Description: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 RECOMMENDING AN EMISSION REDUCTION POTENTIAL

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

Preamble to the description:

The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD

The disclosure herein generally relates to emission reduction and, more particularly, to recommending an emission reduction potential.

BACKGROUND

The environment faces a long-term challenge because of global warming due to greenhouse gases. The effect of greenhouse gases on the environment/climate and health are immense. In addition to causing climate change, the greenhouse gases can also harm the health of living beings by contributing to smog and air pollution. Further other effects of climate change include extreme weather, food shortages, and wildfires. Hence, to stabilize the effects of the greenhouse gases, it is necessary to reduce substantially, rapidly, and sustainably greenhouse gas emissions to achieve net-zero emissions.
Sustainability risk management (SRM) is concerned with minimizing environmental and social responsibility risks to achieve an overarching business strategy to enable sustainability and profitability in the long term. One of the prime focuses of sustainability is emission monitoring, wherein the emission of the greenhouse gases is quantified-monitored to curb its increase. The existing techniques for emission reduction perform emission monitoring and recommend possible reduction solution. However the possible reduction solution recommendations do not explicitly consider the profile of assets/entity that are responsible for the emission nor the historic data. Hence there is a requirement for techniques that can quantify emission reduction and also provide recommendations for emission reduction while considering the asset details.

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 method for recommending an emission reduction potential is provided.
The system includes a memory storing instructions, one or more communication interfaces, and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to receive a plurality of inputs, via one or more hardware processors, wherein the plurality of inputs are associated with a plurality entities producing emissions, wherein the plurality of inputs includes a plurality of attributes of each entity from the plurality of entities, an activity level of each entity, and an emission associated with the activity level of the entity. The system is further configured to pre-process the plurality of inputs, via the one or more hardware processors, using a set of pre-processing techniques to obtain the set of pre-processed inputs. The system is further configured to generate a plurality of emission entities and the associated emission entity attributes, via the one or more hardware processors, for the set of pre-processed inputs based on a dynamic external source. The system is further configured to compute an emission intensity for each entity, via the one or more hardware processors, using the activity level and the plurality of emission entities based on an emission intensity computation technique. The system is further configured to cluster the plurality of entities to obtain a plurality of entity clusters, via the one or more hardware processors, based on the emission intensity and the emission entity attributes, using a clustering technique, wherein centroid of each of the entity cluster is the emission intensity attribute of the entity. The system is further configured to classify each of the plurality of entity clusters as one of: (a) minimum emission intensity cluster, and (b) maximum emission intensity cluster, via the one or more hardware processors, based on the centroids of each cluster using a ranking technique. The system is further configured to determine a total quantum of emission reduction, via the one or more hardware processors, for the clustered plurality of entities using an emission reduction technique. The system is further configured to recommend an emission reduction potential, via the one or more hardware processors, wherein an attribute from the plurality of attributes is identified by computing an attribute difference.
In another aspect, a method for recommending an emission reduction potential is provided. The method includes receiving a plurality of inputs, via one or more hardware processors, wherein the plurality of inputs are associated with a plurality entities producing emissions, wherein the plurality of inputs includes a plurality of attributes of each entity from the plurality of entities, an activity level of each entity, and an emission associated with the activity level of the entity. The method further includes pre-processing the plurality of inputs, via the one or more hardware processors, using a set of pre-processing techniques to obtain the set of pre-processed inputs. The method further includes generating a plurality of emission entities and the associated emission entity attributes, via the one or more hardware processors, for the set of pre-processed inputs based on a dynamic external source. The method further includes computing an emission intensity for each entity, via the one or more hardware processors, using the activity level and the plurality of emission entities based on an emission intensity computation technique. The method further includes clustering the plurality of entities to obtain a plurality of entity clusters, via the one or more hardware processors, based on the emission intensity and the emission entity attributes, using a clustering technique, wherein centroid of each of the entity cluster is the emission intensity attribute of the entity. The method further includes classifying each of the plurality of entity clusters as one of: (a) minimum emission intensity cluster, and (b) maximum emission intensity cluster, via the one or more hardware processors, based on the centroids of each cluster using a ranking technique. The method further includes determining a total quantum of emission reduction, via the one or more hardware processors, for the clustered plurality of entities using an emission reduction technique. The method further includes recommending an emission reduction potential, via the one or more hardware processors, wherein an attribute from the plurality of attributes is identified by computing an attribute difference.
In yet another aspect, a non-transitory computer readable medium for recommending an emission reduction potential is provided. The method includes receiving a plurality of inputs, via one or more hardware processors, wherein the plurality of inputs are associated with a plurality entities producing emissions, wherein the plurality of inputs includes a plurality of attributes of each entity from the plurality of entities, an activity level of each entity, and an emission associated with the activity level of the entity. The method further includes pre-processing the plurality of inputs, via the one or more hardware processors, using a set of pre-processing techniques to obtain the set of pre-processed inputs. The method further includes generating a plurality of emission entities and the associated emission entity attributes, via the one or more hardware processors, for the set of pre-processed inputs based on a dynamic external source. The method further includes computing an emission intensity for each entity, via the one or more hardware processors, using the activity level and the plurality of emission entities based on an emission intensity computation technique. The method further includes clustering the plurality of entities to obtain a plurality of entity clusters, via the one or more hardware processors, based on the emission intensity and the emission entity attributes, using a clustering technique, wherein centroid of each of the entity cluster is the emission intensity attribute of the entity. The method further includes classifying each of the plurality of entity clusters as one of: (a) minimum emission intensity cluster, and (b) maximum emission intensity cluster, via the one or more hardware processors, based on the centroids of each cluster using a ranking technique. The method further includes determining a total quantum of emission reduction, via the one or more hardware processors, for the clustered plurality of entities using an emission reduction technique. The method further includes recommending an emission reduction potential, via the one or more hardware processors, wherein an attribute from the plurality of attributes is identified by computing an attribute difference.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

