1. A method for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) 109 in a structure 101, the method comprising: receiving, by an energy management system 103 associated with the structure 101, internal weather data 207 and external weather data 209 associated with the structure 101 from one or more sensors 105 configured in the structure 101 and one or more data sources 107 respectively; detecting, by the energy management system 103, thermal load distributed in one or more locations in the structure 101 based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure 101 and status of each of one or more opening components in the structure 101; identifying, by the energy management system 103, a HVAC setpoint for the one or more locations in the structure 101 based on the internal weather data 207, the external weather data 209, and the thermal load distribution; identifying, by the energy management system 103, a transition time required for the HVAC 109 to shift from a current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data 209 and the real-time data; and controlling, by the energy management system 103, the HVAC 109 based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC 109 in the structure 101.

2. The method as claimed in claim 1, wherein the reference HVAC setpoint indicates optimal energy usage in the structure 101.

3. The method as claimed in claim 1, wherein the reference HVAC setpoint is varied based on one of comfort of users in the structure 101 or energy optimization.

4. The method as claimed in claim 1, wherein controlling the HVAC 109 comprises one of activating or deactivating the HVAC 109 or varying the reference HVAC setpoint based on the identified transition time and the current HVAC setpoint.

5. The method as claimed in claim 4, wherein the HVAC 109 is activated when the HVAC setpoint is less than temperature inside the structure 101 for cooling condition in the structure 101.

6. The method as claimed in claim 4, wherein the HVAC 109 is deactivated based on the transition time when the HVAC setpoint is greater than temperature inside the structure 101 for cooling condition in the structure 101.

7. The method as claimed in claim 4, wherein the HVAC 109 is deactivated based on the transition time when the HVAC setpoint is less than temperature inside the structure 101 and for heating condition in the structure 101.

8. The method as claimed in claim 4, wherein the HVAC 109 is activated when the HVAC setpoint is greater than temperature inside the structure 101 for heating condition in the structure 101.

9. The method as claimed in claim 4, wherein the reference HVAC setpoint is varied based on heating and cooling condition inside the structure 101.

10. The method as claimed in claim 1, wherein the HVAC setpoint is determined by a machine learning model which is trained using one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, cost parameter associated with the energy consumption of HVAC 109 and comfort parameter associated with occupants in the structure 101.

11. The method as claimed in claim 1, wherein the transition time is identified by a machine learning model which is trained using the one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, transition time to shift from one HVAC setpoint to another HVAC setpoint and time interval for which the HVAC 109 is one of activated or deactivated.

12. An energy management system 103 for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) 109 in a structure 101, the energy management system 103 comprising: a processor 203; and a memory 205 communicatively coupled to the processor 203, wherein the memory 205 stores processor-executable instructions, which, on execution, causes the processor 203 to: receive internal weather data 207and external weather data 209 associated with the structure 101 from one or more sensors 105 configured in the structure 101 and one or more data sources 107 respectively; detect thermal load distributed in one or more locations in the structure 101 based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure 101 and status of each of one or more opening components in the structure 101; identify a current HVAC setpoint for each of the one or more locations in the structure 101 based on the internal weather data 207, the external weather data 209, and the thermal load distribution; identify a transition time required for the HVAC 109 to shift from the current HVAC setpoint to a reference HVAC setpoint of the HVAC 109 in the one or more locations based on the external weather data 209 and the real-time data; and control the HVAC 109 based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC 109 in the structure 101.

13. The energy management system 103 as claimed in claim 12, wherein the reference HVAC setpoint indicates optimal energy usage in the structure 101.

14. The energy management system 103 as claimed in claim 12, wherein the reference HVAC setpoint is varied based on one of comfort of users in the structure 101 or energy optimization.

15. The energy management system 103 as claimed in claim 12, wherein processor 203 controls the HVAC 109 by performing one of activating or deactivating the HVAC 109 or varying the reference HVAC setpoint based on the identified transition time and the current HVAC setpoint.

16. The energy management system 103 as claimed in claim 15, wherein the processor 203 activates the HVAC 109 when the HVAC setpoint is less than temperature inside the structure 101 for cooling condition in the structure 101.

17. The energy management system 103 as claimed in claim 15, wherein the processor 203 deactivates the HVAC 109 based on the transition time when the HVAC setpoint is greater than temperature inside the structure 101 for cooling condition in the structure 101.

18. The energy management system 103 as claimed in claim 15, wherein the processor 203 deactivates the HVAC 109 based on the transition time when the HVAC setpoint is less than temperature inside the structure 101 for heating condition in the structure 101.

19. The energy management system 103 as claimed in claim 15, wherein the processor 203 activates the HVAC 109 when the HVAC setpoint is greater than temperature inside the structure 101 for heating condition in the structure 101.

20. The energy management system 103 as claimed in claim 15, wherein the processor 203 varies the reference HVAC setpoint based on heating and cooling condition inside the structure 101.

21. The energy management system 103 as claimed in claim 12, wherein the processor 203 determines the reference HVAC setpoint using a machine learning model which is trained using one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, cost parameter associated with the energy consumption of HVAC 109 and comfort parameter associated with occupants in the structure 101.

