Description:TECHNICAL FIELD
[0001] The present disclosure relates, in general, to energy meters, and more specifically, relates to load monitoring by energy meter using appliance signature.

BACKGROUND
[0002] The conventional approach to energy disaggregation has traditionally involved employing individual load sensors or an external computing device directly affixed to the appliances. These devices communicate wirelessly with a central head-end system, enabling the monitoring and analysis of individual energy consumption patterns. Existing inventions known in the art are established by using a device which is outside the energy meter to perform load analysis, which then sends this electric usage tracking (EUT) data to a data concentrator unit or an energy meter using radio frequency (RF) communication. Typically, these devices require n-devices for capturing n-appliances.
[0003] Therefore, it is desired to overcome the drawbacks, shortcomings, and limitations associated with existing solutions, and develop a system that enables the precise categorization and differentiation of individual appliances or devices through detailed data analysis. The electricity meter serves a dual function as both a computing/signature-capturing device and an energy measurement device. This integrated system facilitates wireless communication to a central base computer or mobile devices, providing real-time usage statistics.
[0004] In the current market scenario, this method addresses the current market challenge of providing consumers and utilities with comprehensive insights into individual appliance load consumption, detailed patterns of maximum demand, and power factors, amidst the proliferation of numerous appliances within homes.

OBJECTS OF THE PRESENT DISCLOSURE
[0005] An object of the present disclosure relates, in general, to energy meters, and more specifically, to load monitoring by energy meter using appliance signature.
[0006] Another object of the present disclosure is to provide a system that enables the precise categorization and differentiation of individual appliances or devices through detailed data analysis.
[0007] Another object of the present disclosure is to provide a system that facilitates in-depth examination and understanding of the consumption patterns of appliances over a monthly timeframe, providing valuable insights for energy optimization.
[0008] Another object of the present disclosure is to provide a system that empowers users to schedule and trigger appliances at specific times, strategically reducing peak demand and optimizing overall energy consumption.
[0009] Another object of the present disclosure is to provide a system that identifies devices operating within suboptimal power factor ranges, allowing users to address and improve power efficiency.
[0010] Another object of the present disclosure is to provide a system that enables the early identification of faulty or worn-out devices, facilitating timely replacement and reducing the risk of energy inefficiencies or breakdowns.
[0011] Another object of the present disclosure is to provide a system that provides statistical data on energy consumption patterns, allowing for anomaly detection and proactive measures to maintain device health and efficiency.
[0012] Yet another object of the present disclosure is to provide a system that offers detailed information about appliances, including maximum demand (MD), average power factor, and kilowatt-hour (kWh) consumption, aiding users in making informed decisions for energy conservation and device management.

SUMMARY
[0013] The present disclosure relates in general, to energy meters, and more specifically, relates to load monitoring by energy meter using appliance signature. The main objective of the present disclosure is to overcome the drawbacks, limitations, and shortcomings of the existing system and solution, by providing a system and method for load monitoring of appliances without the need for external sensors, by utilizing the energy meter. The present disclosure involves storing appliance-specific consumption patterns in the memory of the energy meter and subsequently analyzing these load patterns through the utilization of appliance-specific signatures. In contrast to the prior art, where external devices located outside the energy meter perform load analysis and transmit electric usage tracking (EUT) data to a data concentrator unit or an energy meter through radio frequency (RF) communication, the proposed approach integrates the load monitoring process within the energy meter, streamlining and enhancing the efficiency of appliance-specific energy consumption analysis.
[0014] The present disclosure relates to a system for load monitoring, the system includes an energy meter configured to measure energy consumption data of one or more appliances operatively coupled to the energy meter. The energy meter is configured to incorporate a capturing mechanism to capture a set of EUT signatures derived from electrical parameters during the operation of corresponding appliances. The electrical parameters selected from voltage, current waveforms, power factor, harmonic content and any combination thereof. The energy meter stores the set of EUT signatures in a memory of the energy meter and disaggregates real-time energy based on the stored set of EUT signatures, facilitating a comprehensive breakdown of the energy consumption data of corresponding appliances. The memory stores disaggregated data resulting from real-time energy disaggregation, thereby establishing a repository for a set of reference data associated with the energy consumption data of the corresponding appliances. Thus, the system empowers users to schedule and trigger appliances at specific times, strategically reducing peak demand and optimizing overall energy consumption.