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 exemplary system for recommending an emission reduction potential according to some embodiments of the present disclosure.
FIG. 2 is a functional block diagram for recommending an emission reduction potential according to some embodiments of the present disclosure.
FIGS. 3A and FIG. 3B is a flow diagram illustrating a method (300) for recommending an emission reduction potential in accordance with some embodiments of the present disclosure.
FIG. 4 is a flow diagram illustrating a method (400) for emission reduction technique 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.
Referring now to the drawings, and more particularly to FIG. 1 through FIG. 4, 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 is an exemplary block diagram of a system 100 for emission reduction technique in accordance with some embodiments of the present disclosure.
In an embodiment, the system 100 includes a processor(s) 104, communication interface device(s), alternatively referred as input/output (I/O) interface(s) 106, and one or more data storage devices or a memory 102 operatively coupled to the processor(s) 104. The system 100 with one or more hardware processors is configured to execute functions of one or more functional blocks of the system 100.
Referring to the components of the system 100, in an embodiment, the processor(s) 104, can be one or more hardware processors 104. In an embodiment, the one or more hardware processors 104 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 one or more hardware processors 104 is configured to fetch and execute computer-readable instructions stored in the memory 102. In an embodiment, the system 100 can be implemented in a variety of computing systems including laptop computers, notebooks, hand-held devices such as mobile phones, workstations, mainframe computers, servers, a network cloud and the like.
The I/O interface(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, a touch user interface (TUI) 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 (s) 106 can include one or more ports for connecting a number of devices (nodes) of the system 100 to one another or to another server.
The memory 102 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.
Further, the memory 102 may include a database 108 configured to include information regarding emission reduction and recommending an emission reduction potential. The memory 102 may comprise information pertaining to input(s)/output(s) of each step performed by the processor(s) 104 of the system 100 and methods of the present disclosure. In an embodiment, the database 108 may be external (not shown) to the system 100 and coupled to the system via the I/O interface 106.
Functions of the components of system 100 are explained in conjunction with functional overview of the system 100 in FIG. 2 and flow diagram of FIG. 3A and FIG. 3B for recommending an emission reduction potential.
The system 100 supports various connectivity options such as BLUETOOTH®, USB, ZigBee and other cellular services. The network environment enables connection of various components of the system 100 using any communication link including Internet, WAN, MAN, and so on. In an exemplary embodiment, the system 100 is implemented to operate as a stand-alone device. In another embodiment, the system 100 may be implemented to work as a loosely coupled device to a smart computing environment. The components and functionalities of the system 100 are described further in detail.
FIG.2 is an example functional block diagram of the various modules of the system of FIG.1, in accordance with some embodiments of the present disclosure. As depicted in the architecture, the FIG.2 illustrates the functions of the modules of the system 100 that includes recommending an emission reduction potential.
As depicted in FIG.2, the functional system 200 of system 100 system 200 is configured for recommending an emission reduction potential. The system 200 comprises an input module 202 configured for receiving a plurality of inputs, wherein the plurality of inputs are associated with a plurality entities producing emissions. The system 200 further comprises a pre-processor 204 configured for pre-processing the plurality of inputs using a set of pre-processing techniques to obtain the set of pre-processed inputs. The system 200 further comprises an emission entities extractor 206 configured for generating a plurality of emission entities and the associated emission entity attributes for the set of pre-processed inputs based on a dynamic external source. The system 200 further comprises an emission intensity module 208 configured for computing an emission intensity for each entity using the activity level and the plurality of emission entities based on an emission intensity computation technique. The system 200 further comprises a clusterer 210 configured for clustering the plurality of entities based on the emission intensity and the emission entity attributes using a clustering technique, wherein centroid of each cluster is the emission intensity attribute of the entity. The system 200 further comprises a classifier 212 configured for classifying each of the clustered plurality of entities as one of: (a) minimum emission intensity cluster and (b) maximum emission intensity cluster, based on the centroids of each cluster using a ranking technique. The system 200 further comprises an emission reduction determiner 214 configured for determining a total quantum of emission reduction for the clustered plurality of entities using an emission reduction technique. The system 200 further comprises an emission reduction potential recommender 216 configured for recommending an emission reduction potential, wherein an attribute from the plurality of attributes is identified by computing an attribute difference
The various modules of the system 100 and the functional blocks in FIG. 2 are configured for recommending an emission reduction potential are implemented as at least one of a logically self-contained part of a software program, a self-contained hardware component, and/or, a self-contained hardware component with a logically self-contained part of a software program embedded into each of the hardware component that when executed perform the above method described herein.
Functions of the components of the system 200 are explained in conjunction with functional modules of the system 100 stored in the memory 102 and further explained in conjunction with flow diagram of FIGS. 3A-3B. The FIGS. 3A-3B with reference to FIG. 1, is an exemplary flow diagram illustrating a method 300 for recommending an emission reduction potential using the system 100 of FIG. 1 according to an embodiment of the present disclosure.
