DESC:FORM 2 THE PATENTS ACT, 1970 (39 of 1970) & THE PATENT RULES, 2003 COMPLETE SPECIFICATION (See Section 10 and Rule 13) Title of invention: METHOD AND SYSTEM FOR LOW SAMPLING RATE ELECTRICAL LOAD DISAGGREGATION Applicant: Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956 Having address: Nirmal Building, 9th Floor, Nariman Point, Mumbai 400021, Maharashtra, India The following specification particularly describes the invention and the manner in which it is to be performed. CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY The present application claims priority from Indian provisional patent application no. 201921022305, filed on June 05, 2019. The entire contents of the aforementioned application are incorporated herein by reference. TECHNICAL FIELD The disclosure herein generally relates to utility consumption, and, more particularly, to method and system for low sampling rate electrical load disaggregation. BACKGROUND In modern power systems, smart meter data analytics is crucial for various entities involved in energy management. Present power sector is witnessing demand-side deregulations across the world, coupled with the ongoing deployment of smart meters which provides energy consumption data at regular intervals. Electrical load disaggregation is one of the smart meter analytics techniques which provide information about the usage and consumption patterns of the individual loads using the aggregate power consumption measurements. It can be leveraged for a variety of functionalities and value additions in the emerging smart grid scenario. Many existing and emerging smart meters provide low granular aggregate power measurements at 15- or 30-minutes intervals. However, for these low sampling rates, disaggregation of loads is tedious due to unavailability of events and signatures of the constituent loads. Hence, there is a necessity for a technique to analyze this low sampled aggregated power data to provide valuable inputs and insights for different entities in the ecosystem like, consumers, aggregators, retailers and utilities. While disaggregation of loads using high power sampled data is studied extensively in literature, limited results are available for the low sampled case. Most of the recent methods for low sampled power data utilize the emerging concepts of Graph Signal Processing (GSP) for load disaggregation. One method is based on greedily solving the graph smoothness term for load identification from 1-minute sampled aggregate power data. Although the detection accuracy is good, the consumption estimates are poor as they are merely the average consumption of the load. Another method uses graph signal smoothness-based label propagation for load identification and actual power consumption estimation for 15 minutes aggregate power data. Both these methods use the assumption that the load signals are smooth with respect to the graph created using aggregate power measurements to carry out load disaggregation. Another method on the similar lines, utilizes matrix factorization and graph shift quadratic form constraint to carry out load disaggregation. However, this method estimates the individual load signals as piece-wise constant values instead of real values. Thus, conventional techniques lack determining disaggregated energy loads at low sampling frequency. SUMMARY Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a system for is provided. The system includes a processor, an Input/output (I/O) interface and a memory coupled to the processor is capable of executing programmed instructions stored in the processor in the memory to pre-process to receive a aggregated load data (X ¯_test) obtained from a utility meter among a plurality of utility meters at a plurality of regular time intervals. The aggregated load data comprises total energy consumed by a plurality of electrical loads connected to the corresponding utility meter and base loads which are low power loads. The system further receives a training data (X ¯_train), wherein the training data is generated from the plurality of utility meters. The aggregate load data (X ¯_test) and the training data (X ¯_train) is represented as a matrix, where a column vector represents the plurality of regular time intervals covered in a predefined time period and a row vector represents a total number of predefined time periods considered. Further, a disaggregated energy load at low sampling rate for the aggregated load data (X ¯_test) of each utility meter is determined using an energy disaggregation technique. This technique comprises obtaining, a total load consumption (X ¯) using the aggregated load data (X ¯_test) and the training data (X ¯_train). Further, a laplacian graph? (L?_m) is constructed for each electrical load (m) using the total load consumption (X ¯).Further, an optimization model is generated for estimating the dictionary coefficient (Z_m) of each electrical loads (m). The dictionary coefficient (?? (Z?_m?^test) of each electrical loads (m) is estimated using the laplacian graph? (L?_m), a dictionary (D_m) and a dictionary co-efficient ?? (Z?_m?^train), wherein, the dictionary (D_m) and the dictionary co-efficient ?? (Z?_m?^train) are obtained corresponding to the electrical load (m) using a pre-trained dictionary. The dictionary is pre-trained using the dictionary learning for every load using the load specific consumption (X_m) corresponding to the electrical load (m). The load consumption ((X_m ) ^) for each electrical load (m) among the plurality of electrical loads (M) is estimated using the estimated energy coefficient ?? (Z?_m?