System and method for monitoring energy consumption

Abstract

The present invention describes a system (200) and a method (300) for monitoring energy consumption in a facility System comprising one or more current measurement devices (202) to measure flow of current that flows across electrical circuit (210) comprising one or more electrical equipment (212) arranged in facility. Information processing apparatus (204) coupled to current measurement devices (202). Processing circuitry (206) configured to determine electrical energy consumption data associated with electrical equipment (212) in electrical circuit (210) and determine anomaly associated with the one or more electrical circuit (210) and the one or more electrical equipment (212) based on the electrical energy consumption data associated with the one or more electrical equipment (212).

Description

[0001] SYSTEM AND METHOD FOR MONITORING ENERGY CONSUMPTION

[0002] FIELD OF THE INVENTION

[0003] The present disclosure relates to an energy management system. More particularly, it is related to a method and system for monitoring energy consumption in a facility7.

[0004] BACKGROUND OF THE INVENTION

[0005] The following description of related art is intended to provide background information pertaining to the field of the present disclosure. This section may include certain aspects of the art that may be related to various aspects of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.

[0006] Energy conservation and management has become a need of today’s world where huge amount of energy consumption may be seen everywhere, be it a personal space, an industry set-up, commercial place like shopping mall or any organization like academics or workplace. With effective energy management, such huge consumptions and anomalies may be fine-tuned, and end costs may be optimized.

[0007] Many systems are implemented to monitor energy consumption at each of a small level like personal space or at a large level like an industry or shopping mall as per set benchmark values, however, such existing systems still lack a timely detection of issue or malfimctioning / anomaly at a device level in rea-time or at a desired time interval. Such existing systems also lack real-time insights into electrical aspects (consumption, suggestive measures etc.) in a particular set-up. Furthermore, such systems also fail in providing a real-time optimization of energy consumption especially at large scale.

[0008] SUMMARY OF THE INVENTION

[0009] Existing systems lack timely detection of issue or malfimctioning / anomaly at a device level in rea-time or at a desired time interval. Such existing systems also lack real-time insights into electrical aspects (consumption, suggestive measures etc.) in a particular set-up. Furthermore, such systems also fail in providing a real-time optimization of energy consumption especially at large scale.

[0010] The present invention relates to a system and method for monitoring energy consumption in a facility. It is an object of the present disclosure to mitigate, alleviate or eliminate one or more of the aboveidentified deficiencies and disadvantages in the prior art and solve at least the above-mentioned problem. According to a first aspect, there is provided a system for monitoring energy consumption in a facility. The system comprises one or more current measurement devices configured to measure flow of current that flows across one or more electrical circuit arranged in the facility, wherein each electrical circuit of the one or more electrical circuit comprising one or more electrical equipment. The system further comprises an information processing apparatus coupled to the one or more current measurement devices. The information processing apparatus comprises processing circuitry. The processing circuitry is configured to determine electrical energy consumption data associated with the one or more electrical equipment in the one or more electrical circuit. The processing circuitry is configured to determine an anomaly associated with the one or more electrical circuit and the one or more electrical equipment based on the electrical energy consumption data associated with the one or more electrical equipment. According to a second aspect there is provided a method for monitoring energy consumption in a facility. The method comprises measuring, by way of one or more current measurement devices, flow of current that flows across one or more electrical circuit. Each electrical circuit of the one or more electrical circuit comprising one or more electrical equipment. The method further comprises determining, by way of processing circuitry of an information processing apparatus coupled to the one or more current measurement devices, electrical energy consumption data associated with the one or more electrical equipment in the one or more electrical circuit. The method further comprises determining, by way of the processing circuitry, an anomaly associated with the one or more electrical circuit and the one or more electrical equipment based on the consumption of the electrical energy by the one or more electrical equipment.

[0011] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

[0012] BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS

[0013] The foregoing will be apparent from the following more particular description of the example embodiments, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the example embodiments.