22. The energy management system 103 as claimed in claim 12, wherein the processor 203 identifies the transition time using a machine learning model which is trained using the one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure 101 information, transition time to shift from one HVAC setpoint to another HVAC setpoint and time interval for which the HVAC 109 is one of activated or deactivated. , Description:TECHNICAL FIELD The present subject matter generally relates to energy management. More particularly, but not exclusively, the present disclosure discloses a method and a system for optimizing energy consumption of a Heating Ventilation and Air Conditioning (HVAC) in a structure. BACKGROUND Energy management is the process of tracking and optimizing energy consumption to conserve usage in a building. Particularly, energy management of Heating, Ventilation and Cooling (HVAC) has become a primary concern for building facility managers. In commercial buildings, HVAC systems may consume up to 40% energy consumption. Thus, existing systems have proposed to reduce energy consumption of HVAC using various techniques on energy savings without compromising comfort of occupants. One such existing technique includes sensing a real-time occupancy of occupants in an enclosed area of the building using a camera. However, this existing technique do not consider natural thermal characteristics of the building. Traditionally, various Building Energy Management Systems (BEMS) or Building Management System (BMS) or Building Automation Systems (BAS) rely on inaccurate occupancy sensors, which hinder the responsiveness of automation systems. For example, passive infrared and ultra-sound occupancy sensors produce poor accuracy, because they are unable to determine the occupancy state adequately when occupants remain stationary for a prolonged period. They also have a limited range of operation which hinders their performance, especially in a large area. In one of the existing prior arts, the system facilitates data-driven HVAC optimization for outputting HVAC setpoints to a thermostat. The system determines a predicted HVAC usage of HVAC unit based on the HVAC setpoints and an outdoor temperature. The system further determines and outputs a predicted cost of the HVAC unit for heating or cooling a structure based on the HVAC setpoints. The system further selects optimized HVAC setpoints such that the predicted cost is less than or equal to a user defined cost constraint and minimize deviation from user preferred HVAC setpoints. The method of predicting the HVAC usage during equilibrium, ramp up, and ramp down is reasonably accurate as disclosed in this existing prior art when HVAC load demand is high (for example, the cooling load in the summer or the heating load in winter). However, at other times, when the demand of HVAC operation is sparse, there is insufficient data to determine the HVAC usage using a linear relationship. Also, there is a need for full building structure information to predict or retrieve HVAC setpoint and linear transition time to ramp up or ramp down which is not considered in the existing system. The building transition time do depend on common spaces and add non-linearity in ramp up or ramp down time of the HVAC due to its dependencies on thermal conduction and convection losses. Further, the existing system also fails to consider information on real-time thermal load, like occupancy, door / window status or shade where the linear ramp up or ramp down relation might fail due to two common spaces occupancies. This may result in further non-linearity to transition time and hence affect optimization in energy consumption. The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art. SUMMARY Disclosed herein is a method for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure. The method comprises receiving, by an energy management system associated with the structure, internal weather data and external weather data associated with the structure from one or more sensors configured in the structure and one or more data sources, respectively. Thereafter, the method comprises detecting, by the energy management system, thermal load distributed in one or more locations in the structure based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure. Once the thermal load distribution is identified, the method comprises identifying a HVAC setpoint for the one or more locations in the structure based on the internal weather data, the external weather data, and the thermal load distribution. Thereafter, the method comprises identifying a transition time required for the HVAC to shift from a current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data and the real-time data. Further, the method comprises controlling the HVAC based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC in the structure. Further, the present disclosure discloses a system for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure. The energy management 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 receive internal weather data and external weather data associated with the structure from one or more sensors configured in the structure and one or more data sources respectively. The processor detects thermal load distributed in one or more locations in the structure based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure. Thereafter, the processor identifies a current HVAC setpoint for each of the one or more locations in the structure based on the internal weather data, the external weather data, and the thermal load distribution. Once the current HVAC setpoint is identified, the processor identifies a transition time required for the HVAC to shift from the current HVAC setpoint to a reference HVAC setpoint of the HVAC in the one or more locations based on the external weather data and the real-time data. Further, the processor is configured to control the HVAC based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC in the structure. 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 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 illustrates an exemplary environment for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure in accordance with some embodiments of the present disclosure. Fig.2 shows a exemplary block diagram of an energy management system in accordance with some embodiments of the present disclosure. Fig.3a shows an exemplary structure with one or more locations for controlling HVAC in accordance with some embodiments of the present disclosure. Fig.3b shows a graph illustrating exemplary HVAC setpoint and the desired temperature bandwidth to be maintained in the location in accordance with some embodiments of the present disclosure. Fig.3c shows an exemplary environment illustrating a method for training a machine learning model in accordance with some embodiments of the present disclosure. Fig.3d shows an exemplary HVAC database and the process of controlling the HVAC in accordance with some embodiments of the present disclosure. Fig.3e illustrates an exemplary method for activating and deactivating HVAC for energy optimization in accordance with some embodiments of the present disclosure. Fig.4 shows a flowchart illustrating a method for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure in accordance with some embodiments of the present disclosure. 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 specific forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure. The terms “comprises”, “comprising”, “includes”, “including” 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 management system [also referred as system] for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure. The system receives internal weather data and external weather data associated with the structure from one or more sensors configured in the structure and one or more data sources, respectively. As an example, but not limited to, the structure may be an office building, a residential establishment, or a non-residential establishment. The system may detect thermal load distributed in one or more locations in the structure based on a real-time data. The real-time data is associated with number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure. The one or more opening components may include, but not limited to, door and window. Further, the system may identify a HVAC setpoint for the one or more locations in the structure based on the internal weather data indicative of weather condition inside the structure, the external weather data indicative of weather condition outside the structure, and the thermal load distribution. The HVAC setpoint may refer to parameter change which drives the HVAC to cool or heat. The system also identifies a transition time required for the HVAC to shift from a current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data and the real-time data. The reference HVAC setpoint is determined by a machine learning model which is trained using one or more parameters comprising at least one of internal weather data, external weather data, thermal load distributed in the one or more locations in the structure, structure information, cost parameter associated with the energy consumption of HVAC and comfort parameter associated with occupants in the structure. The transition time is identified by a machine learning model which is trained using the one or more parameters comprising at least one of internal weather data, external weather data, thermal load distributed in the one or more locations in the structure, structure information, transition time to shift from one HVAC setpoint to another HVAC setpoint detected for controlling the HVAC while training the machine learning model and time interval for which the HVAC is one of activated or deactivated. Based on the identified transition time and the current HVAC setpoint, the system controls the HVAC for optimizing the energy consumption of the HVAC in the structure. Controlling the HVAC comprises one of activating or deactivating the HVAC or varying the reference HVAC setpoint based on the identified transition time and the current HVAC setpoint. The HVAC is activated when the HVAC setpoint is less than temperature inside the structure for a cooling condition in the structure and the HVAC is deactivated based on the transition time when the HVAC setpoint is greater than temperature inside the structure for a cooling condition in the structure. Similarly, the HVAC is deactivated based on the transition time when the HVAC setpoint is less than temperature inside the structure for a heating condition in the structure and the HVAC is activated when the HVAC setpoint is greater than temperature inside the structure for a heating condition in the structure. In this manner, the present disclosure proposes a method and a system for optimizing energy consumption in the structure by considering real time data such as number of occupants and appliances present in the one or more locations in the structure, status of each of one or more opening components in the structure and the transition time required for the HVAC to shift from current HVAC setpoint to a reference HVAC setpoint. The present disclosure may be implemented in individual or connected buildings and for residential, commercial/industrial applications. 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 of 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 illustrates an exemplary environment for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure in accordance with some embodiments of the present disclosure. As shown in Fig.1, the environment 100 may include an energy management system 103, one or more sensors 105, one or more data sources 107, a structure 101, and a HVAC. In one implementation, the energy management system 103, the one or more sensors 105 and the HVAC is configured in the structure 101. As an example, but not limited to, the structure 101 may be an office building. The energy management system 103 may receive internal weather data associated with the structure 101 from the one or more sensors 105 configured in the structure 101 and may receive external weather data associated with the structure 101 from one or more data sources 107. As an example, the internal weather data may include, but not limited to, data associated with temperature inside the structure 101, humidity inside the structure 101 and CO2 level inside the structure 101. The one or more sensors 105 may include, but not limited to, temperature detection sensor, humidity detection sensor, CO2 detection sensor and a camera. The external weather data may be detected using one or more data sources 107 such as open source Application Programming Interface (API). The external weather data may also be detected using one or more sensors configured in the structure 101 which may include, but not limited to, thermometer for measuring temperature, anemometer for measuring wind speed, wind vane for measuring wind direction, hygrometer for measuring humidity and barometer for measuring atmospheric pressure. Further, the energy management system 103 may detect thermal load distributed in one or more locations in the structure 101 based on real-time data. As an example, the structure 101 may have three locations. The real-time data is associated with number of occupants and appliances present in each of the three locations and status of each of one or more opening components in the structure 101. As an example, the one or more