FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION (See section 10, rule 13)
1. Title of the Invention:
“AN ENERGY OPTIMIZATION SYSTEM AND A METHOD FOR OPTIMIZING ENERGY IN ONE OR MORE
APPLIANCES”
2. APPLICANT (S) -
(a) Name : Zensar Technologies Limited
(b) Nationality : India
(c)Address : Plot#4 Zensar Knowledge Park, MIDC, Kharadi,
Off Nagar Road, Pune, Maharashtra - 411014, India
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
The present disclosure relates in general energy management system. More particularly, but not exclusively, the present disclosure discloses a method and system to learn about energy consumption of electronic appliances and optimize the energy consumption based on the learning.
BACKGROUND
Home and industry automation are consistently gaining importance throughout the world. It gives luxury to users to remotely monitor and operate electronic appliances in the home and industry as per their convenience. On other hand, there are many regions in the world which are still away from the reach of electricity. Unlike other energy sources which are generated based on renewal energy, source of the electricity generation is non-renewal in nature. With an increasing urbanization, demand and usage of the electricity is increasing day by day. Considering the non-renewable nature of their source of generation and increasing demand, it becomes utmost important to judiciously use the electricity.
Though the home and the industry automation provide remote monitoring and control, however it still requires user’s instructions for such monitoring and control. With a busy routine, many times it happens that the user might overlook extra energy usage or may not be able to give timely instructions, which ultimately results in wastage of the electricity. Thus, it becomes a challenge to provide timely instructions to the electronic appliances in the home or the industry automation to prevent any electricity wastage. For providing the timely instructions, another challenge is to understand usage behavior of the users while operating the electronic appliances, and thereafter learn from the usage behavior.
SUMMARY
Accordingly, the present disclosure relates to a method for optimizing energy consumption of one or more electronic appliances. The method comprises a step of receiving current appliance data corresponding to the one or more electronic appliances from one or more sensors associated with the one or more electronic appliances at predefined intervals of time. The method further

comprises a step of analyzing the current appliance data in relative to optimized appliance data using a self-learning model. Based upon the analyzing, the method performs at least one of updating the self-learning model with the current appliance data and detecting an anomaly in the one or more electronic appliances. In one aspect, the aforementioned method for optimizing the energy consumption of the one or more electronic appliances may be performed by a processor using programmed instructions stored in a memory.
Further, the present disclosure relates to an energy optimization system for optimizing energy consumption of one or more electronic appliances. The energy optimization system comprises a processor and a memory communicatively coupled to the processor. The memory stores processor-executable instructions, which, on execution, causes the processor to perform one or more operations comprising receiving current appliance data corresponding to the one or more electronic appliances from one or more sensors associated with the one or more electronic appliances at predefined intervals of time. Further, the energy optimization system analyzes the current appliance data in relative to optimized appliance data using a self-learning model. Based upon the analyzing, the system performs at least one of updating the self-learning model with the current appliance data and detecting an anomaly in the one or more electronic appliances.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
BRIEF DESCRIPTION OF THE ACCOMPANYING 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. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:

FIG. 1 shows an exemplary environment illustrating an energy optimization system for optimizing energy consumption of one or more electronic appliances in accordance with some embodiments of the present disclosure;
FIG. 2 shows a detailed block diagram illustrating the energy optimization system in accordance with some embodiments of the present disclosure;
FIG. 3 shows a flowchart illustrating a method for optimizing energy consumption of one or more electronic appliances in accordance with some embodiments of the present disclosure;
FIG. 4 shows a flowchart illustrating a method for analyzing current appliance data in relative to optimized appliance data in accordance with some embodiments of the present disclosure; and
FIG. 5 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION
In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in

detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure.
The terms “comprises”, “comprising”, “includes”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.
The present disclosure relates to a method and an energy optimization system (alternatively also referred as “system”) for optimizing energy consumption of one or more electronic appliances. Although, the method for optimizing energy consumption is described in conjunction with a server, the said method can also be implemented in various computing systems/devices, other than the server. In today’s environment, considering the scarcity of electricity on one hand, and rising demand of the electricity on other hand, saving or optimizing energy consumption has become crucial and needs serious attention. Such optimization not only prevents wastage of the electricity, but also helps in reducing electricity bills.
Now a days, with the advancement of technology, the use of home and industry automation is gaining popularity. The system disclosed in the present disclosure leverages the use of the automation technology in any establishment, for example, but not limited to, home and industry, in collaboration with user assistance technology (for example, voice assistance, virtual assistance, visual assistance, or any other type of assistance which is capable of interacting with users) for optimizing the energy consumption of the electronic appliances. The objective is not only to provide convenience to the users while using various electronic appliances, but also to learn about usage pattern of the electronic appliances for the users. The learning helps the system understand and differentiate the usage pattern from one user to another user in order to provide personalized service to the users. To learn about the usage pattern of the electronic appliances, the system, initially, collects historical or past appliance data which may comprise, for example, but not limited