The steps of the method of the present disclosure will now be explained with reference to the components of the system 100 of FIG. 1 for recommending an emission reduction potential and the modules 202-216 as depicted in FIG. 2 and the flow diagrams as depicted in FIGS. 3A-3B. 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 to 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, a plurality of inputs is received from a plurality of sources by the input module 202. The plurality of inputs includes a plurality of attributes of each entity from the plurality of entities, an activity level of each entity, an emission associated with the activity level of the entity.
In an embodiment, the plurality of entities are emission entities, wherein the emission entities can be one of a vehicle owned by an enterprise which burn fuel and produce emissions, or generators installed in the enterprise producing electricity and producing emissions or facilities in the organization which use fuel for heating or consume electricity which leads to emissions. Each entity is associated with a plurality of attributes, in an example scenario attributes for a Vehicle entity includes a vehicle_code, a type is_on_road, a make, a model, a year, an engine_type, a fuel_type_recommended, an engine_mileage_rating,a distance_travelled, a fuel_consumption, an emission_value and a emission_intensity. Similarly the attributes for the electricity generators (Emission Entity) includes an Asset ID, make , model, capacity, type of fuel, last services date, an emission value for last year, fuel consumption per month, and emission per liter of fuel consumed.
At step 304 of the method 300, the plurality of inputs is pre-processed in the pre-processor 204. The plurality of inputs is pre-processed using a set of pre-processing techniques to obtain the set of pre-processed inputs.
In an embodiment, the set of pre-processing techniques comprises a plurality of data check techniques for identifying data duplicates, a set of missing data fields identification techniques, and a set of techniques to perform data format check.
At step 306 of the method 300, a plurality of emission entities and the associated emission entity attributes are generated for the set of pre-processed inputs in the emission entities extractor 206. The plurality of emission entities and the associated emission entity attributes are generated based on a dynamic external source.
In an embodiment, the dynamic external source can be a master data of the emission entities obtained a plurality of sources associated with emission entities. The dynamic external source comprises information associated with the entity attributes including an entity type, an entity properties, an entity age etc – all of which is associated with the entity. The information obtained from the dynamic external source has to be uniquely linked to the emission entity.
At step 308 of the method 300, an emission intensity is computed for each entity using the activity level and the plurality of emission entities in the emission intensity module 208. The emission intensity is computed based on an emission intensity computation technique.
In an embodiment, the emission intensity computation technique is expressed as shown below:
Emission intensity = (Emission in Kg ?CO?_2 by the plurality of emission entities )/(activity level ) --- (1)
The emission intensity computation technique is performed based on the activity level of each entity and the emission associated with the activity level of the entity for a pre-defined timeframe.
At step 310 of the method 300, the plurality of entities is clustered based on the emission intensity and the emission entity attributes in the clusterer 210. The plurality of entities is clustered using a clustering technique to obtain a plurality of entity clusters, wherein the centroid of each cluster is the emission intensity attribute of the entity.
In an embodiment, the clustering techniques includes a K-Means clustering technique. The clustering technique uses the associated emission entity attributes for the plurality of emission entities to group them into clusters. Various attributes used for example to cluster the plurality of emission entities (vehicles) - Vehicle code, vehicle type, on road / offroad, make of vehicle, model of vehicle, year of manufacture , type of engine, engine mileage rating, Emission Intensity.
At step 312 of the method 300, each of the plurality of entity clusters is classified in the classifier 212. The clustered plurality of entities is classified as one of:
(a) minimum emission intensity cluster, and
(b) maximum emission intensity cluster.
In an embodiment, the plurality of entity clusters is classified based on the centroids of each cluster using a ranking technique. The clustering is performed based on the centroids of each cluster, wherein the centroids indicate the emission intensity. Based on the ranking technique the cluster with a “minimum value” of the centroid/ emission intensity will be classified as the minimum emission intensity cluster and the cluster with a “maximum value” of the centroid/ emission intensity will be classified as the maximum emission intensity cluster.
At step 314 of the method 300, a total quantum of emission reduction is determined in the emission reduction determiner 214. The emission reduction technique is explained using the flowchart in method 400 of FIG.4.
At step 402 of the method 400, a difference between the centroid of the minimum emission intensity clusters and the centroid of the maximum emission intensity clusters is computed.
The difference between the centroids of the minimum emission intensity cluster and the maximum emission intensity cluster is computed. The difference is computed for all the minimum emission intensity cluster with respect to the maximum emission intensity cluster, as shown below:
Diff = Centroid (maximum emission intensity cluster)
– Centroid (minimum emission intensity cluster) --- (2)
At step 404 of the method 400, the difference of the centroids is multiplied with the activity levels of each of the minimum emission intensity clusters to obtain a quantum of emission reduction for each cluster.
In an embodiment, the difference of the centroids is multiplied with activity levels of each emission entity in the minimum emission intensity cluster (the activity levels will be fuel consumed, distance travelled and so on by each entity in the above example) :
Possible Reduction in emission from a minimum emission intensity cluster=
(Activity level of each emission entity of in a maximum emission intensity cluster)
x Diff --- (3)

