FIG. 7A is a graphical representation of a typical active power consumption cycle of an air-conditioner as an appliance to be managed by the system of FIG. 4, in accordance with one or more exemplary embodiments of the present disclosure;
[0025] FIG. 7B is a graphical representation of a typical active power consumption versus reactive power consumption of an air-conditioner as an appliance to be managed by the system of FIG. 4, in accordance with one or more exemplary embodiments of the present disclosure;
[0026] FIGS. 8A-8C are flowcharts listing steps involved in establishing telemetry and for data transmission in the system of FIG. 4, in accordance with another exemplary embodiment of the present disclosure;
[0027] FIG. 9 is a flowchart listing steps involved in authentication for establishing telemetry and for data transmission in the system of FIG. 4, in accordance with one or more exemplary embodiments of the present disclosure;
[0028] FIG. 10 is a flowchart listing steps involved in collection/ingestion of data in the system of FIG. 4, in accordance with one or more exemplary embodiments of the present disclosure; and
[0029] FIG. 11 is a flowchart listing steps involved in a method for management of one or more appliances in a premise, in accordance with one or more exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION
[0030] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure is not limited to these specific details.
[0031] Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Further, the terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not for other embodiments.
[0032] Furthermore, in the following detailed description of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be understood that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present disclosure.
[0033] Embodiments described herein may be discussed in the general context of computer-executable instructions residing on some form of computer-readable storage medium, such as program modules, executed by one or more computers or other devices. By way of example, and not limitation, computer-readable storage media may comprise non-transitory computer-readable storage media and communication media; non-transitory computer-readable media include all computer-readable media except for a transitory, propagating signal. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or distributed as desired in various embodiments.
[0034] Some portions of the detailed description that follows are presented and discussed in terms of a process or method. Although steps and sequencing thereof are disclosed in figures herein describing the operations of this method, such steps and sequencing are exemplary. Embodiments are well suited to performing various other steps or variations of the steps recited in the flowchart of the figure herein, and in a sequence other than that depicted and described herein. Some portions of the detailed descriptions that follow are presented in terms of procedures, logic blocks, processing, and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those utilizing physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as transactions, bits, values, elements, symbols, characters, samples, pixels, or the like.
[0035] In some implementations, any suitable computer usable or computer readable medium (or media) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer-usable, or computer-readable, storage medium (including a storage device associated with a computing device) may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fibre, a portable compact disc read-only memory (CD-ROM), an optical storage device, a digital versatile disk (DVD), a static random access memory (SRAM), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, a media such as those supporting the internet or an intranet, or a magnetic storage device. Note that the computer-usable or computer-readable medium could even be a suitable medium upon which the program is stored, scanned, compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory. In the context of the present disclosure, a computer-usable or computer-readable, storage medium may be any tangible medium that can contain or store a program for use by or in connection with the instruction execution system, apparatus, or device.
[0036] In some implementations, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. In some implementations, such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. In some implementations, the computer readable program code may be transmitted using any appropriate medium, including but not limited to the internet, wireline, optical fibre cable, RF, etc. In some implementations, a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
[0037] In some implementations, computer program code for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java®, Smalltalk, C++ or the like. Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. However, the computer program code for carrying out operations of the present disclosure may also be written in conventional procedural programming languages, such as the "C" programming language, PASCAL, or similar programming languages, as well as in scripting languages such as JavaScript, PERL, or Python. In present implementations, the used language for training may be one of Python, TensorflowTM, Bazel, C, C++. Further, decoder in user device (as will be discussed) may use C, C++ or any processor specific ISA. Furthermore, assembly code inside C/C++ may be utilized for specific operation. Also, ASR (automatic speech recognition) and G2P decoder along with entire user system can be run in embedded Linux (any distribution), Android, iOS, Windows, or the like, without any limitations. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the internet using an Internet Service Provider). In some implementations, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs) or other hardware accelerators, micro-controller units (MCUs), or programmable logic arrays (PLAs) may execute the computer readable program instructions/code by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
[0038] In some implementations, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus (systems), methods and computer program products according to various implementations of the present disclosure. Each block in the flowchart and/or block diagrams, and combinations of blocks in the flowchart and/or block diagrams, may represent a module, segment, or portion of code, which comprises one or more executable computer program instructions for implementing the specified logical function(s)/act(s). These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program instructions, which may execute via the processor of the computer or other programmable data processing apparatus, create the ability to implement one or more of the functions/acts specified in the flowchart and/or block diagram block or blocks or combinations thereof. It should be noted that, in some implementations, the functions noted in the block(s) may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
[0039] In some implementations, these computer program instructions may also be stored in a computer-readable memory 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 memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks or combinations thereof.
[0040] In some implementations, the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed (not necessarily in a particular order) on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts (not necessarily in a particular order) specified in the flowchart and/or block diagram block or blocks or combinations thereof.
[0041] Referring now to the example implementation of FIG. 1, there is shown a system 100 that may reside on and may be executed by a computer (e.g., computer 12), which may be connected to a network (e.g., network 14) (e.g., the internet or a local area network). Examples of computer 12 may include, but are not limited to, a personal computer(s), a laptop computer(s), mobile computing device(s), a server computer, a series of server computers, a mainframe computer(s), or a computing cloud(s). In some implementations, each of the aforementioned may be generally described as a computing device. In certain implementations, a computing device may be a physical or virtual device. In many implementations, a computing device may be any device capable of performing operations, such as a dedicated processor, a portion of a processor, a virtual processor, a portion of a virtual processor, portion of a virtual device, or a virtual device. In some implementations, a processor may be a physical processor or a virtual processor. In some implementations, a virtual processor may correspond to one or more parts of one or more physical processors. In some implementations, the instructions/logic may be distributed and executed across one or more processors, virtual or physical, to execute the instructions/logic. Computer 12 may execute an operating system, for example, but not limited to, Microsoft® Windows®; Mac® OS X®; Red Hat® Linux®, or a custom operating system. (Microsoft and Windows are registered trademarks of Microsoft Corporation in the United States, other countries or both; Mac and OS X are registered trademarks of Apple Inc. in the United States, other countries or both; Red Hat is a registered trademark of Red Hat Corporation in the United States, other countries or both; and Linux is a registered trademark of Linus Torvalds in the United States, other countries or both).
[0042] In some implementations, the instruction sets and subroutines of system 100, which may be stored on storage device, such as storage device 16, coupled to computer 12, may be executed by one or more processors (not shown) and one or more memory architectures included within computer 12. In some implementations, storage device 16 may include but is not limited to: a hard disk drive; a flash drive, a tape drive; an optical drive; a RAID array (or other array); a random-access memory (RAM); and a read-only memory (ROM).
[0043] In some implementations, network 14 may be connected to one or more secondary networks (e.g., network 18), examples of which may include but are not limited to: a local area network; a wide area network; or an intranet, for example.