opening components, may include, but not limited to, a door or a window. The information about the number of occupants, appliances, and the status of one or more opening components may be identified using the camera. Based on the real-time data, the thermal load in each of the one or more locations in the structure 101 is detected by the energy management system 103. In an embodiment, the energy management system 103 identifies HVAC setpoint based on the internal weather data, external weather data and the thermal load distribution. The HVAC setpoint is any parameter related to HVAC which may include, but not limited to, temperature, humidity and air volume. The present disclosure develops thermal models dynamically for forecasting building temperature or HVAC setpoint with respect to change in external weather conditions using the building structure 101. In an embodiment, the energy management system 103 may also consider one or more parameters associated with architecture of the structure 101 such as ventilation in the structure 101, solar loss in the structure 101, fabric loss in the structure 101, conduction, and radiation of the structure 101 for identifying the HVAC setpoint. The identified HVAC setpoint is recorded as the current HVAC setpoint in the energy management system 103. Once the HVAC setpoint is identified, the energy management system 103 may identify a transition time required for the HVAC to shift from the current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data and the real-time data. The transition time identified is the linear transition time and is identified using machine learning model. The machine learning model is trained using the one or more parameters comprising at least one of internal weather data, external weather data, thermal load distributed in the one or more locations in the structure 101, structure information, transition time to shift from one HVAC setpoint to another HVAC setpoint and time interval for which the HVAC is one of activated or deactivated. The reference HVAC setpoint indicates at which a HVAC must be operated to achieve optimal energy usage in the structure 101. The reference HVAC setpoint may be varied based on one of comfort of users in the structure 101 or energy optimization by a facility manager of the structure 101. As an example, the facility manager may decide to maintain the reference HVAC setpoint based on energy optimization from 9 am to 12 PM of a day and maintain the reference HVAC setpoint based on comfort of the user during 1-3pm of the day. The reference HVAC setpoint may also be varied based on heating and cooling condition inside the structure 101. As an example, the current HVAC setpoint may be 24 degree and the reference HVAC setpoint may be 26 degree. The transition time may refer to the time required by the HVAC 109 to shift from 24 degree to 26 degree. In an embodiment, the energy management system 103 controls the HVAC 109 based on the transition time and the current HVAC setpoint. The controlling may be in terms of activating the HVAC 109, deactivating the HVAC 109, or varying the reference HVAC setpoint. For cooling condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC is activated since cooling is required inside the structure 101. But when the HVAC setpoint is greater than the temperature inside the structure 101, then the HVAC 109 may be deactivated. For heating condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is deactivated and when the HVAC setpoint is greater than the temperature inside the structure 101 then the HVAC 109 is activated. The present disclosure solves the issue of computation of linear transition time by identifying highly sensitive correlation vectors for identifying transition time using machine learning models. Based on the transition time, the HVAC 109 is controlled. The transition time concept facilitates extra benefit in terms of energy saving in the structure 101 and is resulting into cost saving. Fig.2 shows a block diagram of an energy management system in accordance with some embodiments of the present disclosure. As shown in Fig.2, the energy management system 103 may comprise an Input/Output (I/O) interface 201, a processor 203 and a memory 205. The I/O interface 201 may be configured to receive data 206 from one or more sensors 105 and one or more data sources 107. The processor 203 may be configured to perform one or more functions of the energy management system 103. In some implementations, the energy management system 103 may include data 206 and modules 218 for performing various operations in accordance with embodiments of the present disclosure. In an embodiment, the data 206 may be stored within the memory 205 and may include, internal weather data 207, external weather data 209, thermal load data 211, HVAC setpoint data 213, reference HVAC setpoint data 215 and other data 217. In some embodiments, the data 206 may be stored within the memory 205 in the form of various data structures. Additionally, the data 206 may be organized using data models, such as relational or hierarchical data models. The other data 217 may store data, including temporary data and temporary files, generated by the modules 218 for performing various functions of the energy management system 103. In an embodiment, one or more modules 218 may process the data of the energy management system 103. In one implementation, the one or more modules 218 may be communicatively coupled to the processor 203 for performing one or more functions of the energy management system 103. The modules 218 may include, without limiting to, a receiving module 219, a thermal load detection module 221, a HVAC setpoint identification module 223, a transition time identification module 225, a HVAC controlling module 227 and other modules 229. As used herein, the term module 218 refers to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor 203 (shared, dedicated, or group) and memory 205 that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In an embodiment, the other modules 229 may be used to perform various miscellaneous functionalities of the energy management system 103. It will be appreciated that such modules 218 may be represented as a single module or a combination of different modules 218. Furthermore, a person of ordinary skill in the art will appreciate that in an implementation, the one or more modules 218 may be stored in the memory 205, without limiting the scope of the disclosure. The said modules 218 when configured with the functionality defined in the present disclosure will result in a novel hardware. In an embodiment, the receiving module 219 may be configured to receive internal weather data 207 and external weather data 209 associated with the structure 101. The internal weather data 207 may be received from one or more sensors 105 such as temperature sensor configured in the structure 101, humidity sensor configured in the structure 101 and CO2 sensor configured in the structure 101 and the like. The external weather data 209 associated with the structure 101 may be received from one or more data sources 107 such as open source API’s and one or more sensors which may include, but not limited to, thermometer for measuring temperature, anemometer for measuring wind speed, wind vane for measuring wind direction, hygrometer for measuring humidity and barometer for measuring atmospheric pressure. In an embodiment, the thermal load detection module 221 may be configured to detect thermal load in each of one or more locations in the structure 101 which is stored as thermal load data 211. The thermal load is detected based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure 101 and status of each of one or more opening components in the structure 101. The one or more opening components may include, but not limited to, window and door. The information about number of occupants and appliances in the location and the information of whether the opening components are open or close is detected using image capturing sensors, for example cameras installed in the structure 101. In an embodiment, the thermal load for identified human count and appliances are computed based on common location wise distribution in the structure 101. The data set is prepared for forecasting internal temperature behavior inside the location for external weather conditions. The data set consists of all thermal load state variables, like human [occupant]/appliance count and their distribution inside the structure 101, HVAC parameters, event-oriented information such as open and close status of windows and doors and external weather conditions. The internal temperature predicted based on the correlation feature vectors shall be operated in the desired temperature bandwidth. Further, in addition to occupants count or occupancy recognition, occupancy (pattern) prediction is required to anticipate structure 101 usage and control pre-cooling or pre-heating in advance based on the environment. So, the occupancy pattern in the structure 101 may be identified for identifying when to activate or deactivate the HVAC 109. The occupancy pattern may be identified using a machine learning model which implements regression technique which is trained using number of occupants in the structure 101, thermal load distribution in the structure 101 on each day and sensor data. In an embodiment, the HVAC setpoint identification module 223 may be configured to identify the HVAC setpoint [as an example, internal temperature] for the one or more locations in the structure 101 based on the internal weather data 207, the external weather data 209, and the thermal load distribution. Since the aspect of thermal load distribution is considered for identifying the HVAC setpoint, the present disclosure is more accurate in controlling the HVAC 109 for energy optimization than the existing conventional mechanisms. The HVAC setpoint is determined by a machine learning model which is trained using one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, cost parameter associated with the energy consumption of HVAC 109 and comfort parameter associated with occupants in the structure 101. The HVAC setpoint identified is the current HVAC setpoint which is stored as HVAC setpoint data 213. In an embodiment, the transition time identification module 225 may be configured to identify transition time required for the HVAC 109 to shift from the current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data 209 and the real-time data. The reference setpoint is stored as reference HVAC setpoint data 215. The identified transition time is the non-linear transition time as compared to the linear transition time disclosed in the conventional mechanisms. As an example, the structure 101 may have one or more locations, location 1 3011, location 2 3012 and location 3 3013 as shown in Fig.3a. Each location may be configured with a camera 307 [3071, 3072, and 3073] and one or more sensors 105. The camera 307 may be configured to detect number of occupants 305 [3051, 3052 and 3053], appliances and status of opening components 303 [3031, 3032,3033] in each location. The one or more sensors 105 may be configured to detect, as an example, but not limited to, temperature inside the location and humidity inside the location. As an example, the objective is to operate internal temperature in location 1 3011 within 24 degree [minimum temperature balance point] to 28 degrees [maximum temperature balance point] which is the desired temperature [bandwidth] as shown in Fig.3b. As an example, 24 degrees may be considered as human comfort inside the structure 101 and 28 degrees may be considered as optimal energy usage. So, the desired bandwidth to operate the HVAC 109 lies between 24 degrees to 28 degrees. As an example, the current HVAC setpoint [indicated as “current internal temperature” in Fig.3b] in location 1 3011 may be 25 degrees. The optimal energy usage is at 28 degrees. So, the transition time required for the HVAC setpoint to shift from 25 degrees to 28 degrees is “Tr” which is identified by the transition time identification module 225. The slope indicated is the behavior of the internal temperature inside the structure 101. Since there is a raise in internal temperature from 25 degrees to 28 degree, the HVAC 109 is deactivated for the time “Tr”. As an example, it may take about 30 minutes for the HVAC 109 to reach 28 degrees temperature inside the location 1 3011. This time is identified by the transition time identification module 225. Hence, the HVAC 109 may be deactivated for 30 minutes and thus optimize the energy consumption. Similarly, if the internal temperature has to reach 25 degree from 28 degree, then the transition time identification module 225 identifies the transition time required for the HVAC 109 to shift from 28 degrees to 25 degrees which may be 1 Hour. Hence, the HVAC 109 may be activated for 1 Hour. In the present disclosure, the transition time of the HVAC 109 is identified within the defined bandwidth using real time thermal loads in the location 1 3011. So, whenever there is a change in the reference HVAC setpoint, the transition time identification module 225 identifies the transition time for the HVAC 109 to shift from the current HVAC setpoint to the reference HVAC setpoint. In an embodiment, the transition time identification module 225 may identify the transition time as a function of external weather data 209, internal weather data 207, thermal load, HVAC setpoint event information such