METHOD AND SYSTEM FOR DETERMINING POTENTIAL FOR ENERGY USAGE IMPROVEMENTS IN BUILT ENVIRONMENT

1 . A method for calculating a probabilistic score to determine a potential for improvements in energy consumption inside a built environment, the method comprising:

collecting a first set of statistical data associated with a plurality of energy consuming devices present in the built environment, wherein the first set of statistical data comprises a current energy consumption data associated with each of the plurality of energy consuming devices and a past energy consumption data associated with the each of the plurality of energy consuming devices, wherein the first set of statistical data being collected based on a first plurality of parameters and wherein the first set of statistical data being collected in real time;

receiving a second set of statistical data associated with each of a plurality of users present inside the built environment, wherein the second set of statistical data being received based on a second plurality of parameters and wherein the second set of statistical data being received in the real time;

comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data for determining the potential for improvement in the energy consumption of each of the plurality of energy consuming devices present inside the built environment wherein the current energy consumption data and the past energy consumption data being compared in the real time; and

calculating the probabilistic score for determining the potential for improvements in the energy consumption of the built environment, wherein the probabilistic score being calculated for providing a rating to each of the plurality of energy consuming devices present in the built environment, wherein the calculation of the probabilistic score being performed by deriving a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment.

2 . The method as recited in claim 1 , wherein the x-y intercept denotes the energy consumption of each of the plurality of the energy consuming devices.

3 . The method as recited in claim 1 , wherein the probabilistic score being improved by an application of a learning algorithm, wherein the application of learning algorithm comprises recording of the first set of statistical data pertaining to each of the plurality of energy consuming devices and an operating behavior pertaining to the plurality of users, wherein the operating behavior being recorded based on a type of the built environment, a physical location, duration of energy usage for each of plurality of portable communication devices associated with the plurality of users.

4 . The method as recited in claim 1 , further comprising segregating the first set of statistical data by creating one or more fields pertaining to the energy consumption of each of the plurality of energy consuming devices, wherein the one or more fields being created based on one or more operating characteristics and one or more physical characteristics associated with the plurality of energy consuming devices, wherein the one or more operating characteristics comprises an operating voltage, a running load amperage, a full load amperage, a wattage, a voltage, a frequency, the temperature and a flow rate and wherein the one or more physical characteristics comprises size, dimension, packaging and shape of each of the plurality of energy consuming devices present in the built environment.

5 . The method as recited in claim 1 , wherein the plurality of sources of the past energy consumption data and weather conditions comprises a plurality of external application programming interfaces and third party databases.

6 . The method as recited in claim 4 , wherein the plurality of energy consuming devices comprises a plurality of electrical devices and a plurality of portable communication devices present inside the built environment and wherein the first set of statistical data being collected manually and electronically.

7 . The method as recited in claim 1 , wherein the first plurality of parameters comprises the current energy consumption data pertaining to each of the plurality of energy consuming devices, a physical location, a duration of the energy usage by each of the plurality of energy consuming devices, a seasonal variation in the energy consumption and an off-seasonal variation in the energy consumption;

8 . The method as recited in claim 1 , wherein the second plurality of parameters comprises an occupancy behavior of the plurality of users, an energy consuming pattern of a corresponding energy consuming device associated with each of the plurality of users, the physical location of the each of the plurality of users and the duration of the energy usage of the corresponding energy consuming device associated with each of the plurality of users present in the built environment.

9 . The method as recited in claim 1 , further comprising storing the first set of statistical data, the second set of statistical data, and the probabilistic score, wherein the first set of statistical data, the second set of statistical data and the probabilistic score being stored in the real time.

10 . The method as recited in claim 1 , further comprising updating the first set of statistical data, the second set of statistical data and the probabilistic score.

11 . A computer system comprising:

one or more processors; and

a memory coupled to the one or more processors, the memory for storing instructions which, when executed by the one or more processors, cause the one or more processors to perform a method for calculating a probabilistic score to determine a potential for improvements in energy consumption inside a built environment, the method comprising:

collecting a first set of statistical data associated with a plurality of energy consuming devices present in the built environment, wherein the first set of statistical data comprises a current energy consumption data associated with each of the plurality of energy consuming devices and a past energy consumption data associated with the each of the plurality of energy consuming devices, wherein the first set of statistical data being collected based on a first plurality of parameters and wherein the first set of statistical data being collected in real time;

receiving a second set of statistical data associated with each of a plurality of users present inside the built environment, wherein the second set of statistical data being received based on a second plurality of parameters and wherein the second set of statistical data being received in the real time;

comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data for determining the potential for improvement in the energy consumption of each of the plurality of energy consuming devices present inside the built environment wherein the current energy consumption data and the past energy consumption data being compared in the real time; and

calculating the probabilistic score for determining the potential for improvements in the energy consumption of the built environment, wherein the probabilistic score being calculated for providing a rating to each of the plurality of energy consuming devices present in the built environment, wherein the calculation of the probabilistic score being performed by deriving a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment.