[0015] In an aspect, the energy meter is equipped with selectable sampling frequencies enabling precise measurement of higher harmonic orders for fine current and voltage waveform analysis, wherein the selectable sampling frequencies are selected from 2k, 4k, 8k and any combination thereof.
[0016] In another aspect, the energy meter, upon capturing the set of EUT signatures, is configured to segregate and store distinctive EUT load patterns of the corresponding appliances, the EUT load patterns pertain to minimum and maximum values of voltage, current, power, power factor, and transient events. Thus, the system facilitates in-depth examination and understanding of the consumption patterns of appliances over a monthly timeframe, providing valuable insights for energy optimization.
[0017] In another aspect, the energy meter is configured to analyze the EUT load patterns using the stored set of EUT signatures in any or a combination of automated mode and manual mode. The energy meter is operated in the automated mode by utilizing a digital signal processing (DSP) engine and machine learning engine to acquire the set of EUT signatures of the corresponding appliances. Further, the energy meter is operated in manual mode by allowing users to capture and store the set of EUT signatures of the corresponding appliances in the memory of the energy meter by activating the corresponding appliances during specific operational modes.
[0018] In another aspect, the capturing mechanism of the energy is configured to enable the segregation of one or more appliances through data analysis, facilitating precise categorization and differentiation based on the electrical parameters. Enable early identification of faulty or worn-out devices, thereby facilitating timely replacement and reducing the risk of energy inefficiencies or breakdowns. Provide statistical data on energy consumption patterns, allowing for anomaly detection and proactive measures to maintain device health and efficiency and provide detailed information on the corresponding appliances, pertaining to maximum demand (MD), average power factor, and kWh consumption, thereby aiding the users to make informed decisions for energy conservation and device management.
[0019] Moreover, the energy meter schedules an activation of the corresponding appliances at predetermined times to regulate maximum demand, thereby promoting energy efficiency and identifying the corresponding appliances operating within lower power factor ranges, allowing for precise identification and strategic measures to enhance overall energy consumption.
[0020] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0022] FIG. 1 illustrates an exemplary view of an energy meter for load monitoring using appliance signature, in accordance with an embodiment of the present disclosure.
[0023] FIG. 2 is a high-level flow diagram of load monitoring by energy meter using appliance signature, in accordance with an embodiment of the present disclosure.
[0024] FIG. 3 illustrates an exemplary flow chart of a method of load monitoring, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0025] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0026] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0027] The present disclosure relates, in general, to energy meters, and more specifically, relates to load monitoring by energy meter using appliance signature. The present disclosure relates to a system for load monitoring, the system includes an energy meter configured to measure energy consumption data of one or more appliances operatively coupled to the energy meter. The energy meter is configured to incorporate a capturing mechanism to capture a set of EUT signatures derived from electrical parameters during the operation of corresponding appliances. The electrical parameters selected from voltage, current waveforms, power factor, harmonic content and any combination thereof. The energy meter stores the set of EUT signatures in a memory of the energy meter and disaggregates real-time energy based on the stored set of EUT signatures, facilitating a comprehensive breakdown of the energy consumption data of corresponding appliances. The memory stores disaggregated data resulting from real-time energy disaggregation, thereby establishing a repository for a set of reference data associated with the energy consumption data of the corresponding appliances. The system empowers users to schedule and trigger appliances at specific times, strategically reducing peak demand and optimizing overall energy consumption.
[0028] In an aspect, the energy meter is equipped with selectable sampling frequencies enabling precise measurement of higher harmonic orders for fine current and voltage waveform analysis, wherein the selectable sampling frequencies are selected from 2k, 4k, 8k and any combination thereof.