as status of opening components. The transition time “Tr” is identified using the below equation 1. ____________Equation 1 Where, X1, X2…. Xn are number of locations inside the structure 101, The transition time slope depends on non-linear coefficients of location wise thermal convection and/or conduction. X1X2 is thermal convection and/or conduction with respect to location 1 3011 and location 3 3013; X1= Text -Location 1 3011 HVAC setpoint or internal temperature X2=Text-Location 2 3012 HVAC setpoint or internal temperature d_1, d_2, d_3 = Predefined constants with respect to each location. For example, d_1 is a predefined constant associated with location 1 3011 [X1], d_2 is a predefined constant associated with location 2 [X2] 3012 and d_3 is a predefined constant associated with location 1 3011 and location 3 [X3] 3013. Since location 1 3011 and location 3 3013 are interconnected, if a component such as door for example is open which is common to both location 1 3011 and location 3 3013, then the non-linearity factor or the constant is varied because the rise in temperature or cooling may take more time because of the common space and hence these factors have to be considered while identifying the transition time. In an embodiment, the transition time identification module 225 while computing the transition time considers the aspect of location wise thermal convection and/or conduction heat loss. The transition time identification module 225 estimates HVAC activation and deactivation transition time (Tr) based on the machine learning model 226 which is trained using one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, HVAC setpoint data 213, time interval of the day 311, transition time to shift from one HVAC setpoint to another HVAC setpoint as shown in Fig.3c. In an embodiment, the HVAC controlling module 227 is configured to control the HVAC 109 based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC 109 in the structure 101. Controlling the HVAC 109 comprises one of activating or deactivating the HVAC 109 or varying the reference HVAC setpoint based on the identified transition time and the current HVAC setpoint. For cooling condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is activated since cooling is required inside the structure 101. But when the HVAC setpoint is greater than the temperature inside the structure 101, then the HVAC 109 may be deactivated. For heating condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is deactivated and when the HVAC setpoint is greater than the temperature inside the structure 101 then the HVAC 109 is activated. In an embodiment, the energy management system 103 receives is associated with a HVAC database 231 which stores information associated with thermal load, number of occupants, window status, door status, external weather data, temperature data and transition time in minutes per degree as shown in Fig.3d. These information are stored location wise or zone wise. For example, Z1, Z2, Z3 and Z4 represents 4 zones or 4 locations in the structure 101. The information is updated in the HVAC database 231 at predefined time intervals such as every 5 minutes. This information is provided to the machine learning model 226 for training purpose. The input to the machine learning model 226 is the real time data such as number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure at present in each location or zone. Based on these inputs, the transition time identification module 225 may identify the transition time “Tr” which is required for the HVAC 109 to shift from current HVAC setpoint to reference HVAC setpoint. Based on the transition time and the current HVAC setpoint, the HVAC controlling module 227 may control the HVAC 109 for optimizing energy consumption. Fig.3e illustrates an exemplary method for activating and deactivating HVAC for energy optimization in accordance with some embodiments of the present disclosure. As shown in Fig.3e, the X-Axis represents time of a day and Y-Axis represents internal temperature in the structure 101. As an example, the internal temperature between 24 degree and 28 degree is the desired temperature bandwidth. 24 degree is minimum internal temperature, and 28 degree is the maximum internal temperature. T1 represents temperature inside the structure 101 to reach reference setpoint when HVAC 109 is activated. T2 represents the temperature inside the structure 101 to naturally reach the reference HVAC setpoint when the HVAC 109 is deactivated from activation state and T3 represents the temperature inside the structure 101 to reach the reference HVAC setpoint by changing the HVAC setpoint. As an example, the HVAC 109 may be activated on a particular day at 8:30 am. The time at which the HVAC 109 must be activated or deactivated each day is identified by a machine learning model which is trained based on sensor data and thermal load of the structure 101. As an example, at 9 am, the current internal temperature is at 28 degree and in next few minutes, the current internal temperature is moving away from the desired temperature bandwidth that is away from 28 degrees. The objective is to maintain the HVAC setpoint within the desired temperature bandwidth for optimum energy usage. Therefore, at what time the HVAC setpoint has to be changed to bring it back into the desired temperature bandwidth is provided by the transition time. So, till 12 PM, the HVAC setpoint is within the desired temperature bandwidth. In another exemplary scenario, from 12-1:30 PM there may be lesser number of people in the structure 101 as it may be lunch time for people in the structure 101 and hence during this time, the HVAC 109 may be deactivated. Therefore, the internal temperature may raise to 28 degree and beyond 28 degrees as well. However, after lunch time, there may be a requirement to cool the structure 101 for human comfort. At this point, the transition time identification module 225 may identify that the transition time required for the HVAC 109 to shift from 28 degree to 24 degree which may be 30 min. So, HVAC 109 may be activated such that 28 degrees may reach by 1:30 PM and based on human comfort the HVAC setpoint may be changed from 28 degrees to 24 degrees later. In another exemplary scenario, at 3 PM, the temperature outside the structure 101 may not be high and hence may not cause discomfort inside the structure 101. So, at this point the transition time may be identified for the HVAC 109 to shift from 24 degree to 28 degree and hence during this time, the HVAC 109 may be deactivated for optimal energy usage. Fig.4 shows a flowchart illustrating a process for optimizing energy consumption of HVAC in a structure in accordance with some embodiments of the present disclosure. As illustrated in Fig.4, the method 400 includes one or more blocks illustrating a method for optimizing energy consumption of HVAC 109 in a structure 101. The method 400 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 specific functions or implement specific abstract data types. The order in which the method 400 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 spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. At block 401, the method comprises receiving internal weather data 207and external weather associated with the structure 101. The internal weather data 207 may be received from one or more sensors 105 such as temperature sensor configured in the structure 101, humidity sensor configured in the structure 101, CO2 sensor and the like. The external weather data 209 associated with the structure 101 may be received from one or more data sources 107 such as open source API’s. At block 403, the method comprises providing the internal weather data 207and the external weather data 209 to the internal temperature forecast module. The internal temperature forecast module may detect the internal temperature of the structure 101 based on which current HVAC setpoint is identified. The internal temperature forecast module may detect the internal temperature based on a machine learning model which is trained using data such as internal weather data 207, external weather data 209 and state variables related to thermal load distribution in each of one or more locations. At block 405, the method comprises detecting non-linear transition time for the HVAC 109 to shift from the current HVAC setpoint to a reference HVAC setpoint. The reference HVAC setpoint indicates optimal energy usage in the structure 101 and the reference HVAC setpoint may be varied based on one of comfort of users in the structure 101 or energy optimization by a facility manager of the HVAC 109. The transition time is identified by the transition time identification module 225 using the machine learning model. The machine learning model is trained using one or more parameters such as comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, transition time to shift from one HVAC setpoint to another HVAC setpoint and time interval for which the HVAC is one of activated or deactivated for identifying the transition time. At block 407, the method comprises predicting the HVAC setpoint or changing the HVAC setpoint based on the detected transition time and the current internal temperature. The HVAC setpoint is predicted based on the transition time and the forecasted internal temperature in the structure 101. At block 409, the method comprises controlling the HVAC 109 based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC 109 in the structure 101. For cooling condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is activated since cooling is required inside the structure 101. But when the HVAC setpoint is greater than the temperature inside the structure 101, then the HVAC 109 may be deactivated. For heating condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is deactivated and when the HVAC 109 setpoint is greater than the temperature inside the structure 101 then the HVAC 109 is activated. So, in the present disclosure, the energy management system 103 predicts the time at which the HVAC 109 should be activated first time in a day and also at what time the desired temperature would be achieved based on the transition time. The energy management system 103 also predicts when to activate and deactivate the HVAC 109 in the desired bandwidth. Computer System Fig.5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 500 may be an energy management system 103 for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure 101. The computer system 500 may include 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. The computer system 500 may receive an image for processing from an image capturing device 101. 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 several 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 several 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 506, an operating system 507, a web browser 508, mail client 515, mail server 516, web server 517 and the like. In some embodiments, 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 OracleR or SybaseR. The operating system 507 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, APPLE MACINTOSHR OS X, UNIXR, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, OPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), APPLER IOSTM, GOOGLER ANDROIDTM, BLACKBERRYR OS, or the like. A user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces 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 MACINTOSHR operating systems, IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), UnixR X-Windows, web interface libraries (e.g., AJAXTM, DHTMLTM, ADOBE® FLASHTM, JAVASCRIPTTM, JAVATM, etc.), or the like. 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 Present Disclosure In an embodiment, the present disclosure provides a method and system for optimizing energy consumption of Heating Ventilation and Air Conditioning in structure. In an embodiment, in the present disclosure, the HVAC is controlled using the transition time and the HVAC setpoint which results in less usage of HVAC and therefore results in energy savings. In an embodiment, the present disclosure utilizes HVAC forecasting module setpoints and adjust HVAC real-time setpoints to utilize transition time for energy saving. In an embodiment, the present disclosure provides a method for controlling HVAC based on transition time using real time data such as number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure and hence provides energy optimization. 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 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 clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear 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 101 Structure 103 Energy management system 105 One or more sensors 107 One or more data sources 109 HVAC 201 I/O Interface 203 Processor 205 Memory 206 Data 207 Internal Weather data 209 External weather data 209 211 Thermal load data 213 HVAC setpoint data 215 Reference HVAC setpoint data 217 Other Data 218 Modules 219 Receiving Module 221 Thermal load detection Module 223 HVAC setpoint identification module 225 Transition time identification module 226 Machine Learning Model 227 HVAC controlling Module 229 Other Modules 231 HVAC database 3011 ,3012,3013 Location 3031,3032,3033 Opening Component 3051,3052,3053 Occupants 3071,3072,3073 Camera 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 511 Input devices 512 Output devices 513 RAM 514 ROM 515 Mail Client 516 Mail Server 517 Web Server