[0044] In some implementations, computer 12 may include a data store, such as a database (e.g., relational database, object-oriented database, triplestore database, etc.) and may be located within any suitable memory location, such as storage device 16 coupled to computer 12. In some implementations, data, metadata, information, etc. described throughout the present disclosure may be stored in the data store. In some implementations, computer 12 may utilize any known database management system such as, but not limited to, DB2, in order to provide multi-user access to one or more databases, such as the above noted relational database. In some implementations, the data store may also be a custom database, such as, for example, a flat file database or an XML database. In some implementations, any other form(s) of a data storage structure and/or organization may also be used. In some implementations, system 100 may be a component of the data store, a standalone application that interfaces with the above noted data store and/or an applet / application that is accessed via client applications 22, 24, 26, 28. In some implementations, the above noted data store may be, in whole or in part, distributed in a cloud computing topology. In this way, computer 12 and storage device 16 may refer to multiple devices, which may also be distributed throughout the network.
[0045] In some implementations, computer 12 may execute application 20 for management of one or more appliances in a premise (as discussed later in more detail). In some implementations, system 100 and/or application 20 may be accessed via one or more of client applications 22, 24, 26, 28. In some implementations, system 100 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within application 20, a component of application 20, and/or one or more of client applications 22, 24, 26, 28. In some implementations, application 20 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within system 100, a component of system 100, and/or one or more of client applications 22, 24, 26, 28. In some implementations, one or more of client applications 22, 24, 26, 28 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within and/or be a component of system 100 and/or application 20. Examples of client applications 22, 24, 26, 28 may include, but are not limited to, a standard and/or mobile web browser, an email application (e.g., an email client application), a textual and/or a graphical user interface, a customized web browser, a plugin, an Application Programming Interface (API), or a custom application. The instruction sets and subroutines of client applications 22, 24, 26, 28, which may be stored on storage devices 30, 32, 34, 36, coupled to user devices 38, 40, 42, 44, may be executed by one or more processors and one or more memory architectures incorporated into user devices 38, 40, 42, 44.
[0046] In some implementations, one or more of storage devices 30, 32, 34, 36, may include but are not limited to: hard disk drives; flash drives, tape drives; optical drives; RAID arrays; random access memories (RAM); and read-only memories (ROM). Examples of user devices 38, 40, 42, 44 (and/or computer 12) may include, but are not limited to, a personal computer (e.g., user device 38), a laptop computer (e.g., user device 40), a smart/data-enabled, cellular phone (e.g., user device 42), a notebook computer (e.g., user device 44), a tablet (not shown), a server (not shown), a television (not shown), a smart television (not shown), a media (e.g., video, photo, etc.) capturing device (not shown), and a dedicated network device (not shown). User devices 38, 40, 42, 44 may each execute an operating system, examples of which may include but are not limited to, Android®, Apple® iOS®, Mac® OS X®; Red Hat® Linux®, or a custom operating system.
[0047] In some implementations, one or more of client applications 22, 24, 26, 28 may be configured to effectuate some or all of the functionality of system 100 (and vice versa). Accordingly, in some implementations, system 100 may be a purely server-side application, a purely client-side application, or a hybrid server-side / client-side application that is cooperatively executed by one or more of client applications 22, 24, 26, 28 and/or system 100.
[0048] In some implementations, one or more of client applications 22, 24, 26, 28 may be configured to effectuate some or all of the functionality of application 20 (and vice versa). Accordingly, in some implementations, application 20 may be a purely server-side application, a purely client-side application, or a hybrid server-side / client-side application that is cooperatively executed by one or more of client applications 22, 24, 26, 28 and/or application 20. As one or more of client applications 22, 24, 26, 28, system 100, and application 20, taken singly or in any combination, may effectuate some or all of the same functionality, any description of effectuating such functionality via one or more of client applications 22, 24, 26, 28, system 100, application 20, or combination thereof, and any described interaction(s) between one or more of client applications 22, 24, 26, 28, system 100, application 20, or combination thereof to effectuate such functionality, should be taken as an example only and not to limit the scope of the disclosure.
[0049] In some implementations, one or more of users 46, 48, 50, 52 may access computer 12 and system 100 (e.g., using one or more of user devices 38, 40, 42, 44) directly through network 14 or through secondary network 18. Further, computer 12 may be connected to network 14 through secondary network 18, as illustrated with phantom link line 54. System 100 may include one or more user interfaces, such as browsers and textual or graphical user interfaces, through which users 46, 48, 50, 52 may access system 100.
[0050] In some implementations, the various user devices may be directly or indirectly coupled to communication network, such as communication network 14 and communication network 18, hereinafter simply referred to as network 14 and network 18, respectively. For example, user device 38 is shown directly coupled to network 14 via a hardwired network connection. Further, user device 44 is shown directly coupled to network 18 via a hardwired network connection. User device 40 is shown wirelessly coupled to network 14 via wireless communication channel 56 established between user device 40 and wireless access point (i.e., WAP) 58, which is shown directly coupled to network 14. WAP 58 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, Wi-Fi®, RFID, and/or BluetoothTM (including BluetoothTM Low Energy) device that is capable of establishing wireless communication channel 56 between user device 40 and WAP 58. User device 42 is shown wirelessly coupled to network 14 via wireless communication channel 60 established between user device 42 and cellular network / bridge 62, which is shown directly coupled to network 14.
[0051] In some implementations, some or all of the IEEE 802.11x specifications may use Ethernet protocol and carrier sense multiple access with collision avoidance (i.e., CSMA/CA) for path sharing. The various 802.11x specifications may use phase-shift keying (i.e., PSK) modulation or complementary code keying (i.e., CCK) modulation, for example, BluetoothTM (including BluetoothTM Low Energy) is a telecommunications industry specification that allows, e.g., mobile phones, computers, smart phones, and other electronic devices to be interconnected using a short-range wireless connection. Other forms of interconnection (e.g., Near Field Communication (NFC)) may also be used.
[0052] The system 100 may include a server (such as server 200, as shown in FIG. 2) for management of one or more appliances in a premise (as will be described later in more detail). Herein, FIG. 2 is a block diagram of an example of the server 200 capable of implementing embodiments according to the present disclosure. In one embodiment, an application server as described herein may be implemented on exemplary server 200. In the example of FIG. 2, the server 200 includes a processing unit 205 (hereinafter, referred to as CPU 205) for running software applications (such as, the application 20 of FIG. 1) and optionally an operating system. As illustrated, the server 200 further includes a database 210 (hereinafter, referred to as memory 210) which stores applications and data for use by the CPU 205. Storage 215 provides non-volatile storage for applications and data and may include fixed disk drives, removable disk drives, flash memory devices, and CD-ROM, DVD-ROM or other optical storage devices. An optional user input device 220 includes devices that communicate user inputs from one or more users to the server 200 and may include keyboards, mice, joysticks, touch screens, etc. A communication or network interface 225 is provided which allows the server 200 to communicate with other computer systems via an electronic communications network, including wired and/or wireless communication and including an Intranet or the Internet. In one embodiment, the server 200 receives instructions and user inputs from a remote computer through communication interface 225. Communication interface 225 can comprise a transmitter and receiver for communicating with remote devices. An optional display device 250 may be provided which can be any device capable of displaying visual information in response to a signal from the server 200. The components of the server 200, including the CPU 205, memory 210, data storage 215, user input devices 220, communication interface 225, and the display device 250, may be coupled via one or more data buses 260.