to, how much energy was consumed, for how much duration the electronic appliances were used by the user, and at what operating parameters the electronic appliances were operated.
All the above-mentioned past appliance data may be collected for a predefined time-period, for example last one week, one month, one year and the like, which gives an insight about the usage of the electronic appliances. The system uses such insight for generating energy consumption trend for all the electronic appliances used by the user. The energy consumption trend may be understood as a relation of an amount of energy consumed, by the electronic appliances, between different time-intervals of the predefined time-period. The energy consumption trend also gives a starting point to the system to create its knowledge base, and further use such knowledge base for learning purpose. In other words, the system, by using machine learning technique, generates a self-learning model based on the energy consumption trend. The self-learning model further learns from the energy consumption trend and determines optimized appliance data for the electronic appliances which may include, but not limited to, how much energy is consumed by the electronic appliances if operated for a specific time period, duration pertaining to the usage of the electronic appliances by the user, and at what operating parameters the electronic appliances are normally operated.
In an embodiment, the self-learning model with the knowledge of how the user has normally used the electronic appliances helps in analyzing the current usage of the electronic appliances. For instance, when the system receives current appliance data, the system analyzes the current appliance data using the self-learning model. Here, it may be understood that, the current appliance data includes, but not limited to, how much energy is consumed in real-time, for how much duration the electronic appliances are currently being used by the user, and at what operating parameters the electronic appliances are currently being operated
The system checks whether the current appliance data is deviating from normal behavior learnt while determining the optimized appliance data. If the deviation is for a small period, then the system may understand that the current usage of the electronic appliances is going beyond the normal or genuine usage. Hence the system may conclude this situation as an anomaly for taking corrective actions. However, it may happen that the current appliance data may frequently or regularly deviate from the normal behavior. In such a situation, the system, by using the self-

learning model, understand that the usage pattern of the electronic appliances has been changed, and hence updates itself using the current appliance data. Instead of taking any corrective action, the system now modifies the existing optimized appliance data with new optimized appliance data based on the learning. The system also facilitates the user, via a user assisting device, to interrogate about the health of the electronic appliances in real-time by receiving a request/command from the user in a form of text, audio, visual or combination thereof. Based on the command, the system performs real-time analysis of the electronic appliances and respond to the user with results.
In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
FIG. 1 shows an exemplary environment illustrating an energy optimization system for optimizing energy consumption of one or more electronic appliances in accordance with some embodiments of the present disclosure.
The environment 100 includes an energy optimization system 102, electronic appliances 112, sensor 104 associated with the electronic appliances 112, a data aggregator 106 (optional), a user assisting device 108 for assisting user, operating devices 114 associated with the electronic appliances 112, and a network 110. According to an embodiment, the network 110 may be a wired or wireless communication network through which the system 102 is connected with the sensor 104, the data aggregator 106, and the user assisting device 108. The sensor 104 attached with the corresponding electronic appliances 112 (for example, but not limited to, air-conditioner, washing machine television, microwave oven, and fridge) are capable of sensing and capturing appliance data (past and current) such as energy consumption data, usage data and operating data. Here, the sensor 104 is a dedicated hardware device which is capable of sensing various characteristics associated with the electronic appliances 112. For example, the sensor 104 may comprise energy consumption sensor like an energy meter capable of sensing how much energy units are consumed

by the electronic appliances 112. In another example, the sensor 104 may comprise appliance’s usage sensor for sensing usage time and usage frequency of the electronic appliances 112. In another example, the sensor 104 may comprise appliances’ operating sensor for sensing operating parameters like operating mode and operating value of the electronic appliances 112.
Once the appliance data is captured, the sensor 104 transmits the appliance data to the data aggregator 106 for formatting the appliance data into useful format, which is further used by the system 102 for analysis. The data aggregator 106 is a dedicated hardware device for receiving and formatting the appliance data. However, according to an embodiment, the sensor 104 may directly send the appliance data to the system 102. In such scenario, the system 102 may itself format the appliance data into the useful format for analysis.
Initially, when the system 102 receives the sensed data i.e., the appliance data for a predefined time-period, the system 102 learns about usage of the electronic appliances 112 by the user. Such appliance data may be considered as past appliance data or historical appliance data. The system learns about the normal usage behavior of the electronic appliances by the user based on the sensed data received for the predefined time-interval. The normal usage behavior may include, but is not limited to, how the user normally uses the electronic appliances. The system 102 now becomes capable of analyzing current appliance data voluntarily or on receiving user request. The user can provide the request to the system 102 via the user assisting device 108. The user assisting device 108 may be a dedicated hardware device capable of receiving and responding to the user request in a text form, an audio form, a visual form or combination thereof. For example, the user assisting device 108 may be a voice assistance device which is capable of interacting with the user in voice mode. In another example, the user assisting device 108 may be virtual assistant which is capable of assisting the user in an audio-visual mode. Yet in another example, the user assisting device 108 may assist the user via textual mode, such as chat assistance.
In addition, the operating device 114, coupled with the electronic appliances 112, is a dedicated hardware such as a circuit for operating the electronic appliances. Here, the operating device 114 may be understood as a device which is operated upon receipt of instructions from the system 102 for taking any corrective actions. The corrective actions are taken when any anomaly

is detected in the electronic appliances 112. For example, on receiving the instructions from the system 102, the operating device 114 may switch ON or switch OFF the electronic appliances 112. In another example, on receiving the instructions from the system 102, the operating device 114 may change operating parameters such as temperature settings, operating mode, operating speed and the like of the electronic appliances 112. The analyzing and learning from the appliance data and responding to the user request is explained in detail in subsequent paragraphs of the specification.
FIG. 2 shows a detailed block diagram illustrating the energy optimization system in accordance with some embodiments of the present disclosure.
The energy optimization system 102 (alternatively also referred as “system”) comprises an I/O interface 202, a processor 204, a memory 206, and a self-learning model 232. The I/O interface 202 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 202 may allow the system 102 to interact with the user directly or through user devices. Further, the I/O interface 202 may enable the system 102 to communicate with other devices, such as the sensor 104 and the data aggregator 106. The memory 206 is communicatively coupled to the processor 204. The processor 204 is configured to perform one or more functions of the system 102 for optimizing energy consumption of the one or more electronic appliances 112. In one implementation, the system 102 comprises data 208 and modules 210 for performing various operations in accordance with the embodiments of the present disclosure. In an embodiment, the data 208 may include, without limitation, past appliance data 212, optimized appliance data 214, current appliance data 216, and other data 218.
In one embodiment, the data 208 may be stored within the memory 206 in the form of various data structures. Additionally, the aforementioned data 208 can be organized using data models, such as relational or hierarchical data models. The other data 218 may store data, including temporary data and temporary files, generated by the modules 210 for performing the various functions of the system 102. For example, other data 218 may be an energy consumption trend which may be understood as a graphical representation of an amount of energy consumed by the electronic appliances 112 at different time intervals of the predefined time-period.