Claims:We claim:

1. A method for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) 109 in a structure 101, the method comprising:
receiving, by an energy management system 103 associated with the structure 101, internal weather data 207 and external weather data 209 associated with the structure 101 from one or more sensors 105 configured in the structure 101 and one or more data sources 107 respectively;
detecting, by the energy management system 103, thermal load distributed in one or more locations in the structure 101 based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure 101 and status of each of one or more opening components in the structure 101;
identifying, by the energy management system 103, a HVAC setpoint for the one or more locations in the structure 101 based on the internal weather data 207, the external weather data 209, and the thermal load distribution;
identifying, by the energy management system 103, a transition time required for the HVAC 109 to shift from a current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data 209 and the real-time data; and
controlling, by the energy management system 103, the HVAC 109 based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC 109 in the structure 101.

2. The method as claimed in claim 1, wherein the reference HVAC setpoint indicates optimal energy usage in the structure 101.

3. The method as claimed in claim 1, wherein the reference HVAC setpoint is varied based on one of comfort of users in the structure 101 or energy optimization.

4. The method as claimed in claim 1, wherein controlling the HVAC 109 comprises one of activating or deactivating the HVAC 109 or varying the reference HVAC setpoint based on the identified transition time and the current HVAC setpoint.
5. The method as claimed in claim 4, wherein the HVAC 109 is activated when the HVAC setpoint is less than temperature inside the structure 101 for cooling condition in the structure 101.

6. The method as claimed in claim 4, wherein the HVAC 109 is deactivated based on the transition time when the HVAC setpoint is greater than temperature inside the structure 101 for cooling condition in the structure 101.

7. The method as claimed in claim 4, wherein the HVAC 109 is deactivated based on the transition time when the HVAC setpoint is less than temperature inside the structure 101 and for heating condition in the structure 101.

8. The method as claimed in claim 4, wherein the HVAC 109 is activated when the HVAC setpoint is greater than temperature inside the structure 101 for heating condition in the structure 101.

9. The method as claimed in claim 4, wherein the reference HVAC setpoint is varied based on heating and cooling condition inside the structure 101.

10. The method as claimed in claim 1, wherein the HVAC setpoint is determined by a machine learning model which is trained using one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, cost parameter associated with the energy consumption of HVAC 109 and comfort parameter associated with occupants in the structure 101.

11. The method as claimed in claim 1, wherein the transition time is identified by a machine learning model which is trained using the one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, transition time to shift from one HVAC setpoint to another HVAC setpoint and time interval for which the HVAC 109 is one of activated or deactivated.

12. An energy management system 103 for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) 109 in a structure 101, the energy management system 103 comprising:
a processor 203; and
a memory 205 communicatively coupled to the processor 203, wherein the memory 205 stores processor-executable instructions, which, on execution, causes the processor 203 to:
receive internal weather data 207and external weather data 209 associated with the structure 101 from one or more sensors 105 configured in the structure 101 and one or more data sources 107 respectively;
detect thermal load distributed in one or more locations in the structure 101 based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure 101 and status of each of one or more opening components in the structure 101;
identify a current HVAC setpoint for each of the one or more locations in the structure 101 based on the internal weather data 207, the external weather data 209, and the thermal load distribution;
identify a transition time required for the HVAC 109 to shift from the current HVAC setpoint to a reference HVAC setpoint of the HVAC 109 in the one or more locations based on the external weather data 209 and the real-time data; and
control the HVAC 109 based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC 109 in the structure 101.

13. The energy management system 103 as claimed in claim 12, wherein the reference HVAC setpoint indicates optimal energy usage in the structure 101.

14. The energy management system 103 as claimed in claim 12, wherein the reference HVAC setpoint is varied based on one of comfort of users in the structure 101 or energy optimization.

15. The energy management system 103 as claimed in claim 12, wherein processor 203 controls the HVAC 109 by performing one of activating or deactivating the HVAC 109 or varying the reference HVAC setpoint based on the identified transition time and the current HVAC setpoint.

16. The energy management system 103 as claimed in claim 15, wherein the processor 203 activates the HVAC 109 when the HVAC setpoint is less than temperature inside the structure 101 for cooling condition in the structure 101.

17. The energy management system 103 as claimed in claim 15, wherein the processor 203 deactivates the HVAC 109 based on the transition time when the HVAC setpoint is greater than temperature inside the structure 101 for cooling condition in the structure 101.

18. The energy management system 103 as claimed in claim 15, wherein the processor 203 deactivates the HVAC 109 based on the transition time when the HVAC setpoint is less than temperature inside the structure 101 for heating condition in the structure 101.

19. The energy management system 103 as claimed in claim 15, wherein the processor 203 activates the HVAC 109 when the HVAC setpoint is greater than temperature inside the structure 101 for heating condition in the structure 101.

20. The energy management system 103 as claimed in claim 15, wherein the processor 203 varies the reference HVAC setpoint based on heating and cooling condition inside the structure 101.

21. The energy management system 103 as claimed in claim 12, wherein the processor 203 determines the reference HVAC setpoint using a machine learning model which is trained using one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, cost parameter associated with the energy consumption of HVAC 109 and comfort parameter associated with occupants in the structure 101.

22. The energy management system 103 as claimed in claim 12, wherein the processor 203 identifies the transition time using a machine learning model which is trained using the one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure 101 information, transition time to shift from one HVAC setpoint to another HVAC setpoint and time interval for which the HVAC 109 is one of activated or deactivated.
, Description:TECHNICAL FIELD
The present subject matter generally relates to energy management. More particularly, but not exclusively, the present disclosure discloses a method and a system for optimizing energy consumption of a Heating Ventilation and Air Conditioning (HVAC) in a structure.

BACKGROUND

Energy management is the process of tracking and optimizing energy consumption to conserve usage in a building. Particularly, energy management of Heating, Ventilation and Cooling (HVAC) has become a primary concern for building facility managers. In commercial buildings, HVAC systems may consume up to 40% energy consumption. Thus, existing systems have proposed to reduce energy consumption of HVAC using various techniques on energy savings without compromising comfort of occupants. One such existing technique includes sensing a real-time occupancy of occupants in an enclosed area of the building using a camera. However, this existing technique do not consider natural thermal characteristics of the building.

Traditionally, various Building Energy Management Systems (BEMS) or Building Management System (BMS) or Building Automation Systems (BAS) rely on inaccurate occupancy sensors, which hinder the responsiveness of automation systems. For example, passive infrared and ultra-sound occupancy sensors produce poor accuracy, because they are unable to determine the occupancy state adequately when occupants remain stationary for a prolonged period. They also have a limited range of operation which hinders their performance, especially in a large area.

In one of the existing prior arts, the system facilitates data-driven HVAC optimization for outputting HVAC setpoints to a thermostat. The system determines a predicted HVAC usage of HVAC unit based on the HVAC setpoints and an outdoor temperature. The system further determines and outputs a predicted cost of the HVAC unit for heating or cooling a structure based on the HVAC setpoints. The system further selects optimized HVAC setpoints such that the predicted cost is less than or equal to a user defined cost constraint and minimize deviation from user preferred HVAC setpoints. The method of predicting the HVAC usage during equilibrium, ramp up, and ramp down is reasonably accurate as disclosed in this existing prior art when HVAC load demand is high (for example, the cooling load in the summer or the heating load in winter). However, at other times, when the demand of HVAC operation is sparse, there is insufficient data to determine the HVAC usage using a linear relationship. Also, there is a need for full building structure information to predict or retrieve HVAC setpoint and linear transition time to ramp up or ramp down which is not considered in the existing system. The building transition time do depend on common spaces and add non-linearity in ramp up or ramp down time of the HVAC due to its dependencies on thermal conduction and convection losses. Further, the existing system also fails to consider information on real-time thermal load, like occupancy, door / window status or shade where the linear ramp up or ramp down relation might fail due to two common spaces occupancies. This may result in further non-linearity to transition time and hence affect optimization in energy consumption.

The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

SUMMARY

Disclosed herein is a method for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure. The method comprises receiving, by an energy management system associated with the structure, internal weather data and external weather data associated with the structure from one or more sensors configured in the structure and one or more data sources, respectively. Thereafter, the method comprises detecting, by the energy management system, thermal load distributed in one or more locations in the structure based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure. Once the thermal load distribution is identified, the method comprises identifying a HVAC setpoint for the one or more locations in the structure based on the internal weather data, the external weather data, and the thermal load distribution. Thereafter, the method comprises identifying a transition time required for the HVAC to shift from a current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data and the real-time data. Further, the method comprises controlling the HVAC based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC in the structure.