Possible Reduction in emission from a maximum emission intensity cluster in = ?_1^n¦?(activity level of each emission enity)×Diff? --- (4)

At step 406 of the method 400, the total quantum of emission reduction is determined based on summation of the quantum of emission reduction of the minimum emission intensity clusters and the maximum emission intensity clusters.
In an embodiment, the product is added for all the clusters to arrive at the possible quantum of reduction possible.
Total reduction possible in emissions = ?_1^n¦?Pred(Cluster i)? ---- (5)
At step 316 of the method 300, an emission reduction potential is recommended in the emission reduction potential recommender 216. The emission reduction potential is recommended by identifying an attribute, wherein an attribute from the plurality of attributes is identified by computing an attribute difference.
In an embodiment, the plurality of entity in the clusters will have some common attributes like year of manufacture, type of fuel, last serviced date, type of vehicle etc. The attribute difference is computed as a difference of the plurality of attributes associated with the minimum emission intensity clusters and the maximum emission intensity clusters based on a pre-defined attribute threshold. The attribute difference is computed in several steps as shown below.
The average value of each attribute of the cluster is computed as MODE (Attribute of emission entities). Considering an example scenario of three clusters as shown below :
Cluster 1 Attribute 1 Attribute 2 Attribute 3 Attribute 4 Attribute 5 Attribute 6 Attribute 7