12 . The computer system as recited in claim 11 , further comprising segregating the first set of statistical data by creating one or more fields pertaining to the energy consumption of each of the plurality of energy consuming devices, wherein the one or more fields being created based on one or more operating characteristics and one or more physical characteristics associated with the plurality of energy consuming devices, wherein the one or more operating characteristics comprises an operating voltage, a running load amperage, a full load amperage, a wattage, a voltage, a frequency, the temperature and a flow rate and wherein the one or more physical characteristics comprises size, dimension, packaging and shape of each of the plurality of energy consuming devices present in the built environment.

13 . The computer system as recited in claim 11 , further comprising storing the first set of statistical data, the second set of statistical data, and the probabilistic score, wherein the first set of statistical data, the second set of statistical data and the probabilistic score being stored in the real time.

14 . The computer system as recited in claim 11 , further comprising updating the first set of statistical data, the second set of statistical data and the probabilistic score.

15 . A computer-readable storage medium encoding computer executable instructions that, when executed by at least one processor, performs a method for calculating a probabilistic score to determine a potential for improvements in energy consumption inside a built environment, the method comprising:

collecting a first set of statistical data associated with a plurality of energy consuming devices present in the built environment, wherein the first set of statistical data comprises a current energy consumption data associated with each of the plurality of energy consuming devices and a past energy consumption data associated with the each of the plurality of energy consuming devices, wherein the first set of statistical data being collected based on a first plurality of parameters and wherein the first set of statistical data being collected in real time;

receiving a second set of statistical data associated with each of a plurality of users present inside the built environment, wherein the second set of statistical data being received based on a second plurality of parameters and wherein the second set of statistical data being received in the real time;

comparing the current energy consumption data of the first set of statistical data with the past energy consumption data of the first set of statistical data for determining the potential for improvement in the energy consumption of each of the plurality of energy consuming devices present inside the built environment wherein the current energy consumption data and the past energy consumption data being compared in the real time; and

calculating the probabilistic score for determining the potential for improvements in the energy consumption of the built environment, wherein the probabilistic score being calculated for providing a rating to each of the plurality of energy consuming devices present in the built environment, wherein the calculation of the probabilistic score being performed by deriving a relationship formula for finding an x-y intercept value for a series of plots with temperature and the energy consumption associated with the built environment.

16 . The computer program product as recited in claim 11 , wherein the x-y intercept denotes the energy consumption of each of the plurality of the energy consuming devices.

17 . The computer-readable storage medium as recited in claim 16 , wherein the probabilistic score being improved by an application of a learning algorithm, wherein the application of learning algorithm comprises recording of the first set of statistical data pertaining to each of the plurality of energy consuming devices and an operating behavior pertaining to the plurality of users, wherein the operating behavior being recorded based on a type of the built environment, a physical location, duration of energy usage for each of plurality of portable communication devices associated with the plurality of users.

18 . The computer-readable storage medium as recited in claim 16 , further comprising instructions for segregating the first set of statistical data by creating one or more fields pertaining to the energy consumption of each of the plurality of energy consuming devices, wherein the one or more fields being created based on one or more operating characteristics and one or more physical characteristics associated with the plurality of energy consuming devices, wherein the one or more operating characteristics comprises an operating voltage, a running load amperage, a full load amperage, a wattage, a voltage, a frequency, the temperature and a flow rate and wherein the one or more physical characteristics comprises size, dimension, packaging and shape of each of the plurality of energy consuming devices present in the built environment.

19 . The computer-readable storage medium as recited in claim 16 , further comprising instructions for storing the first set of statistical data, the second set of statistical data, and the probabilistic score, wherein the first set of statistical data, the second set of statistical data and the probabilistic score being stored in the real time.

20 . The computer-readable storage medium as recited in claim 16 , further comprising instructions for updating the first set of statistical data, the second set of statistical data and the probabilistic score.