Further, the present disclosure discloses a system for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure. The energy management 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 receive internal weather data and external weather data associated with the structure from one or more sensors configured in the structure and one or more data sources respectively. The processor detects thermal load distributed in one or more locations in the structure based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure. Thereafter, the processor identifies a current HVAC setpoint for each of the one or more locations in the structure based on the internal weather data, the external weather data, and the thermal load distribution. Once the current HVAC setpoint is identified, the processor identifies a transition time required for the HVAC to shift from the current HVAC setpoint to a reference HVAC setpoint of the HVAC in the one or more locations based on the external weather data and the real-time data. Further, the processor is configured to control the HVAC based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC in the structure.

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 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 illustrates an exemplary environment for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure in accordance with some embodiments of the present disclosure.

Fig.2 shows a exemplary block diagram of an energy management system in accordance with some embodiments of the present disclosure.

Fig.3a shows an exemplary structure with one or more locations for controlling HVAC in accordance with some embodiments of the present disclosure.

Fig.3b shows a graph illustrating exemplary HVAC setpoint and the desired temperature bandwidth to be maintained in the location in accordance with some embodiments of the present disclosure.

Fig.3c shows an exemplary environment illustrating a method for training a machine learning model in accordance with some embodiments of the present disclosure.

Fig.3d shows an exemplary HVAC database and the process of controlling the HVAC in accordance with some embodiments of the present disclosure.

Fig.3e illustrates an exemplary method for activating and deactivating HVAC for energy optimization in accordance with some embodiments of the present disclosure.

Fig.4 shows a flowchart illustrating a method for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure in accordance with some embodiments of the present disclosure.

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 specific forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.
The terms “comprises”, “comprising”, “includes”, “including” 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 management system [also referred as system] for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure. The system receives internal weather data and external weather data associated with the structure from one or more sensors configured in the structure and one or more data sources, respectively. As an example, but not limited to, the structure may be an office building, a residential establishment, or a non-residential establishment. The system may detect thermal load distributed in one or more locations in the structure based on a real-time data. The real-time data is associated with number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure. The one or more opening components may include, but not limited to, door and window. Further, the system may identify a HVAC setpoint for the one or more locations in the structure based on the internal weather data indicative of weather condition inside the structure, the external weather data indicative of weather condition outside the structure, and the thermal load distribution. The HVAC setpoint may refer to parameter change which drives the HVAC to cool or heat. The system also identifies a transition time required for the HVAC to shift from a current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data and the real-time data. The reference HVAC setpoint is determined by a machine learning model which is trained using one or more parameters comprising at least one of internal weather data, external weather data, thermal load distributed in the one or more locations in the structure, structure information, cost parameter associated with the energy consumption of HVAC and comfort parameter associated with occupants in the structure. The transition time is identified by a machine learning model which is trained using the one or more parameters comprising at least one of internal weather data, external weather data, thermal load distributed in the one or more locations in the structure, structure information, transition time to shift from one HVAC setpoint to another HVAC setpoint detected for controlling the HVAC while training the machine learning model and time interval for which the HVAC is one of activated or deactivated.

Based on the identified transition time and the current HVAC setpoint, the system controls the HVAC for optimizing the energy consumption of the HVAC in the structure. Controlling the HVAC comprises one of activating or deactivating the HVAC or varying the reference HVAC setpoint based on the identified transition time and the current HVAC setpoint. The HVAC is activated when the HVAC setpoint is less than temperature inside the structure for a cooling condition in the structure and the HVAC is deactivated based on the transition time when the HVAC setpoint is greater than temperature inside the structure for a cooling condition in the structure. Similarly, the HVAC is deactivated based on the transition time when the HVAC setpoint is less than temperature inside the structure for a heating condition in the structure and the HVAC is activated when the HVAC setpoint is greater than temperature inside the structure for a heating condition in the structure. In this manner, the present disclosure proposes a method and a system for optimizing energy consumption in the structure by considering real time data such as number of occupants and appliances present in the one or more locations in the structure, status of each of one or more opening components in the structure and the transition time required for the HVAC to shift from current HVAC setpoint to a reference HVAC setpoint. The present disclosure may be implemented in individual or connected buildings and for residential, commercial/industrial applications.

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 of 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 illustrates an exemplary environment for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure in accordance with some embodiments of the present disclosure.

As shown in Fig.1, the environment 100 may include an energy management system 103, one or more sensors 105, one or more data sources 107, a structure 101, and a HVAC. In one implementation, the energy management system 103, the one or more sensors 105 and the HVAC is configured in the structure 101. As an example, but not limited to, the structure 101 may be an office building. The energy management system 103 may receive internal weather data associated with the structure 101 from the one or more sensors 105 configured in the structure 101 and may receive external weather data associated with the structure 101 from one or more data sources 107. As an example, the internal weather data may include, but not limited to, data associated with temperature inside the structure 101, humidity inside the structure 101 and CO2 level inside the structure 101. The one or more sensors 105 may include, but not limited to, temperature detection sensor, humidity detection sensor, CO2 detection sensor and a camera. The external weather data may be detected using one or more data sources 107 such as open source Application Programming Interface (API). The external weather data may also be detected using one or more sensors configured in the structure 101 which may include, but not limited to, thermometer for measuring temperature, anemometer for measuring wind speed, wind vane for measuring wind direction, hygrometer for measuring humidity and barometer for measuring atmospheric pressure.

Further, the energy management system 103 may detect thermal load distributed in one or more locations in the structure 101 based on real-time data. As an example, the structure 101 may have three locations. The real-time data is associated with number of occupants and appliances present in each of the three locations and status of each of one or more opening components in the structure 101. As an example, the one or more opening components, may include, but not limited to, a door or a window. The information about the number of occupants, appliances, and the status of one or more opening components may be identified using the camera. Based on the real-time data, the thermal load in each of the one or more locations in the structure 101 is detected by the energy management system 103. In an embodiment, the energy management system 103 identifies HVAC setpoint based on the internal weather data, external weather data and the thermal load distribution. The HVAC setpoint is any parameter related to HVAC which may include, but not limited to, temperature, humidity and air volume. The present disclosure develops thermal models dynamically for forecasting building temperature or HVAC setpoint with respect to change in external weather conditions using the building structure 101. In an embodiment, the energy management system 103 may also consider one or more parameters associated with architecture of the structure 101 such as ventilation in the structure 101, solar loss in the structure 101, fabric loss in the structure 101, conduction, and radiation of the structure 101 for identifying the HVAC setpoint. The identified HVAC setpoint is recorded as the current HVAC setpoint in the energy management system 103.

Once the HVAC setpoint is identified, the energy management system 103 may identify a transition time required for the HVAC to shift from the current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data and the real-time data. The transition time identified is the linear transition time and is identified using machine learning model. The machine learning model is trained using the one or more parameters comprising at least one of internal weather data, external weather data, thermal load distributed in the one or more locations in the structure 101, structure information, transition time to shift from one HVAC setpoint to another HVAC setpoint and time interval for which the HVAC is one of activated or deactivated.

The reference HVAC setpoint indicates at which a HVAC must be operated to achieve optimal energy usage in the structure 101. The reference HVAC setpoint may be varied based on one of comfort of users in the structure 101 or energy optimization by a facility manager of the structure 101. As an example, the facility manager may decide to maintain the reference HVAC setpoint based on energy optimization from 9 am to 12 PM of a day and maintain the reference HVAC setpoint based on comfort of the user during 1-3pm of the day. The reference HVAC setpoint may also be varied based on heating and cooling condition inside the structure 101. As an example, the current HVAC setpoint may be 24 degree and the reference HVAC setpoint may be 26 degree. The transition time may refer to the time required by the HVAC 109 to shift from 24 degree to 26 degree. In an embodiment, the energy management system 103 controls the HVAC 109 based on the transition time and the current HVAC setpoint. The controlling may be in terms of activating the HVAC 109, deactivating the HVAC 109, or varying the reference HVAC setpoint. For cooling condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC is activated since cooling is required inside the structure 101. But when the HVAC setpoint is greater than the temperature inside the structure 101, then the HVAC 109 may be deactivated. For heating condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is deactivated and when the HVAC setpoint is greater than the temperature inside the structure 101 then the HVAC 109 is activated. The present disclosure solves the issue of computation of linear transition time by identifying highly sensitive correlation vectors for identifying transition time using machine learning models. Based on the transition time, the HVAC 109 is controlled. The transition time concept facilitates extra benefit in terms of energy saving in the structure 101 and is resulting into cost saving.