Cluster 2 Attribute 1 Attribute 2 Attribute 3 Attribute 4 Attribute 5 Attribute 6 Attribute 7

Cluster 3 Attribute 1 Attribute 2 Attribute 3 Attribute 4 Attribute 5 Attribute 6 Attribute 7

The average values of attributes for each cluster is computed and further a pair is created between attributes of different clusters to compute the attribute difference as shown below:
Cluster 1(Attribute 1) – Cluster 2 (Attribute 1)
Cluster 1(Attribute2) – Cluster 2 (Attribute 2)
Cluster 1(Attribute 3) – Cluster 2 (Attribute 3)
Cluster 1(Attribute 4) – Cluster 2 (Attribute 4)
Cluster 1(Attribute 5) – Cluster 2 (Attribute 5)
Cluster 1(Attribute 6) – Cluster 2 (Attribute 6)
Cluster 1(Attribute 7) – Cluster 2 (Attribute 7)
Cluster 1(Attribute 8) – Cluster 2 (Attribute 8)
Cluster 1(Attribute 1) – Cluster 3 (Attribute 1)
Cluster 1(Attribute2) – Cluster 3 (Attribute 2)
Cluster 1(Attribute 3) – Cluster 3(Attribute 3)
Cluster 1(Attribute 4) – Cluster 3(Attribute 4)
Cluster 1(Attribute 5) – Cluster 3(Attribute 5)
Cluster 1(Attribute 6) – Cluster 3 (Attribute 6)
Cluster 1(Attribute 7) – Cluster 3(Attribute 7)
Cluster 1(Attribute 8) – Cluster 3(Attribute 8)
Based on the attribute difference - For a particular cluster pair if the difference of one or more attributes is beyond a pre-defined attribute threshold set then that attribute would be recommended as the possible reason for minimum emission intensity cluster. If the same attributes are seen as showing a difference more than a pre-defined attribute threshold across all cluster pairs then that attribute will be the dominant reason for increased emissions. The attribute emission reduction potential is recommended as the attribute, wherein the emission potential can be reduced by optimizing the attribute.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
This disclosure relates generally to emission reduction and, more particularly, to recommending an emission reduction potential. The environment faces a long-term challenge because of global warming due to greenhouse gases. The effect of greenhouse gases on the environment/climate and health are immense. The existing techniques for emission reduction perform emission monitoring to recommend possible reduction solution, however possible reduction recommendations do not explicitly consider the profile of assets that are responsible for the emission. The disclosed technique recommends an emission reduction potential based on profile of entity/asset using several techniques comprising emission intensity computation technique, clustering technique, emission reduction technique to identify a attribute of the asset that would result in potential emission reduction.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. 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., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.
, Claims:We Claim:
1. A processor implemented method (300), comprising:
receiving a plurality of inputs, via one or more hardware processors, wherein the plurality of inputs are associated with a plurality entities producing emissions, wherein the plurality of inputs includes a plurality of attributes of each entity from the plurality of entities, an activity level of each entity, and an emission associated with the activity level of the entity (302);
pre-processing the plurality of inputs, via the one or more hardware processors, using a set of pre-processing techniques to obtain the set of pre-processed inputs (304);
generating a plurality of emission entities and the associated emission entity attributes, via the one or more hardware processors, for the set of pre-processed inputs based on a dynamic external source (306);
computing an emission intensity for each entity, via the one or more hardware processors, using the activity level and the plurality of emission entities based on an emission intensity computation technique (308);
clustering the plurality of entities to obtain a plurality of entity clusters, via the one or more hardware processors, based on the emission intensity and the emission entity attributes, using a clustering technique, wherein centroid of each of the entity cluster is the emission intensity attribute of the entity (310);
classifying each of the plurality of entity clusters as one of: (a) minimum emission intensity cluster, and (b) maximum emission intensity cluster, via the one or more hardware processors, based on the centroids of each cluster using a ranking technique (312);
determining a total quantum of emission reduction, via the one or more hardware processors, for the clustered plurality of entities using an emission reduction technique (314); and
recommending an emission reduction potential, via the one or more hardware processors, wherein an attribute from the plurality of attributes is identified by computing an attribute difference (316).