[0053] In the embodiment of FIG. 2, a graphics system 230 may be coupled with the data bus 260 and the components of the server 200. The graphics system 230 may include a physical graphics processing unit (GPU) 235 and graphics memory. The GPU 235 generates pixel data for output images from rendering commands. The physical GPU 235 can be configured as multiple virtual GPUs that may be used in parallel (concurrently) by a number of applications or processes executing in parallel. For example, mass scaling processes for rigid bodies or a variety of constraint solving processes may be run in parallel on the multiple virtual GPUs. Graphics memory may include a display memory 240 (e.g., a framebuffer) used for storing pixel data for each pixel of an output image. In another embodiment, the display memory 240 and/or additional memory 245 may be part of the memory 210 and may be shared with the CPU 205. Alternatively, the display memory 240 and/or additional memory 245 can be one or more separate memories provided for the exclusive use of the graphics system 230. In another embodiment, graphics processing unit 230 includes one or more additional physical GPUs 255, similar to the GPU 235. Each additional GPU 255 may be adapted to operate in parallel with the GPU 235. Each additional GPU 255 generates pixel data for output images from rendering commands. Each additional physical GPU 255 can be configured as multiple virtual GPUs that may be used in parallel (concurrently) by a number of applications or processes executing in parallel, e.g. processes that solve constraints. Each additional GPU 255 can operate in conjunction with the GPU 235, for example, to simultaneously generate pixel data for different portions of an output image, or to simultaneously generate pixel data for different output images. Each additional GPU 255 can be located on the same circuit board as the GPU 235, sharing a connection with the GPU 235 to the data bus 260, or each additional GPU 255 can be located on another circuit board separately coupled with the data bus 260. Each additional GPU 255 can also be integrated into the same module or chip package as the GPU 235. Each additional GPU 255 can have additional memory, similar to the display memory 240 and additional memory 245, or can share the memories 240 and 245 with the GPU 235. It is to be understood that the circuits and/or functionality of GPU as described herein could also be implemented in other types of processors, such as general-purpose or other special-purpose coprocessors, or within a CPU.
[0054] The system 100 may also include a user device 300 (as shown in FIG. 3). In embodiments of the present disclosure, the user device 300 may embody a standalone physical remote control, a smartphone or a virtual assistant (as discussed later in more detail). Herein, FIG. 3 is a block diagram of an example of the user device 300 capable of implementing embodiments according to the present disclosure. In the example of FIG. 3, the user device 300 includes a processing unit 305 (hereinafter, referred to as CPU 305) for running software applications (such as, the application 20 of FIG. 1) and optionally an operating system. A user input device 320 is provided which includes devices that communicate user inputs from one or more users and may include keyboards, mice, joysticks, touch screens, and/or microphones. Further, a network interface 325 is provided which allows the user device 300 to communicate with other computer systems (e.g., the server 200 of FIG. 2) via an electronic communications network, including wired and/or wireless communication and including the Internet. The user device 300 may also include a decoder 355 may be any device capable of decoding (decompressing) data that may be encoded (compressed). A display device 350 may be provided which may be any device capable of displaying visual information, including information received from the decoder 355. In particular, as will be described below, the display device 350 may be used to display visual information received from the server 200 of FIG. 2. The components of the user device 300 may be coupled via one or more data buses 360.
[0055] It may be seen that compared to the server 200 in the example of FIG. 2, the user device 300 in the example of FIG. 3 may have fewer components and less functionality. However, the user device 300 may include other components, for example, in addition to those described above. In general, the user device 300 may be any type of device that has one or more of display capability and the capability to receive inputs from a user and send such inputs to the server 200. However, it may be appreciated that the user device 300 may have additional capabilities beyond those just mentioned.
[0056] Referring now to FIG. 4, illustrated is an exemplary architecture of a system 400 (which may be similar to or be part of the system 100), in accordance with one or more exemplary embodiments of the present disclosure. The present system 400 is implemented for a premise 401A with a main electricity supply 401B. Herein, the main electricity supply 401B may be a main or sub-distribution line without any limitations. In particular, the present system 400 is utilized for management of one or more appliances (referred by the numeral 14) in the premise 401A. In the exemplary illustration of FIG. 4, the premise 401A has been represented as a home 401A (as depicted); however, it may be contemplated that the premise 401A may be an office space, a building with multiple apartments or even a society with multiple homes and/or offices, without any limitations. Herein, the main electricity supply 401B may be provided by a power distribution company and/or renewable sources, like solar panels installed in the premise 401A, or any other power source suitable for use in the said exemplary premise 401A as known in the art. Further, in the exemplary illustration, although only one appliance 401C has been shown, it may be appreciated that the system 400 is capable of managing multiple appliances 401C in the premise 401A. In the exemplary illustration, the appliance 401C (as depicted) is an air-conditioner; however, it may be noted that the other appliances 401C that could be managed by the system 400 may include room heaters, geysers or any other large appliances typically utilized in home and/or office environment. For the purposes of the present disclosure, the system 400 has been explained primarily in terms of managing energy and determining diagnostic and prognostic measures for heating and cooling function-based appliances; however, other types of appliances may be contemplated without departing from the spirit and the scope of the present disclosure.
[0057] In the premise 401A, the main electricity supply 401B provides electric power for operation of the appliance 401C. In one or more examples of the present disclosure, the main electricity supply 401B may be a single-phase power supply or a multi-phase power supply without any limitations. Residential homes are usually served by a single-phase power supply, while commercial and industrial facilities usually use a three-phase supply. Herein, the phase refers to the distribution of a load. Single-phase power is a two-wire alternating current (AC) power circuit. Typically, there is one power wire (phase wire) and one neutral wire, with current flowing between the power wire (through the load) and the neutral wire. Three-phase power is a three-wire (or four-wire) AC power circuit with each phase AC signal being 120 electrical degrees apart. It may be appreciated by a person skilled in the art, there are various factors related to the main electricity supply 401B which may be monitored to understand and estimate parameters related to the operation of the appliance 401C, including, but not limited to, voltage, current, energy (including active energy, apparent energy and reactive energy), power (including active power, apparent power and reactive power), frequency and power factor. Herein, the “power” in the main electricity supply 401B as measured may be active power provided by the main electricity supply 401B.