In an embodiment, the past appliance data 212 may include past energy consumption data, past usage data, and past operating data associated with the one or more electronic appliances 112. The past energy consumption data may include past energy units corresponding to the one or more electronic appliances 112. For example, the air-conditioner has consumed 180 units in last one month, the fridge has consumed 40 units in last one week, and the washing machine has consumed 20 units in last 3 days. Similarly, the past usage data may include at least one of past usage time and past usage frequency corresponding to the one or more electronic appliances 112. For example, the usage time and the usage frequency of the air-conditioner for last one week was 240 hours and 60 times respectively. Similarly, the past operating data may include at least one of past operating mode and past operating value corresponding to the one or more electronic appliances 112. For example, the operating mode of the air-conditioner for last one month was normal mode – 60 times, medium mode – 90 times and high mode or jet cool mode – 10 times. Similarly, the operating value for the air-conditioner for last one was in range of 18°C to 28°C. Here, the “past appliance data” may be understood as historical data collected for a predefined time-period. For example, for last one day, last two days, last one week, last month, last one quarter, last one year, or any specific predefined time-period. As discussed earlier, the past appliance data 212 may be used by the self-learning model 232 for creating initial knowledge base for learning purpose. That is, the self-learning model 232 learns about the normal usage of the one or more electronic appliances 112 by the user and keeps updating itself at regular intervals of time.
Based on the learning, the self-learning model 232 determines optimized appliance data 214 which may include optimized energy consumption data, optimized usage data, and optimized operating data associated with the one or more electronic appliances 112. The optimized energy consumption data may include optimized energy units corresponding to the one or more electronic appliances 112. Similarly, the optimized usage data may include at least one of optimized usage time and optimized usage frequency corresponding to the one or more electronic appliances 112. Similarly, the optimized operating data may include at least one of optimized operating mode and optimized operating value corresponding to the one or more electronic appliances 112. Here, the term “optimized” may be understood as normal and genuine usage of the one or more electronic appliances 112 which the self-learning model 232 learns from the past appliance data 212. However, it must be also understood to a skilled person that, the learning and training of the self-

learning model 232 does not depend only on the past appliance data 212, but also depend on the current appliance data 216 and future appliance data as and when received. Based on the aforementioned data set, the self-learning model 232 enables the system 102 to learn itself as when any change in the data set is observed, According to an embodiment, the self-learning model 232 may be implemented, into the system 102, as a hardware element like a circuit or chip or a processor or a software or a combination of software and hardware which continuously learns from the past, current and future appliance data.
In an embodiment, the current appliance data 216 obtained in real time from one or more appliances may include current energy consumption data, current usage data, and current operating data associated with the one or more electronic appliances. The current energy consumption data may include current energy units corresponding to the one or more electronic appliances 112. Similarly, the current usage data may include at least one of current usage time and current usage frequency corresponding to the one or more electronic appliances 112. Similarly, the current operating data may include at least one of current operating mode and current operating value corresponding to the one or more electronic appliances 112. Here, the “current appliance data” may be understood as data collected in a real-time for which the analysis is done.
In an embodiment, the above discussed data 208 (past appliance data 212, optimized appliance data 214, and current appliance data 216) may be processed by one or more modules 210. In one implementation, the one or more modules 210 may also be stored as a part of the processor 204. In an example, the one or more modules 210 may be communicatively coupled to the processor 204 for performing one or more functions of the system 102.
In one implementation, the one or more modules 210 may include, without limitation, a receiving module 220, a determining module 222, a generating module 224, an analyzing module 226, a performing module 228, and other modules 230. As used herein, the term module refers to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. The other modules 230 may include programs or coded instructions that supplement

applications and functions of the system 102 for optimizing the energy consumption of the one or more electronic appliances 112.
Now, how the system 102 is implemented for optimizing the energy consumption of the one or more electronic appliances 112 is discussed herein detail. As the objective of the present disclosure is not only to optimize the energy consumption of the one or more electronic appliances 112, but also to learn from the usage behavior, the system 102 first requires training. When the system 102 is initially implemented, with no prior knowledge of how the one or more electronic appliances 112 are being used, the first task is to train the system 102 with the past appliance data 212. In the training phase, the receiving module 220 receives the past appliance data 212 comprising the past energy consumption data, the past usage data and the past operating data associated with the one or more electronic appliances 112. The receiving module 220 receives the past appliance data 212 directly from the sensor 104 which are configured to sense and capture the appliance data, or from the data aggregator 106. Both, the sensor 104 and the data aggregator 106 are already explained above with reference to figure 1. Once the past appliance data 212 is received, it is stored in the memory 206 of the system 102. The system 102 uses the past appliance data 212 stored in the memory 206 for the training, which is explained in subsequent paragraphs of the specification.
An example of the past appliance data 212 is shown below in table 1, in which, the electronic appliance 112 is considered as “Air-Conditioner” (AC) and the predefined time-period is considered as “one month”.