2. A method as claimed in claim 1, wherein the emission intensity computation technique is performed based on the activity level of each entity and the emission associated with the activity level of the entity for a pre-defined timeframe.

3. A method as claimed in claim 1, wherein the clustering technique comprises a K-Means clustering Technique, a set of modeling techniques including a Gaussian Mixture Model algorithm and a balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm.

4. A method as claimed in claim 1, wherein each of the plurality of entities is classified based on the emission intensity associated with the centroid of each entity, wherein the minimum emission intensity cluster is an efficient cluster, and the maximum emission intensity cluster is a least efficient cluster.

5. A method as claimed in claim 1, wherein the emission reduction technique (400) comprises:
computing a difference between the centroid of the minimum emission intensity clusters and the centroid of the maximum emission intensity clusters (402);
multiplying the difference of the centroids with the activity levels of each of the minimum emission intensity clusters to obtain a quantum of emission reduction for each cluster (404); and
determining the total quantum of emission reduction based on summation of the quantum of emission reduction of the minimum emission intensity clusters and the maximum emission intensity clusters (406).

6. A method as claimed in claim 1, wherein the attribute difference is computed as a difference of the plurality of attributes associated with the minimum emission intensity clusters and the maximum emission intensity clusters based on a pre-defined attribute threshold.

7. A system (100), comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
receive a plurality of inputs, via one or more hardware processors, wherein the plurality of inputs are associated with a plurality entities producing emissions, wherein the plurality of inputs includes a plurality of attributes of each entity from the plurality of entities, an activity level of each entity, and an emission associated with the activity level of the entity;
pre-process the plurality of inputs, via the one or more hardware processors, using a set of pre-processing techniques to obtain the set of pre-processed inputs;
generate a plurality of emission entities and the associated emission entity attributes, via the one or more hardware processors, for the set of pre-processed inputs based on a dynamic external source;
compute an emission intensity for each entity, via the one or more hardware processors, using the activity level and the plurality of emission entities based on an emission intensity computation technique;
cluster the plurality of entities to obtain a plurality of entity clusters, via the one or more hardware processors, based on the emission intensity and the emission entity attributes, using a clustering technique, wherein centroid of each of the entity cluster is the emission intensity attribute of the entity;
classify each of the plurality of entity clusters as one of: (a) minimum emission intensity cluster, and (b) maximum emission intensity cluster, via the one or more hardware processors, based on the centroids of each cluster using a ranking technique;
determine a total quantum of emission reduction, via the one or more hardware processors, for the clustered plurality of entities using an emission reduction technique; and
recommend an emission reduction potential, via the one or more hardware processors, wherein an attribute from the plurality of attributes is identified by computing an attribute difference.

8. The system as claimed in claim 7, wherein the emission intensity computation technique is performed based on the activity level of each entity and the emission associated with the activity level of the entity for a pre-defined timeframe.

9. A system as claimed in claim 7, wherein the clustering technique comprises a K-Means clustering Technique, a set of modeling techniques including a Gaussian Mixture Model algorithm and a balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm.

10. A system as claimed in claim 7, wherein each of the plurality of entities is classified based on the emission intensity associated with the centroid of each entity, wherein the minimum emission intensity cluster is an efficient cluster, and the maximum emission intensity cluster is a least efficient cluster.

11. A system as claimed in claim 7, wherein the emission reduction technique comprises:
computing a difference between the centroid of the minimum emission intensity clusters and the centroid of the maximum emission intensity clusters;
multiplying the difference of the centroids with the activity levels of each of the minimum emission intensity clusters to obtain a quantum of emission reduction for each cluster; and
determining the total quantum of emission reduction based on summation of the quantum of emission reduction of the minimum emission intensity clusters and the maximum emission intensity clusters.

12. A system as claimed in claim 7, wherein the attribute difference is computed as a difference of the plurality of attributes associated with the minimum emission intensity clusters and the maximum emission intensity clusters based on a pre-defined attribute threshold.

Dated this 9th Day of August 2023

Tata Consultancy Services Limited
By their Agent & Attorney

(Adheesh Nargolkar)
of Khaitan & Co
Reg No IN-PA-1086