[0058] As illustrated in FIG. 4, the system 400 includes a monitoring unit 402. The monitoring unit 402 is installed with the main electricity supply 401B for the premise 401A. In an example, the main electricity supply 401B may be received through a junction box (not shown) installed in the premise 401A. Herein, the monitoring unit 402 may be installed in the junction box itself. As discussed, as the main electricity supply 401B provides electric power for operation of the appliance 401C in the premise 401A, thus by monitoring the main electricity supply 401B it may be possible to estimate parameters related to the operation of the appliance 401C. Herein, the monitoring unit 402 is configured to monitor various factors (such as voltage, current, energy, power, frequency and power factor, as discussed) related to the main electricity supply 401B. In an embodiment, the monitoring unit 402 includes at least one current transformer (not shown in the accompanied drawings). In particular, the monitoring unit 402 includes a number of current transformers corresponding to a number of phases of the main electricity supply 401B. For instance, for a single-phase supply 401B, the monitoring unit 402 may include a single current transformer, and similarly for a three-phase supply 401B, the monitoring unit 402 may include three number of current transformers. Current transformers are the current-sensing units of the power system. A current transformer provides a secondary current that is accurately proportional to the current flowing in its primary, while presenting a negligible load to the primary circuit. In the system 400, each of the current transformers of the monitoring unit 402 is electrically coupled with one of the phases of the main electricity supply 401B. Herein, each of the current transformers is configured to measure current in the corresponding phase coupled therewith. Further, the voltage may be measured along the current transformers using known methods. Therefrom, other electricity consumption parameters, i.e. energy (including active energy, apparent energy and reactive energy), power (including active power, apparent power and reactive power), frequency and power factor may be derived, techniques for which are well known in the art and thus have not been discussed herein for the brevity of the present disclosure. Specifically, in an embodiment, the electricity consumption parameters comprise one or more of voltage, current, energy, power, frequency and power factor for the corresponding phase of the main electricity supply 401B. Further, the monitoring unit 402 may include a timing circuit (not shown) which may be associated with each of the current transformers and is further configured to associate timestamps with the measurement (i.e. reading) of the electricity consumption parameters. Herein, the said timing circuit may fetch current time value from some internal clock or some NTP server using the Internet, or the like.
[0059] Also, as illustrated in FIG. 4, the system 400 includes a communication unit 404. As shown, the communication unit 404 is generally installed with the monitoring unit 402. For instance, if the monitoring unit 402 may be installed in the junction box, the communication unit 404 may also be installed in the same junction box, or in close proximity to the monitoring unit 402, for example to an outer body of the said junction box. In some examples, the monitoring unit 402 and the communication unit 404 may be formed as a unitary device. The communication unit 404 is configured to receive the measured electricity consumption parameters from the monitoring unit 402. Further, the communication unit 404 is configured to transmit the measured electricity consumption parameters (collected from the monitoring unit 402) as electricity consumption parameters data to a cloud server (such as, a cloud server 406 as described in the subsequent paragraphs). For the purposes of the present disclosure, the communication unit 404 may include a Wi-Fi controller to establish communication for data transmission. In one example, the communication unit 404 may passively collect a number (e.g., a predetermined number) of the measured electricity consumption parameters, and waits a period (e.g., a predetermined period of time) prior to transmitting the measured electricity consumption parameters as electricity consumption parameters data. In the present examples, the measured electricity consumption parameters transmitted by the communication unit 404 may also include timestamps identifying when the corresponding information was measured by the monitoring unit 402 or received thereby.
[0060] Further, as illustrated in FIG. 4, the system 400 includes a cloud server 406. The cloud server 406 is, generally, similar in functionality and configuration to the server 200 of FIG. 2; and hereinafter interchangeably, simply referred to as “server” without any limitations. The server 406 is generally configured to process the electricity consumption parameters data as determined by the energy monitoring device 402. Herein, the server 406 may be any computer or hardware on which the services that clients use reside. Services available on the server 406 are transmitted from the server software to the client software over communication lines in packets of data according to defined protocols. Generally, the term "server" means a discrete host computer in a network, and it provides services to other computers or devices, termed "clients". For purposes of the example embodiments described herein, the term “server” includes machines that can be essentially any interconnected computer systems. Herein, the server 406 has been described as a cloud-based server; however, in other examples, the server 406 may be a local server or a crowd-sourced server without any limitations. Further, it may be appreciated that the use of terms such as “server” is not meant to imply that any particular machine can only be performing host function, or that any particular machine cannot be acting as a client computing platform in any particular circumstance.
[0061] In some examples, the cloud server 406 may include a database (not shown). Such database may be similar in functionality and configuration to the database/memory 210 of FIG. 2. Herein, the term “database” may denote a data organization, a collection of data records, in which data may be organized in rows and columns in one or more tables. Typically, the database utilized for purposes of the present disclosure may be a relational database, as known in the art and thus not explained further in detail herein. In the system 400, the database may generally be configured to store records related to the energy consumption parameters as determined by the energy monitoring device 402. The database may further be configured to store records generated from processing of the electricity consumption parameters data by the server 406.
[0062] Referring to FIG. 5, illustrated is a depiction of an exemplary environment 500 in which a system (such as, the system 400) is implemented for managing multiple appliances 501A, 501B and 501C. In one example, the appliance 501A may be an air-conditioner or a geyser which typically consume 250 watts or higher power, and may be considered as a large appliance. Further, the appliance 501B may be a refrigerator or the like which typically consume between 100-250 watts of power, and may be considered as a medium appliance. Further, the appliance 501C may be a fan, an electric bulb, or the like which typically consume between less than 100 watts of power, and may be considered as a small appliance. As may be seen in the system 500 (which is similar to the system 400), input energy 502 is received by a power supply circuit, associated with the monitoring unit 402. Herein, the power supply circuit supplies the required electric power to the appliances 501A, 501B and 501C for their operations. In embodiments of the present disclosure, the monitoring unit 402 is meant for only considering data which may pertain to the large appliances and may filter any data related to the medium appliance and the small appliances. It may be appreciated that the monitoring unit 402 may apply a cut-off threshold (like 250 watts) and may consider datapoints only above the cut-off threshold to achieve the said purpose. Further, as illustrated in FIG. 5, the electricity consumption parameters data is transmitted by the communication unit 404 from the monitoring unit 402 to the cloud server 406 for further processing. In some embodiments of the present disclosure, the insights as may be provided by the cloud server 406 by processing the electricity consumption parameters data may be displayed on a user device 510 (similar to the user device 300 of FIG. 3).
[0063] Referring to FIG. 6, illustrated is a schematic block diagram of implementation of the monitoring unit 402, in accordance with one or more exemplary embodiments of the present disclosure. The monitoring unit 402 senses instantaneous voltage from a main electricity supply 602. Herein, the monitoring unit 402 includes one or more current transformers 604. As discussed, the number of the current transformers 604 depends on the number of phases of the main power supply. The current transformers 604 sense instantaneous current for the monitoring unit 402. The monitoring unit 402 includes a derivation unit 610 that uses the instantaneous voltage and instantaneous current signals to derive other parameters related to electrical energy, such as RMS voltage, RMS current, active power, apparent power, reactive power, line frequency, active energy, apparent energy, reactive energy, etc. One of the ways to derive active power is represented in FIG. 6. To derive active power, the derivation unit 610 may further include a high-pass filter 606 to filter the signal of instantaneous current. Further, filtered instantaneous current signal can be multiplied with instantaneous voltage to derive the instantaneous power. The derivation unit 610 may further include a low-pass filter 608 to filter the signal of instantaneous power and derive the active power. Herein, the high-pass filter 606 acts as an electronic filter that passes signals with a frequency higher than a certain cut-off frequency and attenuates signals with frequencies lower than the cut-off frequency, and the low-pass filter 608 is a filter that passes signals with a frequency lower than a selected cut-off frequency and attenuates signals with frequencies higher than the cut-off frequency. It will be appreciated by a person skilled in the art that the above described process steps (calculations) used by the derivation unit 610 could be used to calculate active power, and similar techniques (with possibly different circuit designs as compared to one shown in FIG. 6) may be used to calculate apparent power and reactive power independently, without any limitations. The monitoring unit 402 may further include a data processing unit 612 configured to process the determined measured electricity consumption parameters to provide electricity consumption parameters data. In some examples, the monitoring unit 402 may also include a memory unit 614 to store the electricity consumption parameters data. In particular, the data processing unit 612 (which would be a microcontroller, for example) communicates with the monitoring unit 402 through SPI protocol (or some other protocol without departing from the scope of the present disclosure) and then stores the electricity consumption parameters data in the memory unit 614 (which would be an external non-volatile RAM, for example) through the SPI port so that this telemetry data can be read later. Herein, the monitoring unit 402 is shown to be integrated with the communication unit 404 which transmits the electricity consumption parameters data to the server 406 (as shown).