Past Appliance Data Values
past energy consumption data past energy units consumed 180 units/month
past usage data past usage time 240 hours/month

past usage frequency 90 times/month
past operating data past operating mode Normal: 60 times/month Medium: 90 times/month High: 30 times/month

past operating value 18°C to 28°C

Table 1: Showing the past appliance data for past 1 month, for example.
The above data in the table 1 indicates how the electronic appliance 112 (AC in this case) has been used for the entire one month. The data presented in the table 1 also indicates about the user behavior while operating the AC for the entire one month. It may be understood to the skilled person that the above data is shown as an example only, and however different past appliance data may be received for different predefined time-periods, for example last one day, last two days, last one week, last month, last one quarter, last one year, or any specific predefined time-period, as already discussed in the above paragraphs of the specification.
Once the past appliance data 212 is received by the system 102, the next task is to understand trend or pattern of the electronic appliance 112 usage. For this, the determining module 222 analyzes the past appliance data 212 for determining an energy consumption trend for the electronic appliance 112 i.e., AC. The energy consumption trend may be understood as graphical representation of relation between the electronic appliance 112 and energy consumption for the predefined time-period (one month in this case). Stated another way, the energy consumption trend provides a relation of an amount of energy consumed, by the electronic appliance 112, between different time-intervals of the predefined time-period.
The energy consumption trend is essential for the system 102, particularly to understand about how the electronic appliance has been used by the user in the past. Here, the user may be understood as one or more users using the one or more electronic appliances 112. The energy consumption trend also serves a starting point for the generating module 224 for generating the self-learning model 232. The purpose of generating the self-learning model is to make the system 102 independent of any user intervention and provide technical advancement in existing home or industry automation technology. That is, the home and industry automation technology may become advance by learning the usage of the electronic appliances 112, by using the self-learning model 232, and taking the actions accordingly. It may be understood to the skilled person that the self-learning model 232 is capable of learning itself when it encounters with current and future appliance data over the time.

However, before such encounter, the self-learning model 232, based on the learning from the energy consumption trend, determines the optimized appliance data 214 comprising optimized energy consumption data, optimized usage data and optimized operating data. As already discussed in the above paragraphs, the optimized appliance data 214 indicates the normal and the genuine usage of the electronic appliance 112 by the user. Considering the example of table 1, the optimized appliance data 214 determined by the self-learning model 232 is shown in below table 2.

Optimized Appliance Data Values
optimized energy consumption data optimized energy units consumed 4-8 units/Day
optimized usage data optimized usage time 6-10 hours/Day

optimized usage frequency 2-4 times/Day
optimized operating data optimized operating mode Normal: 2-3 times/Day Medium: 2-4 times/Day High: 1-2 times/Day

optimized operating value 22°C to 26°C
Table 2: Showing the optimized appliance data for 1 day based on the past 1 month.
From the above table 2, it can be observed that the optimized appliance data 214 is determined in a form of a “range” rather than definite value on “per day” basis. The reason being, while collecting the past appliance data 212 in table 1, the maximum time-period considered was one month. Hence, it will be appropriate to consider the optimized appliance data 214 for less than a month. So, in the present example, the optimized appliance data 214 has been considered for a day. Since the analysis has to be done (in this example) for the day, it must be understood that the data may vary from one day to another day. Hence, , the system 102 defines the range while determining the optimized appliance data 214. One of the approaches followed by the system 102 for defining the range is explained here below.
For example, for determining the optimized range, the system 102 first averages the values shown in table 1. For example, for determining the range for “optimized energy units consumed”, the system 102 may first average the value associated with “past energy units consumed” of table

1 i.e., by dividing “180 units/month” by 30 (number of days in a month which is considered in this example), which results in 6 units per day. However, as discussed above, 6 units per day (i.e., a definite value) may not be suitable for analyzing the current appliance data on daily basis, as it may vary from one day to another day. Thus, instead of providing any definite value, the system 102 provides a range of 4-8 Units/Day i.e., 6±2 (6 plus minus 2). It may be understood to the skilled person that the ±2 range is just an example and there may be other ranges also which may be considered by the system 102.
Based on the aforementioned elucidation, it may be understood that 4 units is determined as a lower range. Whereas 8 units is determined as an upper range for the optimized energy units consumed in table 2. With the similar logic, the system 102 determines optimized range for remaining optimized appliance data 214 except for optimized operating value, which can be seen from the table 2. For the optimized operating value, the system 102 may implement another logic, in which, the system 102 refers the “optimized energy units consumed” is past one month for determining the optimized operating value. That is, once the system 102 learns that the optimized range for the “optimized energy units consumed” must be in between 4 to 8 units/day, the system 102 itself determines what would be the operating temperature (i.e., operating value) to be set for the AC for keeping the energy consumption in the optimized range of 4-8 units/day.
It may further be understood to the skilled person that the above discussed logic for determining the optimized appliance data is an example, and there may be other logics which may be used by the system 102. However, the motive is to determine the optimized range for the appliance data by learning the past usage by the user. The data shown in the above table 1 and table 2 may vary from one electronic appliance 112 to another as well as from one user to another user. It may be also understood to the skilled person that the system 102 may parallelly perform the similar analysis as discussed in the above paragraphs with respect to table 1 and table 2 for other electronic appliances 112 like washing machine, fridge, microwave oven and the like for same and different users.
Now, once the self-learning model 232 is generated and the optimized appliance data 214 is determined, the system 102 now becomes ready for analyzing the current appliance data 216

with the objective of optimizing the energy consumption of the electronic appliance 112. For this, the receiving module 220 may now receive the current appliance data 216 corresponding to the electronic appliance 112 i.e., AC in this case. As already discussed in the above paragraphs, the current appliance data 216 may comprise the real-time data for which the analysis has to be performed. Since the range defined for the optimized appliance data 214 in table 2 is based on daily basis, the current appliance data 216 is also considered on the daily basis, according to this embodiment of the present disclosure. Below table 3 shows an example of the current appliance data 216 for two different days i.e., day 1 and day 2.