^test) with the corresponding dictionary (D_m). The system iteratively, performs for each electrical load (m+1) among the plurality of electrical loads (M) by, re-computing, a new aggregated load data (X ¯_test) by subtracting the estimated load energy consumption ((X_m ) ^) from the previous load consumption (X ¯_test) value and re-computing, the new total load consumption (X ¯) by subtracting the estimated load energy consumption ((X_m ) ^) and the load specific consumption (X_m) from the previous total load consumption ((X)) ¯. In another aspect, provides a method that includes a processor, an Input/output (I/O) interface and a memory coupled to the processor is capable of executing programmed instructions stored in the processor in the memory to pre-process receive a aggregated load data (X ¯_test) obtained from a utility meter among a plurality of utility meters at a plurality of regular time intervals. The aggregated load data comprises total energy consumed by a plurality of electrical loads connected to the corresponding utility meter and base loads which are low power loads. The method further receives a training data (X ¯_train), wherein the training data is generated from the plurality of utility meters. The aggregate load data (X ¯_test) and the training data (X ¯_train) is represented as a matrix, where a column vector represents the plurality of regular time intervals covered in a predefined time period and a row vector represents a total number of predefined time periods considered. Further, a disaggregated energy load at low sampling rate for the aggregated load data (X ¯_test) of each utility meter is determined using an energy disaggregation technique. This technique comprises obtaining, a total load consumption (X ¯) using the aggregated load data (X ¯_test) and the training data (X ¯_train). Further, a laplacian graph? (L?_m) is constructed for each electrical load (m) using the total load consumption (X ¯). Further, an optimization model is generated for estimating the dictionary coefficient (Z_m) of each electrical loads (m). The dictionary coefficient (?? Z?_m?^test) of each electrical loads (m) is estimated using the laplacian graph? (L?_m), a dictionary (D_m) and a dictionary co-efficient ?? (Z?_m?^train), wherein, the dictionary (D_m) and the dictionary co-efficient ?? (Z?_m?^train) are obtained corresponding to the electrical loads (m) using a pre-trained dictionary. The dictionary is pre-trained using the dictionary learning for every load using the load specific consumption (X_m) corresponding to the electrical load (m). The load consumption ((X_m ) ^) for each electrical load (m) among the plurality of electrical loads (M) is estimated using the estimated energy coefficient ?? (Z?_m?^test) with the corresponding dictionary (D_m). The method iteratively, performs for each electrical load (m+1) among the plurality of electrical loads (M) by, re-computing, a new aggregated load data (X ¯_test) by subtracting the estimated load energy consumption ((X_m ) ^) from the previous load consumption (X ¯_test) value and re-computing, the new total load consumption (X ¯) by subtracting the estimated load energy consumption ((X_m ) ^) and the load specific consumption (X_m) from the previous total load consumption ((X)) ¯. In yet another aspect, provides one or more non-transitory machine-readable information storage mediums comprising one or more instructions, which when executed by one or more hardware processors perform actions includes preprocessing receive a aggregated load data (X ¯_test) obtained from a utility meter among a plurality of utility meters at a plurality of regular time intervals. The aggregated load data comprises total energy consumed by a plurality of electrical loads connected to the corresponding utility meter and base loads which are low power loads. The system further receives a training data (X ¯_train), wherein the training data is generated from the plurality of utility meters. The aggregate load data (X ¯_test) and the training data (X ¯_train) is represented as a matrix, where a column vector represents the plurality of regular time intervals covered in a predefined time period and a row vector represents a total number of predefined time periods considered. Further, a disaggregated energy load at low sampling rate for the aggregated load data (X ¯_test) of each utility meter is determined using an energy disaggregation technique. This technique comprises obtaining, a total load consumption (X ¯) using the aggregated load data (X ¯_test) and the training data (X ¯_train). Further, a laplacian graph? (L?_m) is constructed for each electrical load (m) using the total load consumption (X ¯).Further, an optimization model is formulated for estimating the dictionary coefficient (?? Z?_m?^test) of each electrical loads (m). The dictionary coefficient (?? Z?_m?^test) of each electrical loads (m) is estimated using the laplacian graph? (L?_m), a dictionary (D_m) and a dictionary co-efficient ?? (Z?_m?^train), wherein, the dictionary (D_m) and the dictionary co-efficient ?? (Z?_m?^train) are obtained corresponding to the electrical load (m) using a pre-trained dictionary. The dictionary is pre-trained using the dictionary learning for every load using the load specific consumption (X_m) corresponding to the electrical load (m). The load consumption ((X_m ) ^) for each electrical load (m) among the plurality of electrical loads (M) is estimated using the estimated energy coefficient ?? (Z?_m?^test) with the corresponding dictionary (D_m). The system iteratively, performs for each electrical load (m+1) among the plurality of electrical loads (M) by, re-computing, a new aggregated load data (X ¯_test) by subtracting the estimated load energy consumption ((X_m ) ^) from the previous load consumption (X ¯_test) value and re-computing, the new total load consumption (X ¯) by subtracting the estimated load energy consumption ((X_m ) ^) and the load specific consumption (X_m) from the previous total load consumption ((X)) ¯. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles: FIG.1 illustrates an example environment implementing a system, alternatively referred as energy load disaggregation system, representing a method for disaggregation of electrical loads configured to each utility meter, in accordance with some embodiments of the present disclosure. FIG.2 illustrates a functional block diagram of the energy load disaggregation system of FIG.1, in accordance with some embodiments of the present disclosure. FIG. 3 is a flow diagram illustrating a method for disaggregation of energy loads using the system of FIG. 1 functionally described in FIG.2, in accordance with some embodiments of the present disclosure. FIG.4 is a high-level architecture for disaggregating energy loads using the system of FIG. 1, to calculate the energy usage of the loads or appliances connected to the corresponding utility meter in accordance with some embodiments of the present disclosure. FIG.5 is a graphical representation of the energy load disaggregation results for 1 day using 15 minutes sampled HES dataset, in accordance with some embodiments of the present disclosure. FIG. 6 is a graphical representation of the energy load disaggregation results for 1 day using 30 minutes sampled UMass dataset, in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims. The embodiments herein provide a method and a system, interchangeably referred as energy load disaggregation system for determining the disaggregated energy of the electrical load(s) or appliance (s) from the power data at low sampling rate. The electrical load may also be referred as aggregated power data. The aggregate power data is the total amount of energy load consumed by all the electrical appliances configured to the corresponding utility meter among a plurality of utility meters. The method disclosed determines energy consumed by all the individual appliance(s) of a house or a building from the aggregate power data using an energy disaggregation technique. The term ‘disaggregated energy load’ may refer to an amount of energy that may be consumed by a single or individual appliance configured to the corresponding utility meters available in the electrical network. Further, as used in the present disclosure, the term “load or appliance” may refer to any device that is capable of consuming electricity. For example, it may be a dish washer, refrigerator, dryer or any other device which consumes energy. In an embodiment to effectively address 15- or 30-minutes energy load disaggregation, and to obtain a good estimate of load consumption, the present disclosure presents an analytical formulation blending the sparse dictionary representation and graph signal smoothness based on label propagation for energy load disaggregation. The dictionaries learn individual load characteristics in terms of magnitude and time of operation. The coefficients select the right dictionary columns (atoms) and scale them appropriately to facilitate the discrimination of loads. The graph-signal based label propagation identifies the timings of load operation in a more accurate way though having small amount of training data and further refines the individual load estimation. This technique achieved an accuracy greater than or equal to 70% for the loads of interest. The disclosed technique performs better accuracy than the standard GSP and dictionary-based methods which are known in the art. This technique receives aggregate power data at predefined regular intervals for the duration of N days along with individual power consumed by the M appliances for n days at a low rate (T samples in a day). Referring now to the drawings, and more particularly to FIG. 1 through FIG.6, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method. FIG.1 illustrates an example environment implementing a system, alternatively referred as energy load disaggregation system, representing a method for disaggregation of electrical loads configured to each utility meter, in accordance with some embodiments of the present disclosure. As depicted, in the example herein, the system 102 is configured to the plurality of utility meters, where each utility meter is configured to all the energy consuming devices in a house or a building. The energy consuming devices may include a dish washer 104(1), refrigerator 104(2), dryer 104(3) or any other energy consuming appliances 104(M). Each utility meter may record the total energy consumed by all the connected appliances of the house or building and thereof. Further, the aggregate load data (X ¯_test) which is the total energy consumed and measured or indicated by the utility meter is fed to the system 102 or the energy disaggregation system 102. Further the system 102 analyses the aggregated load data (X ¯_test) to determine the disaggregated energy load at low sampling rate of each utility meter utilizing the training data (X ¯_train ) The system 102 is further explained in detail in conjunction with functional modules of FIG. 2 and flow diagram of FIG. 3 for determining disaggregated energy of the loads/appliances 104(1), 104(2), 104(3), ..…104(m), wherein m = 1,2,3…M may go incrementally till M, configured to the corresponding utility meter. FIG.2 illustrates a functional block diagram of the energy load disaggregation system of FIG.1, in accordance with some embodiments of the present disclosure. In an embodiment, the energy load disaggregation system 102 includes processor (s) 204, communication interface device(s), alternatively referred as or input/output (I/O) interface(s) 206, and one or more data storage devices or memory 208 operatively coupled to the processor (s) 204. The processor (s) 204 may be alternatively referred as one or more hardware processors 204. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) 204 is configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 102 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like. The I/O interface(s) 206 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server for verifying software code. The memory 208 may include any computer-readable medium 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, flash memories, hard disks, optical disks, and magnetic tapes. The memory 208 further may include modules 210. In an embodiment, the modules 210 include an energy load disaggregation module 212, for implementing functions of the system 102. In an embodiment, the modules 210 can be an Integrated Circuit (IC) (not shown), external to the memory 208, implemented using a Field-Programmable Gate Array (FPGA) or an Application-Specific Integrated Circuit (ASIC). The names (or expressions or terms) of the modules of functional block within the modules 210 referred herein, are used for explanation and are not construed to be limitation(s). Further, the memory 208 can also include the repository 214. The repository 214 may store the training data. The memory 208 may further comprise information pertaining to input(s)/output(s) of each step performed by the system 10 and methods of the present disclosure. The system 102 can be configured to process the aggregated load data (X ¯_test) input data, obtained from the utility meter among the plurality of utility meters at low sampling rate. In the sampled power measurements, since there are no events and signatures associated with loads operation, the disclosed technique makes use of dictionary representation and smoothness of the representation coefficients to disaggregate loads in an iterative manner. The method involves utilizing the training data obtained from the pre-trained module, where dedicated dictionaries are learnt for all the individual loads of interest. In the test phase, the aggregated load data (X ¯_test) is processed using the learnt load dictionary and graph smoothness as an additional constraint for solving the optimization model to estimate the constituent loads. Further, processing the aggregated load data (X ¯_test) in the test phase using the disclosed technique will be explained with reference to the accompanying diagrams FIG.3 and FIG.4. FIG. 3 is a flow diagram illustrating a method for disaggregation of electrical loads using the system of FIG.1 functionally described in FIG.2, in accordance with some embodiments of the present disclosure. The steps of the method 300 of the flow diagram will now be explained with reference to the components or blocks of the system 100 in conjunction with the example architecture of the system as depicted in FIG.4. Here, FIG.4 is a high-level architecture for disaggregating energy loads using the system of FIG. 1, to calculate the energy usage of the loads or appliances connected to the corresponding utility meter in accordance with some embodiments of the present disclosure. In an embodiment, the system 102 comprises one or more data storage devices or the memory 208 operatively coupled to the one or more processors 204 and is configured to store instructions for execution of steps of the method 300 by the one or more processors 204. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously. At step 302 of the method 300, the processor 204 is configured to receive a aggregated load data (X ¯_test) and a training data (X ¯_train). Referring now to FIG.1 and FIG.4 depicting an example, where the energy consumed appliances are configured to the corresponding utility meter. Electrical load disaggregation can be viewed as a source separation problem where, given an aggregated load data (X ¯_test), the individual loads that contributed to the aggregate consumption is computed. Mathematically, it can be represented as given in equation 1, p(i)= ?_(m=1)^M¦?Pm (i)+n(i)? ----------------------------- (1) where, p(i) is the aggregate power measurement sampled at time instants i = 1,2,3 ..., N. Pm (i) are the individual loads for m? M contributed to that measurement and n(i)is the measurement noise which includes the base loads (combined low power loads). In an embodiment, the system 102 is configured to receive the aggregated load data (X ¯_test) including the total amount of energy consumed by all the appliances configured to the utility meter. This aggregated load data (X ¯_test) is the input data obtained from the utility meter among the plurality of utility meters at a plurality of regular time intervals. Here, the aggregated load data (X ¯_test) comprises total energy consumed by a plurality of electrical loads connected to the corresponding utility meter and base loads which are low power loads. Subsequently, the system 102 fetches the training data (X ¯_train) obtained from the plurality of utility meters, wherein the system 102 is pre-trained with the training data (X ¯_train). At step 304 of the method 300, the processor 204 is configured to represent, the aggregate load data (X ¯_test) and the training data (X ¯_train) as a matrix, where column vector represents the plurality of regular time intervals covered in a predefined time period and row vector represents a total number of predefined time periods considered. The aggregate power measurements are represented as X ¯?R^(T*N) , where the rows T are the measurements in a day and the columns N are the total number of days considered for training data (X ¯_train) and (X ¯_test). Let the individual load power measurements for m= 1,2,3...M loads be given as X_m?R^(T*n) where n (n