[0014] FIG. 1 illustrates a network implementation of a system 200 for monitoring energy consumption in a facility, according to some embodiments of the invention; FIG. 2 illustrates a block diagram of the system 200 for monitoring energy consumption in the facility, according to some embodiments of the invention;

[0015] FIG. 3 illustrates a flow chart for a method for monitoring energy consumption in the facility through the system 200 as shown in FIG. 1, according to some embodiments of the invention

[0016] FIG. 4 illustrates an example working flow of the system 400 for monitoring energy consumption data in the facility, according to some embodiments of the invention;

[0017] FIG. 5 discloses an example working flow of the system 200 for monitoring energy consumption data in the facility, according to some embodiments of the invention;

[0018] FIG. 6 discloses an example energy report showing energy consumption data, according to some embodiments of the invention; and

[0019] FIG. 7 discloses an example computing environment, according to some embodiments of the invention.

[0020] Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may not have been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure.

[0021] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0022] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0023] The present invention provides a system and method for monitoring energy consumption in a facility.

[0024] FIG. 1 discloses a network implementation of a system 200 comprising a plurality of devices 100a- lOOn. A server (not shown in FIG.s) may be connected to the system 200. The server may be further connected to the plurality of communication devices lOOa-lOOn through the network 30. It should be understood that the server, the system 200, and the plurality of communication devices (lOOa-lOOn) corresponds to computing devices. It may be understood that the server may also be implemented in a variety of computing systems such as, a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a network server, a cloud-based computing environment, or a smart phone, and the like. It may be understood that the system may correspond to a variety of portable computing devices such as, a laptop computer, a desktop computer, a notebook, a smart phone, a tablet, a phablet and the like.

[0025] In an example implementation, the network 30 may be a wireless network, a wired network, or a combination thereof. The network 30 can be implemented as one of the different types of networks, such as intranet, Local Area Network, LAN, Wireless Personal Area Network, WPAN, Wireless Local Area Network, WLAN, wide area network, WAN, the Internet, and the like. The network 200 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, MQ Telemetry Transport, MQTT, Extensible Messaging and Presence Protocol, XMPP, Hypertext Transfer Protocol, HTTP, Transmission Control Protocol / Intemet Protocol, TCP / IP, Wireless Application Protocol, WAP, and the like, to communicate with one another. Further, the communication network 30 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

[0026] In accordance with the embodiments disclosed herein, the server is configured for establishing the communication between the system 200 and the plurality of communication devices lOOa-lOOn. For example, the server is configured for receiving feedback data from a plurality of sources from one or more users through the device 100 and transmit feedback analysis output to the system 200 via the network 30.

[0027] It may be understood that the server (local server / remote server / cloud server) may also be implemented in a variety of computing systems such as, a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a network server, a cloud-based computing environment, or a smart phone, and the like. It may be understood that the system 200 may correspond to a variety of portable device. Further, it may be understood that the system 200 may be, but not limited to, a power saving device.

[0028] In accordance with the embodiments disclosed herein, the server is configured for establishing the communication between the system 200 and the plurality of communication devices lOOa-lOOn. For example, the server is configured to receive electrical energy consumption data through the device 100a- lOOn.

[0029] FIG. 2 is an example block diagram of the system 200. The system 200 is configured to cause performance of the method 300 (as depicted in FIG. 3 later) for monitoring energy consumption in a facility (not shown in FIG.s). In an example, the facility may comprise and not limited to IT buildings, factories, commercial spaces, or anywhere energy monitoring is needed. The system 200 in FIG. 2 comprises one or more modules. The one or more modules may comprise one or more current measurement devices 202 and an information processing apparatus 204. The information processing apparatus 204 may comprise processing circuitry 206 and a memory unit (hereafter referred to as memory) 208. The system 200 may comprise electrical circuit 210. In an example, the electrical circuit 210 may be configured outside of the system 200 in the facility. The electrical circuit 210 comprise one or more electrical equipment 212 and communicatively coupled with the one or more measurement devices 202. Further, the memory 208, the processing circuitry 206, the one or more current measurement devices 202, and the electrical circuit 210 may be operatively connected to each other.