Current Appliance Data Day 1 Values Day 2 Values
current energy current energy 10 units/Day 7 units/Day
consumption units consumed
data
current usage current usage 11 hours/Day 9 hours/Day
data time

current usage 3 times/Day (within range) 5 times/Day (outside range)
frequency
current current operating Normal: 2 times/Day Normal: 4 times/Day
operating data mode (within range) (outside range)
Medium: 3 times/Day Medium: 3 times/Day
(within range) High: 3 times/Day High: 1 time/Day

current operating 19°C 24°C
value
Analysis Anomaly detected No Anomaly detected
Table 3: Showing the current appliance data for two days i.e., day 1 and day 2.
The purpose of considering the day 1 and the day 2 values in the above table 3 is to clearly explain how the system 102 understands two different scenarios and take decisions accordingly using the self-learning model 232. In this section, the analyzing module 226 of the system 102 analyzes the current appliance data 216 in relation to the optimized appliance data 214. The

analyzing process is explained here in detail by referring table 2 (which corresponds to optimized appliance data 214) and table 3 (which corresponds to current appliance data 216).
Analysis for Day 1 Values
Before starting the explanation, it may be understood to the skilled person that the system 102 may give different weightages to different type of the appliance data. By analyzing the day 1 values in the table 3 with the defined range in the table 2, it can be observed that, the value of “current energy units consumed” i.e., 10 units/day is going beyond the optimized range of 4-8 units/day. Similarly, the value of “current usage time” i.e., 11 hours/day is also going beyond the optimized range of 6-10 hours/day. Similarly, current operating mode such as “high” and “current operating value” is also going beyond their optimized ranges defined in the table 2. However, few values (emphasized with underline in table 3 under day 1 values) are still in their optimized ranges. For example, the values for “current usage frequency”, “Normal” mode and “medium” mode are still in their optimized ranges. In other words, it can be said that, the system 102 receives the mixture of values, in which, some values are going beyond the optimized ranges and some value are not.
In this scenario, as discussed in starting of above paragraph, the system 102 may now analyze the day 1 values in view of the weightages. For example, the weightage provided for “current energy units consumed” is highest. This may be because, the system 102 knows that the primary objective is to optimize the energy consumption for the electronic appliances 112. For achieving this objective, the energy units consumed “must” be in optimized range, no matter if other values are deviating or not deviating from their optimized ranges. Similar condition is shown above by day 1 values, in which, though some of the values (as discussed above) is still within their optimized ranges, however the energy units consumed (having the highest weightage) is going beyond the optimized range. Thus, the system 102 consider this deviation as an anomaly. The action taken by the system 102 based on the anomaly is discussed in later paragraphs of the specification.
Analysis for Day 2 Values

By analyzing the day 2 values in the table 3 with the defined range in the table 2, it can be observed that, the value of “current energy units consumed” i.e., 7 units/day is within the optimized range of 4-8 units/day. Similarly, the value of “current usage time” i.e., 9 hours/day is also within the optimized range of 6-10 hours/day. Similarly, current operating mode such as “medium” & “high” and “current operating value” is also within their optimized ranges defined in table 2. However, few values (emphasized with underline under day 2 values in table 3) are going beyond their optimized ranges. For example, the values for “current usage frequency”, “Normal” mode are going beyond their optimized ranges. In this case also, the system 102 receives the mixture of values, in which, some values are within the optimized range, however some value are going beyond the range.
For handling the data of this scenario also, the system 102 may now analyze the day 2 values in view of the weightages. For example, the weightage provided for “current energy units consumed” is highest. This may be because, the system 102 knows that the primary objective is to optimize the energy consumption for the electronic appliances 112. For achieving this objective, the energy units consumed “must” be in optimized range, no matter if other values are deviating or not deviating from their optimized ranges. Similar condition is shown above by day 2 values, in which, though some of the values (as discussed above) is not within their optimized ranges, however the energy units consumed (having the highest weightage) is still within the optimized range. Thus, the system 102 consider this deviation as normal behavior and no anomaly is detected.
While performing the above analysis as discussed with reference to day 1 and day 2 values, the system 102 also monitors the number of instances when the current appliance data 216 deviates from the optimized appliance data 214. If the number of instances of the deviation is greater than or equal to a threshold value for a predefined time period (for example, 3 times in a week or 4 times in a week), the performing module 228 may update the self-learning model 232 and accordingly modify the optimized appliance data 214 to new optimized appliance data using the current appliance data 216. In an exemplary scenario, the system 102 observes that the day 1 values (which is currently considered as anomaly) are received regularly or frequently (for example, for 4 times in a week). In such scenario, the system 102 understands that normal behavior of the user for using the electronic appliance 112 has been changed, and hence the self-learning model 232