[0029] In another aspect, the energy meter, upon capturing the set of EUT signatures, is configured to segregate and store distinctive EUT load patterns of the corresponding appliances, the EUT load patterns pertain to minimum and maximum values of voltage, current, power, power factor, and transient events. The system facilitates in-depth examination and understanding of the consumption patterns of appliances over a monthly timeframe, providing valuable insights for energy optimization.
[0030] In another aspect, the energy meter is configured to analyze the EUT load patterns using the stored set of EUT signatures in any or a combination of automated mode and manual mode. The energy meter is operated in the automated mode by utilizing a digital signal processing (DSP) engine and machine learning engine to acquire the set of EUT signatures of the corresponding appliances. Further, the energy meter is operated in manual mode by allowing users to capture and store the set of EUT signatures of the corresponding appliances in the memory of the energy meter by activating the corresponding appliances during specific operational modes.
[0031] In another aspect, the capturing mechanism of the energy is configured to enable the segregation of the one or more appliances through data analysis, facilitating precise categorization and differentiation based on electrical parameters. Enable early identification of faulty or worn-out devices, thereby facilitating timely replacement and reducing the risk of energy inefficiencies or breakdowns.
[0032] In addition, the energy meter schedules an activation of the corresponding appliances at predetermined times to regulate maximum demand, thereby promoting energy efficiency and identifying the corresponding appliances operating within lower power factor ranges, allowing for precise identification and strategic measures to enhance overall energy consumption.
[0033] Moreover, the energy meter provides statistical data on energy consumption patterns, allowing for anomaly detection and proactive measures to maintain device health and efficiency and provide detailed information on the corresponding appliances, pertaining to maximum demand (MD), average power factor, and kWh consumption, thereby aiding the users to make informed decisions for energy conservation and device management. The present disclosure can be described in enabling detail in the following examples, which may represent more than one embodiment of the present disclosure.
[0034] The advantages achieved by the system of the present disclosure can be clear from the embodiments provided herein. The system represents a comprehensive solution for energy management, offering precise categorization and differentiation of individual appliances through detailed data analysis. Providing a deep understanding of consumption patterns over a monthly timeframe, the system empowers users to strategically schedule and trigger appliances, optimizing energy consumption and reducing peak demand. Furthermore, it identifies devices operating with suboptimal power factor ranges, enabling users to enhance power efficiency. With capabilities for early detection of faulty or worn-out devices, the system facilitates timely replacements, minimizing the risk of energy inefficiencies or breakdowns. Additionally, it furnishes statistical data for anomaly detection, supporting proactive measures to maintain device health and efficiency. This system delivers detailed appliance information, including Maximum Demand (MD), average power factor, and kWh consumption, empowering users to make informed decisions for energy conservation and effective device management. The description of terms and features related to the present disclosure shall be clear from the embodiments that are illustrated and described; however, the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents of the embodiments are possible within the scope of the present disclosure. Additionally, the invention can include other embodiments that are within the scope of the claims but are not described in detail with respect to the following description.
[0035] FIG. 1 illustrates an exemplary view of an energy meter for load monitoring using appliance signature, in accordance with an embodiment of the present disclosure.
[0036] Referring to FIG. 1, a system 100 for load monitoring using appliance signature can include an energy meter 102 and one or more appliances (104-1 to 104-N (which are collectively referred to as appliances 104, herein)) operatively coupled to electricity meter 102 (also referred to as energy meter 102, herein) configured for energy and power measurement and recording, encompassing overall day and month-wise consumption data. Furthermore, the energy meter 102 incorporates a load survey functionality that captures and records appliance-wise consumption. The load survey data can be conveniently downloaded into a computing device e.g., mobile or computer systems for graphical representation. The present disclosure eliminates the need for external sensors by leveraging the existing connection of the energy meter 100 to a house, flat, or building. The energy meter 100 autonomously records and stores the consumption patterns of each appliance in its non-volatile memory. The load patterns are subsequently analyzed using appliance-specific signatures, which are acquired in an automated manner by the energy meter itself or manually input by the user, thereby streamlining appliance load monitoring in a comprehensive and efficient manner.