[0030] The memory 208 is arranged to store a plurality of instructions to be executed by the processing circuitry 206. The memory 208 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as Static Random-Access Memory, SRAM, and Dynamic Random-Access Memory, DRAM, and / or non-volatile memory, such as Read Only Memory, ROM, Erasable Programmable ROM, EPROM, Electrically Erasable and Programmable ROM, EEPROM, flash memories, hard disks, optical disks, and magnetic tapes. The processing circuitry 206 is configured to execute the plurality of modules. The memory 208 is configured for integrating the system 200 to the electrical circuit 210 in the facility.

[0031] A controlling circuitry (not shown in FIG.) may be adapted to control the steps as executed by the processing circuitry 206.

[0032] In an embodiment, the processing circuitry 206 is configured to determine electrical energy consumption data associated with the one or more electrical equipment 212 in the one or more electrical circuits 210. In an example, the electrical equipment 212 may comprise and not limited to light bulbs, ceiling fans, lamps, household appliances such as refrigerator, oven, washing machine, televisions, air conditioner, computers, industrial equipment such as motors, generators, transformers, circuit breakers, switches, etc. The processing circuitry 206 also determine an anomaly associated with the one or more electrical circuit 210 and the one or more electrical equipment 212 based on the electrical energy consumption data associated with the one or more electrical equipment 212. The anomaly may refer to any deviation from an expected or normal behavior of the electrical circuit. The deviations may indicate potential problems, malfunctions, or failures within an electrical system. In an example, anomaly in the electric circuit may comprise and not limited to unexpected voltage fluctuations, excessive current draw, overheating, ground faults, circuit breaker tripping, dimming or flickering lights, etc.

[0033] In an embodiment, the processing circuitry 206 generates an anomaly report based on the determination of the anomaly. In an example, the anomaly report may comprise at least one of an abnormal consumption pattern of the electrical energy by the one or more electrical equipment 212, reasons for the anomaly, and a plurality of recommendations for the one or more electrical equipment 212 and the one or more electrical circuit 210. In an example, the reasons for the anomaly may comprise and not limited to due to overheating, due to short circuits, open circuits due to broken wires, loose connections, component degradation such as, due to aging components (e.g. capacitors, batteries), due to power supply issues such as voltage fluctuation, due to environmental factors such as extreme temperature, humidity, etc. The plurality of recommendations may comprise and not limited to recommendation to replace at least one electrical equipment of the one or more electrical equipment 212 with corresponding another electrical equipment, recommendation for the facility to switch to a renewable energy source such as solar energy; and schemes for usage of the electrical energy for the facility. In an example, the recommendation for the facility to switch to the renewable energy source is based on load pattern. Further, the system 200 also suggest switching to an alternative energy source such as on-site solar and battery systems too.

[0034] In an embodiment, the abnormal consumption pattern of the electrical energy indicates one of, type of fault in the one or more electrical equipment 212 and time instance at which the fault occurred in the one or more electrical equipment 212.

[0035] In an embodiment, the processing circuitry 206 is configured to employ one or more Machine Learning (ML) models and one or more Artificial Intelligence (Al) techniques to determine the anomaly.

[0036] In an example, the processing circuitry 206 may provide actionable insights generated through the ML model. The ML model may suggest recommendation to the facility such as a building to switch to a renewable power generation / storage such as solar energy based on grid availability and non-critical load demand. The ML models may collect energy consumption data, enabling utility as well as buildings plan for different energy-related critical situations.

[0037] In an embodiment, the processing circuitry 206 may provide recommendations regarding a schedule of operation for battery, grid and renewable energy based on the load demand patterns and forecast, tariffs during the day, availability of renewable energy / solar, and charging capacity of the battery.

[0038] In an embodiment, the processing circuitry 206 is configured to determine the location of the anomaly in the one or more electrical circuit 210. In an example, an interface may be provided to display the location / zone or equipment where issues / anomaly occurs. The interface may show the recommendation to the users to fix the issues. In an example, a user may provide an input query such as “Which of my machines are performing inefficiently?” through the interface and the user may get the response to the input query such as “zone 1 machine 3, low PF due to phase imbalance”, “zone 2 machine 4, consuming excess due to ageing”. In another example, the user may provide the input query such as “What should be the schedule for my power sources today?” and the user may get response through the interface as shown in below table.