needs to be updated. In this case, the system 102 may regenerate the new optimized appliance data by using the self-learning model 232. This may happen, if the summer arrives and the user now require more use of AC than it was being used earlier.
On the contrary, if the number of instances of the deviation is less than the threshold value, the performing module 224 may not modify the optimized appliance data 214. In an exemplary scenario, the system 102 observes that the day 2 values (which is currently considered as not an anomaly) are received for only one time or two times (threshold value) in the entire week time. In such scenario, the system 102 understands that the normal behavior of the user for using the electronic appliance 112 has not been changed. In this case, the self-learning model 232 may not modify the existing optimized appliance data 214. Hence, from the above analysis discussed with respect to day 1 values and day 2 values, it can be observed that how the system 102 differentiates between normal and abnormal behavior of the electronic appliances 112, and also simultaneously learns from the analysis.
Here, the system 102 is not only limited to detect the anomaly/non-anomaly situation and learn from such situation, the system 102 also take corrective actions if the anomaly is detected. That is, in a case where the anomaly is detected, the system 102 enables the operating device 114 (discussed with reference to figure 1) for taking corrective action and thereby rectifying the anomaly. Here, the corrective action may be a tangible action taken by the operating device 114. For example, automatically adjusting the current operating data to the optimized operating data to enable the electronic appliances 112 to operate according to the optimized energy consumption data. Referring to Day 1 values of table 3, in which, the anomaly is detected. In this case, the system 102 enables the operating device 114 associated with the AC to adjust the current operating value of “19°C” to the optimized range of “22°C to 26°C” so that the AC may be able to operate according to optimized range (energy units consumed) of 4-8 units/day instead of 10 units/day.
Moreover, the system 102 may also provide an information (recommendation) about the optimized usage data to the user, so that the user himself/herself may able to operate the electronic appliances 112 according to the optimized energy consumption data.

According to an embodiment, the user may ask to the system 102, via the user assisting device 108, about the health of the electronic appliances 112. For example, the user may ask “How is my refrigerator doing?” or “Draw energy analytics of my kitchen appliances for past one month”. Upon receiving the user request enquiring about the performance of electronic appliance, the generating module 224 may generate a response to the user request based on the analysis of the current appliance data 216 in relation to the optimized appliance data 214 using the self-learning model 232 (as discussed in the above paragraphs). According to an embodiment, the user request and the response to the user request are generated in at least one of a textual form, a voice form, a visual form, or a combination thereof. For example, for the first request, the system 102 may respond like “Your refrigerator is performing normal but needs temperature settings change from 48° F (existing temperature setting) to 40° F (recommended temperature setting)”. For second request, the system 102 may respond like “Your kitchen has four high wattage devices impacting the spike in energy usage profile. They need to be used judiciously especially post 8 pm when the electricity demand in the household is high”. This way, the system 102 not only learns and optimizes the energy consumption of the electronic appliances 112, but also helps the users to interacts with the system 102 using the user assisting device 108.
In addition, the system 102 also performs the predictive analysis by analyzing the current appliance data 216. For instance, the current appliance data 216 is causing sudden surge of current in room-heater post 8 pm every day and the heater is responding by dissipating heat through its body and not through coils alone. Here, the body may be understood as an outer surface of the heater which may include cover, handle or stand. The heat is not supposed to dissipate from the body of the heater. In this case, there may be two sensors 104 associated with the room heater. First sensor 104 may sense the “current energy units consumed”, for example 3 units and transmit it to the system 102. Whereas, the second sensor 104 may be used for sensing heat dissipated from the coil and heat dissipated from body of the heater. In normal operating behavior, the heat dissipated from the coil must be more than the heat dissipated from the body of the heater. The normal heat value may be stored with the system 102 in the form of optimized appliance data 214. Now, if the current heat values of the coil and the body of the heater goes beyond the optimized or normal values regularly for a predefined time period, for example 3 consecutive days, the system 102 concludes that this is an abnormal or an unusual behavior of the heater. The system 102

predicts this trend before-hand by using the self-learning model 232 to preclude the cause and its effect at the first place. Based on such prediction, the system 102 may also take cognitive decision like turning the power in OFF state to prevent the electronic appliance 112 from getting damage.
FIG. 3 shows a flowchart illustrating a method of optimizing energy consumption of one or more electronic appliances with some embodiments of the present disclosure.
As illustrated in FIG. 3, the method 300 comprises one or more blocks for optimizing energy consumption of one or more electronic appliances using an energy optimization system 102. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.
The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.
At block 302, the energy optimization system 102 receives current appliance data 216 corresponding to the one or more electronic appliances 112 from one or more sensor 104 associated with the one or more electronic appliances 112 at predefined intervals of time.
At block 304, the energy optimization system 102 analyzes the current appliance data 216 in relation to optimized appliance data 214 using a self-learning model 232. The analyzing is explained in detail using figure 4.
At block 306, the energy optimization system 102 performs, based upon the analyzing as discussed in block 304, at least one of updating the self-learning model 232 with the current appliance data 216 and detecting an anomaly in the one or more electronic appliances 112.

FIG. 4 shows a flowchart illustrating the method for analyzing current appliance data in relative to optimized appliance data in accordance with some embodiments of the present disclosure.
At block 402, the system 102 monitors the current appliance data 216 for the predefined intervals of time.
At block 404, the system 102 determines, based on the monitoring, number of instances when the current appliance data 216 deviates from the optimized appliance data 214 during the predefined intervals of time.
At block 406, system 102 checks whether the number of instances is greater than or equal to a threshold value. For example, it may happen that current appliance data 216 may frequently or regularly deviate from the optimized appliance data 214.
At block 408, if the above condition described in block 406 satisfies, the system 102 updates the self-learning model 232 by modifying the optimized appliance data 214 to new optimized appliance data using the current appliance data 216.
At block 410, however, if the above condition described in the block 406 is not satisfied, then the system 102 does not modify the optimized appliance data 214. This indicates that the optimized appliance data 214 is still correct in relative to the current appliance data 216.
Computer System
Fig.5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present invention. In an embodiment, the computer system 500 can be the energy optimization system 102 which is used for optimizing energy consumption of one or more electronic appliances 112. According to an embodiment, the computer system 500 may receive appliance data 510 which may include, for example, past appliance data 212 and current appliance data 216 from the sensor 104 or the data aggregator 106. The computer system 500 may comprise a central processing unit (“CPU” or “processor”) 502. The processor 502 may