Fig.2 shows a block diagram of an energy management system in accordance with some embodiments of the present disclosure.

As shown in Fig.2, the energy management system 103 may comprise an Input/Output (I/O) interface 201, a processor 203 and a memory 205. The I/O interface 201 may be configured to receive data 206 from one or more sensors 105 and one or more data sources 107. The processor 203 may be configured to perform one or more functions of the energy management system 103. In some implementations, the energy management system 103 may include data 206 and modules 218 for performing various operations in accordance with embodiments of the present disclosure. In an embodiment, the data 206 may be stored within the memory 205 and may include, internal weather data 207, external weather data 209, thermal load data 211, HVAC setpoint data 213, reference HVAC setpoint data 215 and other data 217.

In some embodiments, the data 206 may be stored within the memory 205 in the form of various data structures. Additionally, the data 206 may be organized using data models, such as relational or hierarchical data models. The other data 217 may store data, including temporary data and temporary files, generated by the modules 218 for performing various functions of the energy management system 103.

In an embodiment, one or more modules 218 may process the data of the energy management system 103. In one implementation, the one or more modules 218 may be communicatively coupled to the processor 203 for performing one or more functions of the energy management system 103. The modules 218 may include, without limiting to, a receiving module 219, a thermal load detection module 221, a HVAC setpoint identification module 223, a transition time identification module 225, a HVAC controlling module 227 and other modules 229.

As used herein, the term module 218 refers to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor 203 (shared, dedicated, or group) and memory 205 that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In an embodiment, the other modules 229 may be used to perform various miscellaneous functionalities of the energy management system 103. It will be appreciated that such modules 218 may be represented as a single module or a combination of different modules 218. Furthermore, a person of ordinary skill in the art will appreciate that in an implementation, the one or more modules 218 may be stored in the memory 205, without limiting the scope of the disclosure. The said modules 218 when configured with the functionality defined in the present disclosure will result in a novel hardware.

In an embodiment, the receiving module 219 may be configured to receive internal weather data 207 and external weather data 209 associated with the structure 101. The internal weather data 207 may be received from one or more sensors 105 such as temperature sensor configured in the structure 101, humidity sensor configured in the structure 101 and CO2 sensor configured in the structure 101 and the like. The external weather data 209 associated with the structure 101 may be received from one or more data sources 107 such as open source API’s and one or more sensors which may include, but not limited to, thermometer for measuring temperature, anemometer for measuring wind speed, wind vane for measuring wind direction, hygrometer for measuring humidity and barometer for measuring atmospheric pressure.

In an embodiment, the thermal load detection module 221 may be configured to detect thermal load in each of one or more locations in the structure 101 which is stored as thermal load data 211. The thermal load is detected based on real-time data associated with number of occupants and appliances present in the one or more locations in the structure 101 and status of each of one or more opening components in the structure 101. The one or more opening components may include, but not limited to, window and door. The information about number of occupants and appliances in the location and the information of whether the opening components are open or close is detected using image capturing sensors, for example cameras installed in the structure 101. In an embodiment, the thermal load for identified human count and appliances are computed based on common location wise distribution in the structure 101. The data set is prepared for forecasting internal temperature behavior inside the location for external weather conditions. The data set consists of all thermal load state variables, like human [occupant]/appliance count and their distribution inside the structure 101, HVAC parameters, event-oriented information such as open and close status of windows and doors and external weather conditions. The internal temperature predicted based on the correlation feature vectors shall be operated in the desired temperature bandwidth. Further, in addition to occupants count or occupancy recognition, occupancy (pattern) prediction is required to anticipate structure 101 usage and control pre-cooling or pre-heating in advance based on the environment. So, the occupancy pattern in the structure 101 may be identified for identifying when to activate or deactivate the HVAC 109. The occupancy pattern may be identified using a machine learning model which implements regression technique which is trained using number of occupants in the structure 101, thermal load distribution in the structure 101 on each day and sensor data.

In an embodiment, the HVAC setpoint identification module 223 may be configured to identify the HVAC setpoint [as an example, internal temperature] for the one or more locations in the structure 101 based on the internal weather data 207, the external weather data 209, and the thermal load distribution. Since the aspect of thermal load distribution is considered for identifying the HVAC setpoint, the present disclosure is more accurate in controlling the HVAC 109 for energy optimization than the existing conventional mechanisms. The HVAC setpoint is determined by a machine learning model which is trained using one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, cost parameter associated with the energy consumption of HVAC 109 and comfort parameter associated with occupants in the structure 101. The HVAC setpoint identified is the current HVAC setpoint which is stored as HVAC setpoint data 213.

In an embodiment, the transition time identification module 225 may be configured to identify transition time required for the HVAC 109 to shift from the current HVAC setpoint to a reference HVAC setpoint in the one or more locations based on the external weather data 209 and the real-time data. The reference setpoint is stored as reference HVAC setpoint data 215. The identified transition time is the non-linear transition time as compared to the linear transition time disclosed in the conventional mechanisms. As an example, the structure 101 may have one or more locations, location 1 3011, location 2 3012 and location 3 3013 as shown in Fig.3a. Each location may be configured with a camera 307 [3071, 3072, and 3073] and one or more sensors 105. The camera 307 may be configured to detect number of occupants 305 [3051, 3052 and 3053], appliances and status of opening components 303 [3031, 3032,3033] in each location. The one or more sensors 105 may be configured to detect, as an example, but not limited to, temperature inside the location and humidity inside the location. As an example, the objective is to operate internal temperature in location 1 3011 within 24 degree [minimum temperature balance point] to 28 degrees [maximum temperature balance point] which is the desired temperature [bandwidth] as shown in Fig.3b. As an example, 24 degrees may be considered as human comfort inside the structure 101 and 28 degrees may be considered as optimal energy usage. So, the desired bandwidth to operate the HVAC 109 lies between 24 degrees to 28 degrees.

As an example, the current HVAC setpoint [indicated as “current internal temperature” in Fig.3b] in location 1 3011 may be 25 degrees. The optimal energy usage is at 28 degrees. So, the transition time required for the HVAC setpoint to shift from 25 degrees to 28 degrees is “Tr” which is identified by the transition time identification module 225. The slope indicated is the behavior of the internal temperature inside the structure 101. Since there is a raise in internal temperature from 25 degrees to 28 degree, the HVAC 109 is deactivated for the time “Tr”. As an example, it may take about 30 minutes for the HVAC 109 to reach 28 degrees temperature inside the location 1 3011. This time is identified by the transition time identification module 225. Hence, the HVAC 109 may be deactivated for 30 minutes and thus optimize the energy consumption. Similarly, if the internal temperature has to reach 25 degree from 28 degree, then the transition time identification module 225 identifies the transition time required for the HVAC 109 to shift from 28 degrees to 25 degrees which may be 1 Hour. Hence, the HVAC 109 may be activated for 1 Hour. In the present disclosure, the transition time of the HVAC 109 is identified within the defined bandwidth using real time thermal loads in the location 1 3011. So, whenever there is a change in the reference HVAC setpoint, the transition time identification module 225 identifies the transition time for the HVAC 109 to shift from the current HVAC setpoint to the reference HVAC setpoint.

In an embodiment, the transition time identification module 225 may identify the transition time as a function of external weather data 209, internal weather data 207, thermal load, HVAC setpoint event information such as status of opening components. The transition time “Tr” is identified using the below equation 1.

____________Equation 1

Where, X1, X2…. Xn are number of locations inside the structure 101,
The transition time slope depends on non-linear coefficients of location wise thermal convection and/or conduction.
X1X2 is thermal convection and/or conduction with respect to location 1 3011 and location 3 3013;
X1= Text -Location 1 3011 HVAC setpoint or internal temperature
X2=Text-Location 2 3012 HVAC setpoint or internal temperature
d_1, d_2, d_3 = Predefined constants with respect to each location.

For example, d_1 is a predefined constant associated with location 1 3011 [X1], d_2 is a predefined constant associated with location 2 [X2] 3012 and d_3 is a predefined constant associated with location 1 3011 and location 3 [X3] 3013. Since location 1 3011 and location 3 3013 are interconnected, if a component such as door for example is open which is common to both location 1 3011 and location 3 3013, then the non-linearity factor or the constant is varied because the rise in temperature or cooling may take more time because of the common space and hence these factors have to be considered while identifying the transition time.