[0064] Referring now to FIG. 7A, illustrated is an exemplary graphical representation 700A of a typical active power consumption cycle of an air-conditioner as an appliance. In particular, the exemplary graphical representation 700A provides a plot of active power (measured in watt) of the main electricity supply 401B (as plotted on the vertical axis) vs. time (as plotted on the horizontal axis). Herein, the given exemplary graphical representation 700A represents the electricity consumption parameters data, as may have been monitored by the monitoring unit 402. Further, referring to FIG. 7B, illustrated is an exemplary graphical representation 700B of a typical reactive power (measured in watt, as plotted on the vertical axis) vs. active power (measured in watt, as plotted on the horizontal axis) of the main electricity supply in a premise, like the main electricity supply 401B in the premise 401A. Herein, the given exemplary graphical representation 700B represents the electricity consumption parameters data, as may have been monitored by the monitoring unit 402.
[0065] In the system 400 of the present disclosure, the cloud server 406 is configured to select a dataset from the electricity consumption parameters data for a running time-period of a predefined duration. That is, the cloud server 406 may consider datasets of predefined duration for determining the usage and consumption pattern. In one or more embodiments, the system 400 implements an algorithm which is executed to trim the continuous stream of data being received (from the monitoring device 402) for the running time-period with the predefined duration of 15 minutes, for example. That is, the cloud server 406 would consider datasets with the electricity consumption parameters data for 15 minutes of data collection by the monitoring device 402. Such data trimming may be achieved by the monitoring device 402 and/or the communication unit 404 locally at the premise 401A, or at the cloud server 406 itself. Although, in the present example, the predefined duration of the running time-period has been considered to be 15 minutes, it shall be appreciated that it may be any other suitable time duration, such as 5 minutes or 30 minutes without any limitations. Further, the algorithm for data trimming for equal time intervals may be contemplated by a person skilled in the art and thus had not been explained herein for the brevity of the present disclosure.
[0066] In an embodiment, the cloud server 406 is further configured to filter datapoints in the electricity consumption parameters data with one or more of the electricity consumption parameters outside of corresponding normal usage range, before selecting the dataset. That is, the datapoints which may represent unusual power consumption are eliminated. The cloud server 406 may also be configured to run a sliding window of 120 minutes (or any other predefined time) for removing the outliers in the dataset. As may be understood, such datapoints in the electricity consumption parameters data with one or more of the electricity consumption parameters outside of corresponding normal usage range may be due to power surge or the like. It may be appreciated that such datapoints may provide false status of operation of the appliance 401C to be managed, and thus such datapoints may need to be filtered. In an embodiment, the cloud server 406 may be configured to remove all datapoints having delta power (change in power) of less than 100 watts. The cloud server 406 may further be configured to smoothen the signal, for example the signal in the exemplary graphical representation 700A. In some implementations, the cloud server 406 may further be configured to smoothen up stepwise increase and decrease in power into one delta power, for facilitating the analysis. In an example implementation, the valid range for filtering of data may have voltage in the range of 150-300 V, current in the range of 0-100 A, power factor in the range of 0-1 and frequency in the range of 45-55.
[0067] As discussed, the monitoring unit 402 may monitor the voltage, current, active power, frequency, power factor and total energy consumption. As may be easily contemplated by a person skilled in the art of electrical engineering, it is possible to calculate reactive power based on the active power and the power factor. Further, as may be seen from FIG. 7A, any heavy appliance typically has an active power usage pattern in the form of rectangular-waves, with peaks when the appliance is ON and troughs when the appliance is OFF. Therefore, it may be appreciated that by checking for such peaks in active power consumption in the main electricity supply, it would be possible to determine the status of operation of the heavy appliance connected to the said main electricity supply. Additionally, it may be understood that different types of appliances may have different patterns of power usage, specifically in relation to active power usage and reactive power usage. For example, an appliance like an air-conditioner typically has high reactive power consumption, especially with cyclic ON and OFF of compressor therein; while an appliance like a geyser typically has very low reactive power consumption because of their inherent resistive load. Herein, the compressor of the air-conditioner may cycle between ON and OFF due to various reasons, such as set temperature and temperature condition inside the room, ambient temperature and so on. Further, as may be seen from FIG. 7B, the reactive power is generally close to 400 watts when the compressor is switched ON, for example. Therefore, it may be appreciated that by checking for such reactive power consumption values in the main electricity supply, it would be possible to determine the status of operation of the air-conditioner connected to the said main electricity supply. These conditions are exemplary only and it may be construed that various such patterns may be considered for determining the status of operation of any large appliance connected to the main electricity supply.
[0068] The cloud server 406 is further configured to analyse the selected dataset to determine instances of at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period. That is, in particular, the selected dataset is analysed to check for significant instances of changes in power consumption, which may be indicative of switching ON and OFF of a large appliance. It may be understood that the cloud server 406 may consider both positive and negative change for such analysis. Typically, for different types of large appliances and even with different models of the same type of large appliances with different power ratings, the absolute change in power consumption (be it active power or reactive power) would vary. By collecting some experimental data, it may be possible to determine thresholds for such absolute changes in power consumption for different types and models of the appliances 401C. Further, by determining if the absolute change is greater than the threshold, the event corresponding to such absolute change may be considered as an indicator of switching ON or OFF of the operation of the large appliance. It may be appreciated that, in some cases, there may be more than one threshold to cater to different types and models of the appliances 401C. The present system 400 may implement the delta active power threshold and the delta reactive power threshold (which are predefined based on said experimental data) for comparison with the absolute change in active power and the absolute change in reactive power. Herein, the delta active power threshold represents a value of absolute change in active power which may be indicative of switching ON or OFF of a large appliance primarily based on active power (such as a resistance-based geyser); and the delta reactive power threshold represents a value of absolute change in reactive power which may be indicative of switching ON or OFF of a large appliance primarily based on reactive power. In an example implementation, if datapoints are present with active power greater than 1000 watts, then the cloud server 406 may check for delta active power of less than and greater than 100 watts. If the condition satisfies, then a presence of a large appliance may be concluded. Herein, if the transition points have absolute value of delta reactive power of greater than 150 watts, then air-conditioner may be present, otherwise it may be assumed to be a geyser or a room heater, or the like. The cloud server 406 may further count the number of such transitions to determine compressor ON/OFF cycle of the air-conditioner. Multiple cases are considered for determining larger number of appliances 401C on a single line (or phase). In general, the cloud server 406 is configured to analyse trends in power consumption over the running time-period, and wherein the trends in power consumption are based on one or more of reactance load, inductance load and resistance load in the power consumption.