[0037] The disclosed electricity meter 102 possesses the capability to measure electrical quantities (also referred to as electrical parameters, herein) and record consumption data. The energy meter 102 is configured to measure energy consumption data of one or more appliances 104 operatively coupled to the energy meter 102. The energy meter 102 is configured to incorporate a capturing mechanism 106 to capture a set of EUT signatures derived from electrical parameters during the operation of corresponding appliances. During the operation of appliance 104, the energy meter 102 captures and stores appliance signatures derived from electrical parameters such as voltage, current waveforms, power factor, and harmonic content. Store the set of EUT signatures in the memory 108 of the energy meter. This storing of appliance signatures is a one-time activity.
[0038] Subsequently, the energy disaggregation process occurs in real time based on these appliance signatures, with the disaggregated data being stored for future reference. Disaggregate real-time energy based on the stored set of EUT signatures, facilitating a comprehensive breakdown of an energy consumption data of corresponding appliances. Thus, the present disclosure enables efficient and runtime energy disaggregation through the utilization of stored appliance signatures. The term “energy disaggregation” as used herein refers to providing detailed insights into how much energy each specific appliance is consuming, enabling users to better understand and manage their energy usage.
[0039] In an implementation, the energy meter 102 disclosed herein incorporates selectable sampling frequencies, including options such as 2k, 4k, 8k, and the like. Utilizing the enhanced sampling rates of 4k and 8k, the meter 102 is capable of accurately measuring higher harmonic orders, thereby facilitating the acquisition of finely detailed current and voltage waveforms. Upon initiation of a specific load, discernible alterations in voltage (V) and current (I) waveforms, as well as harmonic content, are introduced to the energy meter 102. These variations manifest in observable changes in power and power factor parameters, contributing to a distinctive and identifiable electrical signature associated with the operation of the specific load.
[0040] For example, when the appliance e.g., air conditioner starts, the energy meter 102 detects not only the overall increase in power consumption but also the specific changes in voltage and current waveforms and harmonic content unique to the air conditioner's operation. These changes collectively contribute to a recognizable electrical signature stored in the energy meter's memory, enabling it to identify and monitor the air conditioner's energy consumption patterns in real time.
[0041] In an embodiment, initially, the energy meter 102 lacks recognition of electrical usage traces (EUTs) due to the absence of specific signatures. The meter 102 necessitates observing an EUT's TURN_ON, RUN, and SHUT_DOWN events, collectively contributing to the formulation of a distinct EUT signature. Once captured, this signature enables the meter 102 to record and store EUT-specific load patterns, encompassing minimum and maximum values of voltage, current, power, power factor, and transient events in the meter memory. In a theoretical context, the energy meter demonstrates the capability to seamlessly store and differentiate multiple EUTs in real-time, providing an advanced and comprehensive approach to energy monitoring and disaggregation.
[0042] For example, the energy meter 102 lacks recognition of the specific electrical signature of an appliance e.g., dishwasher. The meter is unaware of the unique characteristics associated with the operation of the dishwasher. When the dishwasher is turned on (TURN_ON), runs its cleaning cycle (RUN), and is subsequently shut down (SHUT_DOWN), the energy meter diligently observes these key events. During this period, the meter captures and analyzes the electrical parameters, such as voltage, current, power factor, and harmonic content. The observed events contribute to the formulation of a distinct electrical usage trace (EUT) signature associated with the dishwasher's operation. This signature represents the unique electrical fingerprint generated by the dishwasher during its different operational phases.
[0043] Once the EUT signature is captured, the energy meter 102 can now record and store EUT-specific load patterns. These patterns include the minimum and maximum values of voltage, current, power, power factor, and any transient events associated with the dishwasher's energy consumption. Furthermore, the energy meter 102 displays its capability to seamlessly store and differentiate multiple EUTs in real-time. For example, if the dishwasher is running simultaneously with another appliance, like a microwave, the energy meter 102 can identify and monitor the unique energy consumption patterns of each appliance separately.