comprise at least one data processor for executing program components for executing user- or system-generated business processes. The processor 502 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.
The processor 502 may be disposed in communication with one or more input/output (I/O) devices (511 and 512) via I/O interface 501. The I/O interface 501 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc.
Using the I/O interface 501, the computer system 500 may communicate with one or more I/O devices (511 and 512).
In some embodiments, the processor 502 may be disposed in communication with a communication network 509 via a network interface 503. The network interface 503 may communicate with the communication network 509. The network interface 503 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 509 can be implemented as one of the different types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network 509 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network 509 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.
In some embodiments, the processor 502 may be disposed in communication with a memory 505 (e.g., RAM 513, ROM 514, etc. as shown in FIG. 5) via a storage interface 504. The

storage interface 504 may connect to memory 505 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.
The memory 505 may store a collection of program or database components, including, without limitation, user/application data 506, an operating system 507, web browser 508 etc. In some embodiments, the computer system 500 may store user/application data 506, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.
The operating system 507 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, Net BSD, Open BSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, K-Ubuntu, etc.), International Business Machines (IBM) OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry Operating System (OS), or the like. I/O interface 501 may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, I/O interface may provide computer interaction interface elements on a display system operatively connected to the computer system 500, such as cursors, icons, check boxes, menus, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems’ Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), or the like.
In some embodiments, the computer system 500 may implement a web browser 508 stored program component. The web browser 508 may be a hypertext viewing application, such as Microsoft™ Internet Explorer, Google™ Chrome, Mozilla™ Firefox, Apple™ Safari™, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS) secure sockets layer (SSL), Transport Layer Security (TLS), etc. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, Application Programming

Interfaces (APIs), etc. In some embodiments, the computer system 500 may implement a mail server stored program component. The mail server 516 may be an Internet mail server such as Microsoft Exchange, or the like. The mail server 516 may utilize facilities such as Active Server Pages (ASP), ActiveX, American National Standards Institute (ANSI) C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), Microsoft Exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 500 may implement a mail client 515 stored program component. The mail client 515 may be a mail viewing application, such as Apple™ Mail, Microsoft™ Entourage, Microsoft™ Outlook, Mozilla™ Thunderbird, etc.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.
Advantages of the embodiment of the present disclosure are illustrated herein.
In an embodiment, the present disclosure provides a method of learning about the user behavior while operating the appliances.
In an embodiment, the method of present disclosure optimizes the energy consumption based on the learning, thereby not only saving electricity bills, but also preventing the appliances from unnecessary operation.

In an embodiment, the present disclosure provides a personalized attention to the users based on his/her behavior while using the appliances.
The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.
The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise.
The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise.
The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.
A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.
When a single device or article is described herein, it will be readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.
Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on.

Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Referral Numerals:

Reference Number Description
100 ENVIRONMENT
102 ENERGY OPTIMIZATION SYSTEM
104 SENSOR
106 DATA AGGREGATOR
108 USER ASSISTING DEVICE
110 NETWORK
112 APPLIANCES
114 OPERATING DEVICE
202 I/O INTERFACE
204 PROCESSOR
206 MEMORY
208 DATA
210 MODULES
212 PAST APPLIANCE DATA
214 OPTIMIZED APPLIANCE DATA
216 CURRENT APPLIANCE DATA
218 OTHER DATA
220 RECEIVING MODULE
222 DETERMINING MODULE
224 GENERATING MODULE
226 ANALYZING MODULE
228 PERFORMING MODULE
230 OTHER MODULES
232 SELF-LEARNING MODEL
500 EXEMPLARY COMPUTER SYSTEM
501 I/O INTERFACE OF THE EXEMPLARY COMPUTER SYSTEM
502 PROCESSOR OF THE EXEMPLARY COMPUTER SYSTEM
503 NETWORK INTERFACE

504 STORAGE INTERFACE
505 MEMORY OF THE EXEMPLARY COMPUTER SYSTEM
506 USER/APPLICATION
507 OPERATING SYSTEM
508 WEB BROWSER
509 COMMUNICATION NETWORK
510 APPLIANCE DATA
511 INPUT DEVICES
512 OUTPUT DEVICES
513 RAM
514 ROM
515 MAIL CLIENT
516 MAIL SERVER

We Claim:
1. A method for optimizing energy consumption of one or more electronic appliances (112),
the method comprising:
receiving, by an energy optimization system (102), current appliance data (216) corresponding to the one or more electronic appliances (112) from one or more sensors (104) associated with the one or more electronic appliances (112) at predefined intervals of time;
analyzing, by the energy optimization system (102), the current appliance data (216) in relative to optimized appliance data (214) using a self-learning model (232); and
performing, by the energy optimization system (102), based upon the analyzing, at least one of:
updating the self-learning model (232) with the current appliance data (216); and detecting an anomaly in the one or more electronic appliances (112).
2. The method as claimed in claim 1, wherein the current appliance data (216) comprises,
current energy consumption data comprising current energy units corresponding to the one
or more electronic appliances (112),
current usage data comprising at least one of current usage time and current usage frequency corresponding to the one or more electronic appliances (112), and
current operating data comprising at least one of current operating mode and current operating value corresponding to the one or more electronic appliances (112).
3. The method as claimed in claim 1, wherein the self-learning model (232) is generated by:
receiving, by the energy optimization system (102), past appliance data (212) comprising
past energy consumption data, past usage data and past operating data associated with the one or more electronic appliances (112);
determining, by the energy optimization system (102), an energy consumption trend for the one or more electronic appliances (112) based on the past appliance data (212), wherein the energy consumption trend comprises a relation of an amount of energy consumed, by the one or