[0069] Further, the cloud server 406 is configured to identify the one or more appliances 401C and determine operational parameters of the identified one or more appliances 401C based on the analysis. That is, as discussed, by checking for absolute change in power consumption, it is possible to determine which type of appliance may be connected to the main electricity supply 401B. As mentioned earlier, the main electricity supply 401B may be a three-phase supply line, with one or more appliances 401C connected to each of the three phases therein. And since the monitoring unit 402 has independent current transformers for each of the phases, it is possible to determine the type appliances 401C on each of the phases. Further, it may be appreciated that the electricity consumption parameters data collected over a period of time may be used to check for instances where one of the large appliances 401C which may have been operational while another one of the large appliances 401C may not be operational at that time, but may later be operational. Let’s consider an example of two air-conditioners installed on a single phase of the main electricity supply 401B. By finding one instance of power consumption where only one air-conditioner is operational at a given time and another instance of power consumption where both air-conditioners are operational, it may be deduced that the said phase has two air-conditioners installed thereon. In the example of air-conditioner, the determined operational parameters of the one or more appliances 401C comprise one or more of compressor cycle ON time, compressor cycle OFF time, fan cycle ON time, fan cycle OFF time and compressor average power consumption. In some implementations, decision-tree based classification may be used to determine such operational parameters.
[0070] In an embodiment, the cloud server 406 is further configured to count number of instances of the at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period, in the analysis of the selected dataset. Counting such instances may help to determine the said operational parameters of the one or more appliances 401C. For example, such count for active power may be an indicator of number of times a given air-conditioner on the corresponding line (or phase) may have switched ON or OFF. Therefrom, the listed operational parameters including, but not limited to, compressor cycle ON time, compressor cycle OFF time, fan cycle ON time, fan cycle OFF time and compressor average power consumption may be determined as would be contemplated by a person skilled in the art. In some examples, the present system 400 may also consider ambient conditions to help estimate such operational parameters of the appliances 401C. For example, by monitoring an ambient temperature and available operational details of an air-conditioner, it may be possible to estimate the fan cycle ON time and the fan cycle OFF time in relation to the compressor cycle ON time and the compressor cycle OFF time.
[0071] Further, the cloud server 406 is configured to determine one or more of diagnostic measures and prognostic measures to be implemented for the one or more appliances 401C based on the determined operational parameters thereof. As mentioned, in case the appliance 401C is an air-conditioner appliance, the determined operational parameters of the one or more appliances comprise one or more of compressor cycle ON time, compressor cycle OFF time, fan cycle ON time, fan cycle OFF time and compressor average power consumption. In an implementation, based on power consumption pattern of the air-conditioner appliance with respect to the ambient temperature, an efficiency of the air-conditioner appliance may be determined. Now, if it is found that the efficiency of the air-conditioner appliance has been decreasing or is significantly lower than comparative air-conditioner of generally similar specification, it may be concluded that there may be some issue with the given air-conditioner appliance, like gas leakage and/or low pressure of gas in the compressor of the given air-conditioner appliance. Such findings may further be substantiated by checking if the compressor cycle ON time (as determined) is relatively higher for the given conditions. Similarly, based on the fan cycle ON time, it may be possible to determine possible clogging of a filter in the given air-conditioner appliance. For instance, on a daily basis estimation of dust accumulation on the filter is calculated by using fan run time and dust pollution (PM10 average values) in the location of the given air-conditioner appliance (which can be fetched from the Internet). Herein, “Dust accumulation” (DA) is given by: DA = K x Fan_run_time x PM10, where ‘K’ is a constant factor based on experiment. Now, herein, if Dust Accumulation crosses pre-set value, then the cleaning is suggested to the user. Herein, the determined diagnostic measures and prognostic measures comprise one or more of gas leakage in the air-conditioner appliance based on the compressor average power consumption in relation to average outside temperature, clogging of the filter in the air-conditioner appliance based on the fan cycle ON time and the fan cycle OFF time in relation to available pollution level for location of the premise, and fault in motor and/or compressor in the air-conditioner appliance based on the compressor cycle ON time in relation to average outside temperature. Based on the above points, a predictive maintenance alarm may be generated according to one or more implementations of the present embodiment.
[0072] In an embodiment, the cloud server 406 is further configured to perform real-time analytics on the electricity consumption parameters data to identify issues with the main electricity supply 401B, including one or more of poor power factor, low voltage, high voltage, voltage fluctuation, high current and irregular frequency. As the electricity consumption parameters for the main electricity supply 401B for the premise 401A may have been measured, it is possible to determine if any of the said factors of the main electricity supply 401B may not be in order. Such determination could be made by comparing the measures values to corresponding standard values or standard ranges for the corresponding factor. In some examples, the system 400 may also interpolate the available data at the start of hour to predict the usage if there are some missing data points and produce the results of usage patterns at hour level, day level, month level and year level. In the present implementations, the system 400, after generating the results, may optimize the algorithm and store it in a fast access database so that it can be rendered easily on the app front-end. In some implementations, the present system 400, or specifically the cloud server 406 may be configured to implement machine learning for training and testing of data, and for algorithm improvement. Further, in some implementations, the present system 400 may further be used for controlling the appliances 401C in the premise 401A. For example, when it is determined that the air-conditioner is switched ON for a long period of time while the required cooling for the room may have been achieved, the cloud server 406 may send a command to a universal remote control (such as, IR based remote control) to switch OFF the air-conditioner. Further, in some implementations, the system 400 may be integrated with a smart prepaid meter for the premise 401A, and when available power units or a set number of power units may have been consumed, the system 400 may regulate the operation of the large appliances aggressively, to even switch OFF the large appliances for the due period.
[0073] In one or more embodiments, the system 400 may further include web and mobile apps which may provide the authentication of the users in the application and plotting energy consumption charts and patterns to the users for their corresponding devices. Such apps may also receive notification from the backend if there is any emergency like short circuit, high/low voltage notifications, low power factor etc.