[0044] In an embodiment, the EUT signature capturing process is facilitated through two distinct methods. Firstly, the automated approach employs a Digital Signal Processing (DSP) engine and logical implementation within the meter 100, eliminating the need for human intervention. This method utilizes machine learning algorithms on energy patterns and data to acquire and store EUT signatures seamlessly. For example, the energy meter 102, equipped with the DSP engine and logical implementation, automatically captures the electrical signatures produced by the appliance e.g., refrigerator during its operation. The DSP engine processes the voltage and current waveforms, power factor, and harmonic content, utilizing machine learning algorithms to discern and store the unique signature associated with the refrigerator's energy consumption patterns. This process requires no direct intervention from the user.
[0045] Secondly, the manual approach involves the activation of a single EUT while accessing a signature control panel mode on the energy meter. During this mode, the user manually captures and stores the EUT signature in the meter's memory. The user accesses a signature control panel mode on the energy meter while the appliance e.g., refrigerator is running. In this mode, the energy meter allows the user to manually trigger the capture of the refrigerator's electrical signature. The user initiates the capture process, and the meter records and stores the unique signature associated with the refrigerator's energy consumption. The EUT signature capture process is a one-time activity, ensuring a comprehensive and user-friendly approach to signature acquisition. During the EUT signature capture process, only one appliance EUT should be activated. This process is one time activity per EUT.
[0046] Thus, the present invention overcomes the drawbacks, shortcomings, and limitations associated with existing solutions, and provides a system that represents a comprehensive solution for energy management, offering precise categorization and differentiation of individual appliances through detailed data analysis. Providing a deep understanding of consumption patterns over a monthly timeframe, the system empowers users to strategically schedule and trigger appliances, optimizing energy consumption and reducing peak demand. Furthermore, it identifies devices operating with suboptimal power factor ranges, enabling users to enhance power efficiency. With capabilities for early detection of faulty or worn-out devices, the system facilitates timely replacements, minimizing the risk of energy inefficiencies or breakdowns. Additionally, it furnishes statistical data for anomaly detection, supporting proactive measures to maintain device health and efficiency. This holistic system delivers detailed appliance information, including Maximum Demand (MD), average power factor, and kWh consumption, empowering users to make informed decisions for energy conservation and effective device management.
[0047] FIG. 2 is a high-level flow diagram 200 of load monitoring by energy meter using appliance signature, in accordance with an embodiment of the present disclosure.
[0048] The method 202 begins by determining the availability of electric usage tracking (EUT) signatures within the energy meter.
[0049] At block 204, in the event that the EUT signature is not present, the process involves capturing the EUT signature and subsequently storing it in the memory of the energy meter.
[0050] At block 206, conversely, if the EUT signature is already available, the method proceeds to accumulate EUT-wise energy data onto the memory of the energy meter. This proposed approach ensures a dynamic and efficient management of appliance-specific energy consumption patterns within the energy meter.
[0051] FIG. 3 illustrates an exemplary flow chart of a method of load monitoring, in accordance with an embodiment of the present disclosure.
[0052] The method 300 includes at block 302, the energy meter configured to measure energy consumption data of one or more appliances coupled to the energy meter.
[0053] At block 304, the energy meter is configured to incorporate a capturing mechanism to capture a set of EUT signatures derived from electrical parameters during the operation of corresponding appliances.
[0054] At block 306, the energy meter can store the set of EUT signatures in the memory of the energy meter and at block 308, disaggregate real-time energy based on the stored set of EUT signatures, facilitating a comprehensive breakdown of energy consumption data of corresponding appliances.
[0055] It will be apparent to those skilled in the art that the system 100 of the disclosure may be provided using some or all of the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT INVENTION
[0056] The present disclosure provides a system that enables the precise categorization and differentiation of individual appliances or devices through detailed data analysis.
[0057] The present disclosure provides a system that facilitates in-depth examination and understanding of the consumption patterns of appliances over a monthly timeframe, providing valuable insights for energy optimization.
[0058] The present disclosure provides a system that empowers users to schedule and trigger appliances at specific times, strategically reducing peak demand and optimizing overall energy consumption.
[0059] The present disclosure provides a system that identifies devices operating within suboptimal power factor ranges, allowing users to address and improve power efficiency.