[0039] In an embodiment, the processing circuitry 206 is configured to predict load demand data based on the electrical energy consumption data associated with the one or more electrical equipment 212. The load demand data is predicted using the one or more Machine Learning (ML) models and the Artificial Intelligence (Al) techniques. The processing circuitry 206 may recommend a load plan based on the load demand data. The load plan comprises selection of, a type of energy supplier from one or more energy suppliers, wherein the selection is based on a capacity of selected energy supplier. In an example, the load plan may comprise recommendation for the facility to switch to a renewable energy supplier from the one or more energy suppliers, In an example, the energy supplier may comprise and not limited to solar energy supplier or wind energy supplier, battery, or grid, etc.

[0040] In an embodiment, each current measurement device of the one or more current measurement devices 202 comprises of a current clamp that is disposed in the one or more electrical circuit 210 to measure the flow of current that flows across the one or more electrical circuit 210.

[0041] In an embodiment, the system 200 may comprise a user device from the one or more user devices 300a- 300n. The user device is coupled to the information processing apparatus 202 and is configured to display the anomaly report.

[0042] In an example, the system 200 may send the energy consumption data to a cloud server, using machine learning models to analyze operational shifts and inefficiencies. The system 200 also detect energy losses (such as load and phase imbalances) and power quality issues (like harmonics and power factor problems) using the Al techniques.

[0043] In an example, the system 200 may connect to different onsite power sources such as solar, batteries, diesel generators (DG). The system 200 may monitor energy demands in the facility such as buildings using the Al techniques and decides how to use onsite power sources like solar, batteries, diesel generators (DG), and the main grid based on time, availability, tariffs, and peak demand.

[0044] In an example, the system 200 may be placed / installed behind an electrical meter, attached to any machine, zone, or even an entire building or industry, making it easy to install and manage.

[0045] In another embodiment, FIG. 3 shows a flow chart for a method 300 for monitoring energy consumption in the facility. The method 300 may be executed by the system 200 as discussed above. The order in which the steps of the method 300 is described is not intended to be construed as a limitation, and any number of the described method steps may be combined in any order to implement the method 300 or alternate methods. Additionally, individual steps may be deleted from the method 300 without departing from the scope of the invention as defined in the claims.

[0046] At step 302, the method 300 comprises measuring flow of current that flows across the one or more electrical circuit 210. Each electrical circuit of the one or more electrical circuit 210 comprising one or more electrical equipment 212. The measurement is performed by way of the one or more current measurement devices 202.

[0047] At step 304, the method 300 comprises determining, by way of the processing circuitry 206 of the information processing apparatus 204 coupled to the one or more current measurement devices 202, the electrical energy consumption data associated with the one or more electrical equipment 212 in the one or more electrical circuit 210.

[0048] At step 306, the method 300 comprises determining 306, by way of the processing circuitry 206, the anomaly associated with the one or more electrical circuit 210 and the one or more electrical equipment 212 based on the consumption of the electrical energy by the one or more electrical equipment 212.

[0049] The additional details of the method 300 are similar to the details of the system 200 and hence are not repeated for the sake of brevity.

[0050] In an exemplary embodiment, referring to FIG. 4, an example working flow of the system 400 for monitoring energy consumption data in the facility is shown. The system 400 is similar to the system 200 as discussed above in FIG. 2. As shown in FIG. 4, the system 400 may comprise at least one node 402, at least one gateway 404, one or more current measurement devices 202 (as shown in FIG. 2). In an example, the one or more current measurement devices 202 comprise one or more clip CT device 406. The clip CT device 406 may be hung around without involving any rewiring in the existing infrastructure. The gateway 404 may connect to the at least one node 402. The system 400 identifies any faults / anomalies at each of the electrical equipment 408 and generates alert for the fault / anomalies so that remedy action may be taken. The system 400 may analyze load patterns (based on a particular store benchmark) and suggest low-carbon (renewable) load shift through a nudge tool (not shown in FIG.s). In an example, a power unit comprising a two-pin power socket 410 may be provided to supply power to the system 400. The system 400 may comprise prongs for attaching the clip CT device 406 to the electrical equipment 408.