[0074] Referring to FIGS. 8A-8C, illustrated are flowcharts listing steps involved in establishing telemetry and for data transmission for the communication unit 404 in the present system 400. It would be appreciated by a person skilled in the art that the listed steps are exemplary only, and may be performed in a different order to achieve the same result without departing from the spirit and the scope of the present disclosure. As illustrated in FIG. 8A, a process 800 for establishing telemetry starts at block 802. At block 804, the memory unit (such as the memory unit 614) is initialized. Subsequently, or substantially simultaneously, at block 806, the monitoring unit 402 is initialized. Subsequently, or substantially simultaneously, at block 808, the communication unit 404 is initialized. At block 810, the telemetry task is established which allows for in-situ collection of measurements or other data by the monitoring unit 402, its storage by the memory unit 614, and its automatic transmission to a cloud server (such as the cloud server 406) for monitoring. At block 812, the data transmission link is established by the communication unit 404 for allowing transmission of the electricity consumption parameters data to the cloud server 406. At block 814, the process 800 ends. As illustrated in FIG. 8B, a process 820 for performing the telemetry task starts at block 822. At block 824, it is checked whether the electricity consumption parameters data is available, for example by checking records in the memory unit 614. If NO, the process 820 re-checks for the electricity consumption parameters data until available. If YES, at block 826, the telemetry data, including one or more of voltage, current, energy, power, frequency and power factor, is updated. Further, at block 828, the updated data is stored in the memory unit 614, and the process 820 moves back to block 822 to start again. As illustrated in FIG. 8C, a process 830 for performing the data transmission task starts at block 832. At block 834, the electricity consumption parameters data as stored in the memory unit 614 is fetched by the communication unit 404. At block 836, the communication unit 404 transmits the data to the cloud server 406. In the present implementation, the communication unit 404 may utilize MQTT protocol which is a lightweight messaging protocol for small sensors and mobile devices, optimized for high-latency or unreliable networks. Thereafter, at block 838, the communication unit 404 may go in sleep mode for a predetermined period of time (e.g., 30 seconds) to allow the memory unit 614 to passively collect a number (e.g., a predetermined number) of the measured electricity consumption parameters, prior to transmitting the measured electricity consumption parameters collected in the memory unit 614 as electricity consumption parameters data to the cloud server 406, and then the process 830 may restart after the said predetermined period of time.
[0075] The present system 400 may implement an on-connect feature, such as whenever the monitoring unit 402 / the communication unit 404 (herein, referred to as “device” in combination) connects to the cloud server 406, it subscribes to topics to receive commands and configuration in real-time or near real-time. Then it also publishes its Device ID, model details, and current firmware version to a topic in a JSON message. On receiving that message, the cloud server 406 triggers a serverless API and then processes the request and finds out the best firmware version for the devices. If there is any update for the device, then this serverless API sends the URL of the firmware update to the device via the MQTT command topic subscribed by the device. It may be understood that each device has a virtual copy stored on a cloud NoSQL database; or else, a specific OTA firmware version is sent to a specific device, with those details shared in the virtual device settings. So, the serverless API uses this information and send OTA firmware link to the device through MQTT protocol as a command. The device receives the OTA command and URL to download the firmware using a secure OTA HTTP client using the HTTP Stream buffers. In some examples, the device may have multiple OTA partitions (e.g., by default there may be 2 partitions: OTA_1, OTA_2). The device downloads the firmware in chunks, and then copies it one of the OTA partitions which is not currently in use depending upon the OTA partition algorithm. After the copying is complete the boot partition is updated from using a special partition called the OTA_DATA partition, and then the device restarts. After the restart, the device boots using the new partition where the latest firmware has been downloaded.
[0076] In some implementations, the devices ship with a default Access Point (AP). In some examples the device may have a Wi-Fi reset button. When there are no Wi-Fi credentials stored in the device, or the Wi-Fi reset button is pressed for 5 seconds, the devices start in AP+STA mode (with ‘STA’ stands for station mode) and also start an HTTP server and Wi-Fi manager. The device allows to connect to its AP using other computing devices, selecting the default AP's SSID and entering the default password. After successfully connecting with the AP, the user may open a browser in the said connected computing device, and browse a default address (e.g., 192.168.1.1). A web page may open and ask for SSID and password of the Wi-Fi network to which the device needs to be connected. When details have been entered and the save button has been pressed, a REST HTTP request is sent to the HTTP server running on the device, storing the SSID and password in the flash and later uses it to connect to the Wi-Fi network. It may be understood that in AP mode, other Wi-Fi devices connect to the current device may help configure its Wi-Fi settings i.e. Local Wi-Fi router's SSID and password; in STA mode, the current device uses the SSID and password stored in its flash-memory to connect to the Wi-Fi router present at the installation facility; and in AP+STA mode, the current device works both as an Wi-Fi AP and Wi-Fi station.
[0077] Referring to FIG. 9, illustrated is a flowchart listing steps involved in a process 900 for authentication for establishing telemetry and for data transmission in the system 400. As illustrated, the process 900 starts at block 902. It may be appreciated that to communicate with the cloud server 406, the device needs to authorize itself. In some examples, the communication between the device and the cloud server 406 is end-to-end encrypted using latest encryption standards, such as Asymmetric Elliptic Curve cryptography AES-256 conforming to TLS-v1.2 standard transport layer encryption. AES-256, which has a key length of 256 bits, supports the largest bit size and is practically unbreakable by brute force based on current computing power, making it the strongest encryption standard. To implement this a public-private key pair is generated. Herein, the public key is provided to the cloud server 406 and the private key is provided to the device. At block 904, the private key is read by the communication unit 404. If NO, i.e. if the private key is not read by the communication unit 404, and the process 900 moves to block 906 to retry. If YES, at block 908, the device credentials are fetched and read. At block 910, the device tries to connect to a wireless communication network (through WI-FI, for example). If NO, i.e. if the device is not able to connect, the process 900 moves to block 906 to retry. If YES, at block 912, the device receives time from the Internet. To Authenticate itself, the device using inputs, such as private key and device credentials information, from block 914, creates a JSON Web Token (JWT) at block 916. This JWT has an expiry time and acts as the password to provide Authentication to the device. At block 918, the device starts MQTT client. Then, at block 920, the device sends a connection request using the MQTT client. Further, at block 924, the cloud server 406, specifically IoT core thereof, checks for device credentials using public key from block 922. If NO, i.e. if credentials fail to match, then at block 926, an error log is created. It may be understood that no two devices can communicate with the cloud server 406 using the same credentials at the same time. If YES, i.e. if credentials are matched successfully, then at block 928, the IOT core provides the authentication of the device to communicate with the cloud server 406 till the JWT expires. At block 930, the publishing task is initialized; and at block 932, the telemetry/state data is published. That is, after the authentication, the device can send telemetry and state data to the cloud server 406 and can receive the commands and configurations from the cloud server 406. At block 934, upon checking if it is found that the JWT has expired, the process 900 moves back to block 916 to fetch JWT. That is, after the JWT expires, the device has to go through the entire process again to get the authentication.