more electronic appliances (112), between different time-intervals of a predefined time-period; and
generating, by the energy optimization system (102), the self-learning model (232) based on the energy consumption trend, wherein the self-learning model (232) determines the optimized appliance data (214) comprising optimized energy consumption data, optimized usage data and optimized operating data by learning from the energy consumption trend, and wherein the optimized appliance data (214) indicate natural behavior of the one or more electronic appliances (112) being operated by a user for the predefined time-period.
4. The method as claimed in claim 1, further comprising enabling, by the energy optimization
system (102), an operating device (114) for taking corrective action for rectifying the anomaly,
wherein the corrective action comprises at least one of,
automatically adjusting the current operating data to the optimized operating data thereby enabling the one or more electronic appliances (112) to operate according to the optimized energy consumption data, and
providing information about the optimized usage data to the user thereby enabling the one or more electronic appliances (112) to operate according to the optimized energy consumption data.
5. The method as claimed in claim 1, further comprising enabling the energy optimization
system (102), coupled with a user assisting device (108) associated with the user, for
receiving user request enquiring about performance of the one or more electronic appliances (112), and
generating a response to the user request based on the analyzing of the current appliance data (216) in relative to the optimized appliance data (214) using the self-learning model (232), wherein the user request is received and the response to the user request is generated in at least one of a textual form, a voice form, a visual form, or a combination thereof.
6. The method as claimed in claim 1, wherein the analyzing, by the energy optimization
system (102), the current appliance data (216) in relative to the optimized appliance data (214)
comprises:

monitoring the current appliance data (216) for the predefined intervals of time;
determining, based on the monitoring, number of instances when the current appliance data (216) deviates from the optimized appliance data (214) during the predefined intervals of time; and
updating, based on the determining, the self-learning model (232) by modifying the optimized appliance data (214) to new optimized appliance data using the current appliance data (216) when the number of instances is greater than or equal to a threshold value.
7. An energy optimization system (102) for optimizing energy consumption of one or more
electronic appliances (112), the energy optimization system (102) comprising:
a processor (204); and
a memory (206) communicatively coupled to the processor (204), wherein the memory
(206) stores processor-executable instructions, which, on execution, causes the processor (204) to:
receive current appliance data (216) corresponding to the one or more electronic
appliances (112) from one or more sensors (104) associated with the one or more electronic
appliances (112) at predefined intervals of time;
analyze the current appliance data (216) in relative to optimized appliance data (214) using a self-learning model (232); and
perform, based upon the analyzing, at least one of:
update the self-learning model (232) with the current appliance data (216); and
detect an anomaly in the one or more electronic appliances (112).
8. The energy optimization system (102) as claimed in claim 7, wherein the current appliance
data (216) comprises:
current energy consumption data comprising current energy units corresponding to the one or more electronic appliances (112),
current usage data comprising at least one of current usage time and current usage frequency corresponding to the one or more electronic appliances (112), and
current operating data comprising at least one of current operating mode and current operating value corresponding to the one or more electronic appliances (112).

9. The energy optimization system (102) as claimed in claim 7, wherein the processor (204)
is configured to generate the self-learning model (232) by:
receiving past appliance data (212) comprising past energy consumption data, past usage data and past operating data associated with the one or more electronic appliances (112);
determining an energy consumption trend for the one or more electronic appliances (112) based on the past appliance data (212), wherein the energy consumption trend comprises a relation of an amount of energy consumed, by the one or more electronic appliances (112), between different time-intervals of a predefined time-period; and
generating the self-learning model (232) based on the energy consumption trend, wherein the self-learning model (232) determines the optimized appliance data (214) comprising optimized energy consumption data, optimized usage data and optimized operating data by learning from the energy consumption trend, and wherein the optimized appliance data (214) indicate natural behavior of the one or more electronic appliances (112) being operated by a user for the predefined time-period.
10. The energy optimization system (102) as claimed in claim 7, wherein the processor (204)
is configured to enable an operating device (114) to take corrective action to rectify the anomaly,
wherein the corrective action comprises at least one of:
automatically adjusting the current operating data to the optimized operating data to enable the one or more electronic appliances (112) to operate according to the optimized energy consumption data, and
providing information about the optimized usage data to the user to enable the one or more electronic appliances (112) to operate according to the optimized energy consumption data.
11. The energy optimization system (102) as claimed in claim 7, is coupled with a user assisting
device (108) associated with the user, and wherein the energy optimization system (102) is
configured to:
receive user request enquiring about performance of the one or more electronic appliances (112), and

generate a response to the user request based on the analyzing of the current appliance data (216) in relative to the optimized appliance data (214) using the self-learning model (232), wherein the user request is received and the response to the user request is generated in at least one of a textual form, a voice form, a visual form, or a combination thereof.
12. The energy optimization system (102) as claimed in claim 7, wherein the processor (204)
is configured to analyze the current appliance data (216) in relative to the optimized appliance data (214) by:
monitoring the current appliance data (216) for the predefined intervals of time;
determining, based on the monitoring, number of instances when the current appliance data (216) deviates from the optimized appliance data (214) during the predefined intervals of time;
updating the self-learning model (232), based on the determining, by modifying the optimized appliance data (214) to new optimized appliance data using the current appliance data (216) when the number of instances is greater than or equal to a threshold value.