[0051] In an example, a separate system 400 may be required for each of the electrical equipment such as electrical appliance / energy meter / monitoring point. Once attached to the electrical appliance, the system 400 starts collecting data regarding the energy consumption by the electrical equipment 408. The data may then be transferred (through wired or wireless communication means) to the information processing apparatus 204 (as shown in FIG. 2) where the data is processed and analyzed. The data is then processed to check if the energy consumption is more than a benchmark value set for the electrical equipment 408 and to also check if there are faults or anomalies in functioning of the electrical equipment 408. The data is analyzed to also check any load imbalances, billing inefficiencies, energy usage inefficiencies and possible repairs (suggested by the system 400).

[0052] In an exemplary embodiment, referring to FIG. 5, an example working flow of the system 200 and 400 for monitoring energy consumption data in the facility 502. A nudge tool 506 may analyze the energy consumption data and scope 2 emissions, determining a required solar capacity for buildings to achieve a benchmark value set for the emissions. The scope 2 emissions are indirect Greenhouse gas (GHG) emissions associated with the purchase of electricity, steam, heat, or cooling. Although scope 2 emissions physically occur at the facility where they are generated, they are accounted for in an organization's GHG inventory because they are a result of the organization's energy use. The nudge tool 506 provides an Al-based schedule for using various onsite power sources such as solar, batteries, DGs, grid, etc,, considering peak demand, tariffs and availability. In an example, the system 200 analyzes individual asset demand / supply 504 and provides the recommendation accordingly. In an example, if a company plans to have onsite power (solar), the nudge tool 506 may suggest the capacity, depending on one or factors such as emissions target, demand patterns and critical equipment to operate.

[0053] In an exemplary embodiment, referring to FIG. 6, an example energy report is shown. The energy reports are generated to explain the reasons for abnormal energy usage, and schedules are provided to optimize equipment use based on demand and billing targets. The energy consumption data in the facility from multiple electrical equipment 212 is processed to generate graphs 602, 604 showing energy consumption data during a month and a day (this may also be customized). The date and time may be selected for generating the report. The report may be generated in the form of a bar graph as shown in FIG. 6 (the report may also be generated in any other illustrative form). The energy consumption data may be illustrated in one of an hourly forward active energy, and daily forward active energy. Further the energy report may show live power factor value 606 based on analysis of energy data captured by the system 200. The power factor is shown as very low and data anomalies are highlighted. The alert for getting the anomaly fixed through a technician is also generated. In an example, the system 200 may provide the energy consumption data in the form of report with priority order to a client to help them quickly understand and take relevant action on issues.

[0054] The proposed system 200 and the method 300 provide below listed advantages. The advantages listed are for general understanding for which intent is not to limit the scope of the invention:

[0055] The proposed system 200 and the method 300 monitors all the electrical aspects of an enclosed area on a minute -to-minute basis. In an example, the enclosed area comprises one of a personal space (like house etc.), an industrial space or a commercial place. The system 200 may be deployed as an integrated device in an existing electrical infrastructure of the enclosed area.

[0056] The proposed system 200 and the method 300 benchmark the energy needs and identify the anomalies on fingertips.

[0057] The proposed system 200 and the method 300 generate and share alerts when there is a possible repair coming up.

[0058] The proposed system 200 and the method 300 shows monthly energy consumption data in one snapshot.

[0059] The proposed system 200 and the method 300 compare the consumption in the facility / buildings to ideal benchmarks through energy performance index, as per the Building Energy Efficient Act (Japan) and identifies the buildings which are consistently consuming more energy than a required amount of energy.