[0060] The present disclosure provides a system that enables the early identification of faulty or worn-out devices, facilitating timely replacement and reducing the risk of energy inefficiencies or breakdowns.
[0061] The present disclosure provides a system that provides statistical data on energy consumption patterns, allowing for anomaly detection and proactive measures to maintain device health and efficiency.
[0062] The present disclosure provides a system that offers detailed information about appliances, including maximum demand (MD), average power factor, and kWh consumption, aiding users in making informed decisions for energy conservation and device management.

, Claims:1. A system (100) for load monitoring, the system comprising:
an energy meter (102) configured to measure energy consumption data of one or more appliances (104-1 to 104-N) coupled to the energy meter, wherein the energy meter is configured to:
incorporate a capturing mechanism (106) to capture a set of electrical usage traces (EUT) signatures derived from electrical parameters during operation of corresponding appliances;
store the set of EUT signatures in a memory (108) of the energy meter; and
disaggregate real-time energy based on the stored set of EUT signatures, facilitating a comprehensive breakdown of energy consumption data of corresponding appliances.
2. The system as claimed in claim 1, wherein the electrical parameters are selected from voltage, current waveforms, power factor, harmonic content and any combination thereof.
3. The system as claimed in claim 1, wherein the energy meter (102) is equipped with selectable sampling frequencies enabling precise measurement of higher harmonic orders for fine current and voltage waveform analysis, wherein the selectable sampling frequencies are 2 k, 4 k, 8 k and any combination thereof.
4. The system as claimed in claim 1, wherein the energy meter (102), upon capturing the set of EUT signatures, is configured to segregate and store distinctive EUT load patterns of the corresponding appliances, the EUT load patterns pertain to minimum and maximum values of voltage, current, power, power factor, and transient events.
5. The system as claimed in claim 1, the energy meter (102) configured to analyze the EUT load patterns using the stored set of EUT signatures in any or a combination of automated mode and manual mode.
6. The system as claimed in claim 5, wherein the energy meter is operated in the automated mode by utilizing a digital signal processing (DSP) engine and machine learning engine to acquire the set of EUT signatures of the corresponding appliances.
7. The system as claimed in claim 5, wherein the energy meter is operated in the manual mode by allowing users to capture and store the set of EUT signatures of the corresponding appliances in the memory of the energy meter by activating the corresponding appliances during specific operational modes.
8. The system as claimed in claim 1, wherein the capturing mechanism (106) of the energy meter is configured to:
enable segregation of the one or more appliances through data analysis, facilitating precise categorization and differentiation based on the electrical parameters;
schedule an activation of the corresponding appliances at predetermined times to regulate maximum demand, thereby promoting energy efficiency;
identify the corresponding appliances operating within lower power factor ranges, allowing for precise identification and strategic measures to enhance overall energy consumption;
enable early identification of faulty or worn-out devices, thereby facilitating timely replacement and reducing the risk of energy inefficiencies or breakdowns;
provide statistical data on energy consumption patterns, allowing for anomaly detection and proactive measures to maintain health and efficiency of the corresponding appliances; and
provide detailed information on the corresponding appliances, pertaining to maximum demand (MD), average power factor, and kWh consumption, thereby aiding the users to make informed decisions for energy conservation and management.
9. The system as claimed in claim 1, wherein the memory stores disaggregated data resulting from real-time energy disaggregation, thereby establishing a repository for a set of reference data associated with the energy consumption data of the corresponding appliances.
10. A method (300) for load monitoring, the method comprising:
measuring (302) energy consumption data of one or more appliances coupled to a energy meter;
capturing (304) a set of electrical usage traces (EUT) signatures derived from electrical parameters during the operation of corresponding appliances, wherein the energy meter incorporates a capturing mechanism to capture the set of EUTs;
storing (306) the captured set of EUT signatures in a memory of the energy meter; and
disaggregating (308) real-time energy based on the stored set of EUT signatures, thereby facilitating a comprehensive breakdown of an energy consumption data for the corresponding appliances.