[0078] Referring to FIG. 10, illustrated is a flowchart listing steps involved in a process 1000 for data collection in the present system 400. As illustrated, the process 1000 starts at block 1002. As discussed in reference to FIGS. 8A-8C, the device implements telemetry data collection task which is used to accumulate the total energy consumed for a given period of time. In the process 1000 of FIG. 10, at block 1004, the MQTT client is initialized. At block 1006, the MQTT client connects to the cloud server 406. At block 1008, the data publishing task is initialized to move the process 1000 to block 1014. Simultaneously, at block 1010, the monitoring unit 402 is initialized. At block 1012, the data from the monitoring unit 402 is stored in memory unit 614 (such as, FRAM). At block 1014, the device has a data publishing task which collects the energy data from the memory unit 614 and the sensor data from the monitoring unit 402 to form a single message, and then send it to the cloud server 406 using the MQTT protocol, and other sensor data (telemetry data) directly from the monitoring unit 402, and then send it to the cloud server 406 using the MQTT protocol. The whole message is encrypted using the JWT so that the connection is secure. At block 1016, this data is received by IoT Core at backend of the cloud server 406. Then, at block 1018, it is routed to a message queue using a pull-based subscription. This message queue is a distributed message queue which guarantees that the message will be delivered to its destination at least once. It also acts as a message buffer in case the cloud server 406 is down or there is a sudden spike in load on the cloud server 406 and stores the data up to 7 days. From this Message Queue, at block 1020, there is a streaming pipeline which transforms the messages according to the Data Warehouse table and inserts it there. It also provides the facility to perform stream analytics on the streaming data using the SQL type commands in the real-time or near real-time. At block 1022, this data is stored in a partitioned table in a data Warehouse permanently so that it can be then queried later for generating reports and viewed later. Further, at block 1024, there are scheduled serverless API that generate reports periodically for all the devices. At block 1026, the serverless API stores the generated reports in a cloud-based NoSQL database. At block 1028, the generated reports are made accessible to be quickly viewed using web/mobile based APIs by a user 1030 (as depicted).
[0079] The present disclosure further provides a method for management of one or more appliances in a premise. FIG. 11 provides a flowchart 1100 listing steps involved in the said method for management of one or more appliances in a premise. Various embodiments and variants disclosed above, with respect to the aforementioned system 400, apply mutatis mutandis to the present method for management of one or more appliances in a premise as described hereinafter.
[0080] At step 1102, the method includes measuring electricity consumption parameters for each of phases in a main electricity supply for the premise. At step 1104, the method includes transmitting the measured electricity consumption parameters as electricity consumption parameters data. At step 1106, the method includes selecting a dataset from the electricity consumption parameters data for a running time-period of a predefined duration. At step 1108, the method includes analysing the selected dataset to determine instances of at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period. At step 1110, the method includes identifying the one or more appliances and determining operational parameters of the identified one or more appliances based on the analysis. At step 1112, the method includes determining one or more of diagnostic measures and prognostic measures to be implemented for the one or more appliances based on the determined operational parameters thereof.
[0081] In one or more embodiments, in the step of analysing the selected dataset, the method further includes counting number of instances of the at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period.
[0082] In one or more embodiments, the method further includes filtering datapoints in the electricity consumption parameters data with one or more of the electricity consumption parameters outside of corresponding normal usage range, before selecting the dataset.
[0083] In one or more embodiments, the electricity consumption parameters comprise one or more of voltage, current, energy, power, frequency and power factor for the corresponding phase of the main electricity supply, and wherein the method further comprises performing real-time analytics on the electricity consumption parameters data to identify issues with the main electricity supply, including one or more of poor power factor, low voltage, high voltage, voltage fluctuation, high current and irregular frequency.
[0084] The solutions proposed by the present system 400 and the method (as per the flowchart 1100) can be extended to any type of electrical appliances or a system of electrical appliances. It can be used in device diagnostics and prognostics. In case of a failure, the system will be self-sufficient in doing a preliminary diagnosis to identify the malfunctioning component/part. Prognostics can help in determining the health of the appliance, considering the wear and tear of the appliance over its life. Prognostics can help in ensuring timely service, reducing down time as well as increasing life of a device. Further, data collected by the present system can help in energy consumption forecasting for plurality of electrical/electronic appliances, and thereby help with energy savings.
[0085] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiment was chosen and described in order to best explain the principles of the present disclosure and its practical application, to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated.

Claims:WE CLAIM:

1. A system for management of one or more appliances in a premise, the system comprising:
a monitoring unit installed with a main electricity supply for the premise, the monitoring unit comprising a number of current transformers corresponding to a number of phases of the main electricity supply, with each of the current transformers electrically coupled with one of the phases of the main electricity supply, wherein the monitoring unit is configured to measure electricity consumption parameters for each of the phases in the main electricity supply for the premise using the current transformers;
a cloud server; and
a communication unit configured to receive the measured electricity consumption parameters from the monitoring unit and transmit the measured electricity consumption parameters as electricity consumption parameters data to the cloud server,
wherein the cloud server is configured to:
select a dataset from the electricity consumption parameters data for a running time-period of a predefined duration;
analyse the selected dataset to determine instances of at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period;
identify the one or more appliances and determine operational parameters of the identified one or more appliances based on the analysis; and
determine one or more of diagnostic measures and prognostic measures to be implemented for the one or more appliances based on the determined operational parameters thereof.

2. The system of claim 1, wherein the cloud server is further configured to count number of instances of the at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period, in the analysis of the selected dataset.

3. The system of claim 1, wherein the cloud server is further configured to filter datapoints in the electricity consumption parameters data with one or more of the electricity consumption parameters outside of corresponding normal usage range, before selecting the dataset.

4. The system of claim 1, wherein in case the appliance is an air-conditioner appliance:
the determined operational parameters of the one or more appliances comprise one or more of compressor cycle ON time, compressor cycle OFF time, fan cycle ON time, fan cycle OFF time and compressor average power consumption; and
the determined diagnostic measures and prognostic measures comprise one or more of gas leakage in the air-conditioner appliance based on the compressor average power consumption in relation to average outside temperature, clogging of filter in the air-conditioner appliance based on the fan cycle ON time and the fan cycle OFF time in relation to available pollution level for location of the premise, and fault in motor and/or compressor in the air-conditioner appliance based on the compressor cycle ON time in relation to average outside temperature.

5. The system of claim 1, wherein the electricity consumption parameters comprise one or more of voltage, current, energy, power, frequency and power factor for the corresponding phase of the main electricity supply.

6. The system of claim 5, wherein the cloud server is further configured to perform real-time analytics on the electricity consumption parameters data to identify issues with the main electricity supply, including one or more of poor power factor, low voltage, high voltage, voltage fluctuation, high current and irregular frequency.

7. A method for management of one or more appliances in a premise, the method comprising:
measuring electricity consumption parameters for each of phases in a main electricity supply for the premise;
transmitting the measured electricity consumption parameters as electricity consumption parameters data;
selecting a dataset from the electricity consumption parameters data for a running time-period of a predefined duration;
analysing the selected dataset to determine instances of at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period;
identifying the one or more appliances and determining operational parameters of the identified one or more appliances based on the analysis; and
determining one or more of diagnostic measures and prognostic measures to be implemented for the one or more appliances based on the determined operational parameters thereof.

8. The method of claim 7, wherein the step of analysing the selected dataset, further comprises counting number of instances of the at least one of absolute change in active power and absolute change in reactive power above corresponding predefined delta active power threshold and delta reactive power threshold, respectively, during the running time-period.

9. The method of claim 7 further comprising filtering datapoints in the electricity consumption parameters data with one or more of the electricity consumption parameters outside of corresponding normal usage range, before selecting the dataset.

10. The method of claim 7, wherein the electricity consumption parameters comprise one or more of voltage, current, energy, power, frequency and power factor for the corresponding phase of the main electricity supply, and wherein the method further comprises performing real-time analytics on the electricity consumption parameters data to identify issues with the main electricity supply, including one or more of poor power factor, low voltage, high voltage, voltage fluctuation, high current and irregular frequency.