[0060] FIG. 7 illustrates an example computing environment 700 implementing the system 200, 400 and method 300 as shown in FIGS. 2, 3 and 4 for monitoring energy consumption in the facility. As depicted in FIG. 7, the computing environment 700 comprises at least one data processor 706 that is equipped with a control module 702 and an Arithmetic Logic Unit, ALU 704, a plurality of networking devices 708 and a plurality Input output, I / O devices 710, a memory 712, a storage 714. The data processing module 706 may be responsible for implementing the system 200, 400 and the method 400 as shown in FIGS. 2 and 3 respectively. For example, the data processing unit 706 in some embodiments be equivalent to the controlling circuitry of the platform described above in conjunction with FIGS. 2, 4 and 3. The data processing unit 706 is capable of executing software instructions stored in memory 712. The data processing unit 706 receives commands from the control module 702 in order to perform its processing. Further, any logical and arithmetic operations involved in the execution of the instructions are computed with the help of the ALU 704.

[0061] The computer program is loadable into the data processing unit 706, which may, for example, be comprised in an electronic apparatus (such as the platform). When loaded into the data processing unit 706, the computer program may be stored in the memory 712 associated with or comprised in the data processing unit 706. According to some embodiments, the computer program may, when loaded into and run by the data processing module 706, cause execution of method steps according to, for example, any of the methods illustrated in FIGS. 2, 3 and 4, or otherwise described herein.

[0062] The overall computing environment 700 may be composed of multiple homogeneous and / or heterogeneous cores, multiple CPUs of different kinds, special media and other accelerators. Further, the plurality of data processing unit 706 may be located on a single chip or over multiple chips. The algorithm comprises instructions and codes required for the implementation are stored in either the memory 712 or the storage 714 or both. At the time of execution, the instructions may be fetched from the corresponding memory 712 and / or storage 714 and executed by the data processing unit 706.

[0063] In case of any hardware implementations various networking devices 708 or external I / O devices 710 may be connected to the computing environment to support the implementation through the networking devices 708 and the I / O devices 710.

[0064] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the elements. The elements shown in FIG. 7 include blocks which can be at least one of a hardware device, or a combination of hardware device and software module.

[0065] Although the present invention has been described in considerable detail with reference to certain preferred embodiments and examples thereof, other embodiments and equivalents are possible. Even though numerous characteristics and advantages of the present invention have been set forth in the foregoing description, together with functional and procedural details, the disclosure is illustrative only, and changes may be made in detail, especially in terms of the procedural steps within the principles of the invention to the full extent indicated by the broad general meaning of the terms. Thus, various modifications are possible of the presently disclosed system and process without deviating from the intended scope of the present invention.

Claims

AMENDED CLAIMS received by the International Bureau on 30 June 2025 (30.06.2025)We Claim:

1. A system (200) for monitoring energy consumption in a facility, the system (200) comprising: one or more current measurement devices (202) configured to measure flow of current that flows across one or more electrical circuit (210) arranged in the facility, wherein each electrical circuit of the one or more electrical circuit (210) comprising one or more electrical equipment (212); an information processing apparatus (204) coupled to the one or more current measurement devices (202), the information processing apparatus (204) comprising: processing circuitry (206) configured to: determine electrical energy consumption data associated with the one or more electrical equipment (212) in the one or more electrical circuit (210); determine an anomaly associated with the one or more electrical circuit (210) and the one or more electrical equipment (212) based on the electrical energy consumption data associated with the one or more electrical equipment (212), wherein the processing circuitry (206) is configured to employ one or more Machine Learning (ML) techniques and one or more Artificial Intelligence (Al) techniques to determine the anomaly; and generate an anomaly report comprising a plurality of recommendations for the one or more electrical equipment (212) and the one or more electrical circuit (210), wherein the plurality of recommendations comprising recommendation to replace at least one electrical equipment of the one or more electrical equipment with corresponding another electrical equipment, recommendation for the facility to switch to a renewable energy source, and schemes for usage of the electrical energy for the facility.

2. The system (200) as claimed in claim 1, wherein the anomaly report further comprising one of, an abnormal consumption pattern of the electrical energy by the one or more electrical equipment (212), and reasons for the anomaly.

3. The system (200) as claimed in claim 1, wherein the processing circuitry (206) is configured to determine a location of the anomaly in the one or more electrical circuit (210).

4. The system (200) as claimed in claim 1, wherein the abnormal consumption pattern of the electrical energy indicates one of, type of fault in the one or more electrical equipment (212) and time instance at which the fault occurred in the one or more electrical equipment (212).

5. The system (200) as claimed in claim 1, wherein the processing circuitry (206) is configured to: predict load demand data based on the electrical energy consumption data associated with the one or more electrical equipment (212), wherein the load demand data is predicted using one or more Machine Learning (ML) and Artificial Intelligence (Al) techniques; and recommend a load plan based on the load demand data, wherein the load plan comprises selection of, a type of energy supplier from one or more energy suppliers, wherein the selection is based on a capacity of selected energy supplier.

6. The system (200) as claimed in claim 5, wherein recommend the load plan comprising: recommendation for the facility to switch to a renewable energy supplier from the one or more energy suppliers.

7. The system (200) as claimed in claim 1, wherein each current measurement device of the one or more current measurement devices (202) comprising a current clamp that is disposed in the one or more electrical circuit (210) to measure the flow of current that flows across the one or more electrical circuit (210).

8. The system (200) as claimed in claim 1, comprising a user device coupled to the information processing apparatus (204), and configured to display the anomaly report.

9. A method (300) for monitoring energy consumption, the method (300) comprising: measuring (302), by way of one or more current measurement devices (202), flow of current that flows across one or more electrical circuit (210), wherein each electrical circuit of the one or more electrical circuit (210) comprising one or more electrical equipment (212); determining (304), by way of processing circuitry (206) of an information processing apparatus (204) coupled to the one or more current measurement devices (202), electrical energy consumption data associated with the one or more electrical equipment (212) in the one or more electrical circuit (210); determining (306), by way of the processing circuitry (206), an anomaly associated with the one or more electrical circuit (210) and the one or more electrical equipment (212) based on the consumption of the electrical energy by the one or more electrical equipment (212), wherein the processing circuitry (206) is configured to employ one or more Machine Learning (ML) techniques and one or more Artificial Intelligence (Al) techniques to determine the anomaly; andgenerating, by way of the processing circuitry (206), an anomaly report comprising a plurality of recommendations for the one or more electrical equipment (212) and the one or more electrical circuit (210), wherein the plurality of recommendations comprising recommendation to replace at least one electrical equipment of the one or more electrical equipment with corresponding another electrical equipment, recommendation for the facility to switch to a renewable energy source, and schemes for usage of the electrical energy for the facility.

10. The method (300) as claimed in claim 9, wherein the anomaly report further comprising one of, an abnormal consumption pattern of the electrical energy by the one or more electrical equipment (212), and reasons for the anomaly.

11. The method (300) as claimed in claim 9, comprising: determining, by way of the processing circuitry (206), a location of the anomaly in the one or more electrical circuit (210).

12. The method (300) as claimed in claim 9, wherein the abnormal consumption pattern of the electrical energy indicates one of, type of fault in the one or more electrical equipment (212) and time instance at which the fault occurred in the one or more electrical equipment (212).

13. The method (300) as claimed in claim 9, further comprising: predicting, by way of the processing circuitry (206), load demand data based on the electrical energy consumption data associated with the one or more electrical equipment (212), wherein the load demand data is predicted using one or more Machine Learning (ML) and Artificial Intelligence (Al) techniques; and recommending, by way of the processing circuitry (206), a load plan based on the load demand data, wherein the load plan comprises selection of, a type of energy supplier from one or more energy suppliers, wherein the selection is based on a capacity of selected energy supplier.

14. The method (300) as claimed in claim 13, wherein recommending the load plan comprising recommending for the facility to switch to a renewable energy supplier from the one or more energy suppliers based on load pattern, wherein the recommending the load plan further comprising recommending for the facility to switch to an alternative energy source.

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