In an embodiment, the transition time identification module 225 while computing the transition time considers the aspect of location wise thermal convection and/or conduction heat loss. The transition time identification module 225 estimates HVAC activation and deactivation transition time (Tr) based on the machine learning model 226 which is trained using one or more parameters comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, HVAC setpoint data 213, time interval of the day 311, transition time to shift from one HVAC setpoint to another HVAC setpoint as shown in Fig.3c.

In an embodiment, the HVAC controlling module 227 is configured to control the HVAC 109 based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC 109 in the structure 101. Controlling the HVAC 109 comprises one of activating or deactivating the HVAC 109 or varying the reference HVAC setpoint based on the identified transition time and the current HVAC setpoint. For cooling condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is activated since cooling is required inside the structure 101. But when the HVAC setpoint is greater than the temperature inside the structure 101, then the HVAC 109 may be deactivated. For heating condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is deactivated and when the HVAC setpoint is greater than the temperature inside the structure 101 then the HVAC 109 is activated.

In an embodiment, the energy management system 103 receives is associated with a HVAC database 231 which stores information associated with thermal load, number of occupants, window status, door status, external weather data, temperature data and transition time in minutes per degree as shown in Fig.3d. These information are stored location wise or zone wise. For example, Z1, Z2, Z3 and Z4 represents 4 zones or 4 locations in the structure 101. The information is updated in the HVAC database 231 at predefined time intervals such as every 5 minutes. This information is provided to the machine learning model 226 for training purpose. The input to the machine learning model 226 is the real time data such as number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure at present in each location or zone. Based on these inputs, the transition time identification module 225 may identify the transition time “Tr” which is required for the HVAC 109 to shift from current HVAC setpoint to reference HVAC setpoint. Based on the transition time and the current HVAC setpoint, the HVAC controlling module 227 may control the HVAC 109 for optimizing energy consumption.

Fig.3e illustrates an exemplary method for activating and deactivating HVAC for energy optimization in accordance with some embodiments of the present disclosure.

As shown in Fig.3e, the X-Axis represents time of a day and Y-Axis represents internal temperature in the structure 101. As an example, the internal temperature between 24 degree and 28 degree is the desired temperature bandwidth. 24 degree is minimum internal temperature, and 28 degree is the maximum internal temperature. T1 represents temperature inside the structure 101 to reach reference setpoint when HVAC 109 is activated. T2 represents the temperature inside the structure 101 to naturally reach the reference HVAC setpoint when the HVAC 109 is deactivated from activation state and T3 represents the temperature inside the structure 101 to reach the reference HVAC setpoint by changing the HVAC setpoint. As an example, the HVAC 109 may be activated on a particular day at 8:30 am. The time at which the HVAC 109 must be activated or deactivated each day is identified by a machine learning model which is trained based on sensor data and thermal load of the structure 101. As an example, at 9 am, the current internal temperature is at 28 degree and in next few minutes, the current internal temperature is moving away from the desired temperature bandwidth that is away from 28 degrees. The objective is to maintain the HVAC setpoint within the desired temperature bandwidth for optimum energy usage. Therefore, at what time the HVAC setpoint has to be changed to bring it back into the desired temperature bandwidth is provided by the transition time. So, till 12 PM, the HVAC setpoint is within the desired temperature bandwidth.

In another exemplary scenario, from 12-1:30 PM there may be lesser number of people in the structure 101 as it may be lunch time for people in the structure 101 and hence during this time, the HVAC 109 may be deactivated. Therefore, the internal temperature may raise to 28 degree and beyond 28 degrees as well. However, after lunch time, there may be a requirement to cool the structure 101 for human comfort. At this point, the transition time identification module 225 may identify that the transition time required for the HVAC 109 to shift from 28 degree to 24 degree which may be 30 min. So, HVAC 109 may be activated such that 28 degrees may reach by 1:30 PM and based on human comfort the HVAC setpoint may be changed from 28 degrees to 24 degrees later.

In another exemplary scenario, at 3 PM, the temperature outside the structure 101 may not be high and hence may not cause discomfort inside the structure 101. So, at this point the transition time may be identified for the HVAC 109 to shift from 24 degree to 28 degree and hence during this time, the HVAC 109 may be deactivated for optimal energy usage.

Fig.4 shows a flowchart illustrating a process for optimizing energy consumption of HVAC in a structure in accordance with some embodiments of the present disclosure.

As illustrated in Fig.4, the method 400 includes one or more blocks illustrating a method for optimizing energy consumption of HVAC 109 in a structure 101. The method 400 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 specific functions or implement specific abstract data types.

The order in which the method 400 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 spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

At block 401, the method comprises receiving internal weather data 207and external weather associated with the structure 101. The internal weather data 207 may be received from one or more sensors 105 such as temperature sensor configured in the structure 101, humidity sensor configured in the structure 101, CO2 sensor and the like. The external weather data 209 associated with the structure 101 may be received from one or more data sources 107 such as open source API’s.

At block 403, the method comprises providing the internal weather data 207and the external weather data 209 to the internal temperature forecast module. The internal temperature forecast module may detect the internal temperature of the structure 101 based on which current HVAC setpoint is identified. The internal temperature forecast module may detect the internal temperature based on a machine learning model which is trained using data such as internal weather data 207, external weather data 209 and state variables related to thermal load distribution in each of one or more locations.

At block 405, the method comprises detecting non-linear transition time for the HVAC 109 to shift from the current HVAC setpoint to a reference HVAC setpoint. The reference HVAC setpoint indicates optimal energy usage in the structure 101 and the reference HVAC setpoint may be varied based on one of comfort of users in the structure 101 or energy optimization by a facility manager of the HVAC 109. The transition time is identified by the transition time identification module 225 using the machine learning model. The machine learning model is trained using one or more parameters such as comprising at least one of internal weather data 207, external weather data 209, thermal load distributed in the one or more locations in the structure 101, structure information, transition time to shift from one HVAC setpoint to another HVAC setpoint and time interval for which the HVAC is one of activated or deactivated for identifying the transition time.

At block 407, the method comprises predicting the HVAC setpoint or changing the HVAC setpoint based on the detected transition time and the current internal temperature. The HVAC setpoint is predicted based on the transition time and the forecasted internal temperature in the structure 101.

At block 409, the method comprises controlling the HVAC 109 based on the transition time and the current HVAC setpoint for optimizing the energy consumption of the HVAC 109 in the structure 101. For cooling condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is activated since cooling is required inside the structure 101. But when the HVAC setpoint is greater than the temperature inside the structure 101, then the HVAC 109 may be deactivated. For heating condition, when the HVAC setpoint is less than temperature inside the structure 101 then the HVAC 109 is deactivated and when the HVAC 109 setpoint is greater than the temperature inside the structure 101 then the HVAC 109 is activated. So, in the present disclosure, the energy management system 103 predicts the time at which the HVAC 109 should be activated first time in a day and also at what time the desired temperature would be achieved based on the transition time. The energy management system 103 also predicts when to activate and deactivate the HVAC 109 in the desired bandwidth.

Computer System

Fig.5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 500 may be an energy management system 103 for optimizing energy consumption of Heating Ventilation and Air Conditioning (HVAC) in a structure 101. The computer system 500 may include 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. The computer system 500 may receive an image for processing from an image capturing device 101.

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 several 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 several 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 506, an operating system 507, a web browser 508, mail client 515, mail server 516, web server 517 and the like. In some embodiments, 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 OracleR or SybaseR.

The operating system 507 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, APPLE MACINTOSHR OS X, UNIXR, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, OPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), APPLER IOSTM, GOOGLER ANDROIDTM, BLACKBERRYR OS, or the like. A user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces 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 MACINTOSHR operating systems, IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), UnixR X-Windows, web interface libraries (e.g., AJAXTM, DHTMLTM, ADOBE® FLASHTM, JAVASCRIPTTM, JAVATM, etc.), or the like.
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 Present Disclosure

In an embodiment, the present disclosure provides a method and system for optimizing energy consumption of Heating Ventilation and Air Conditioning in structure.

In an embodiment, in the present disclosure, the HVAC is controlled using the transition time and the HVAC setpoint which results in less usage of HVAC and therefore results in energy savings.

In an embodiment, the present disclosure utilizes HVAC forecasting module setpoints and adjust HVAC real-time setpoints to utilize transition time for energy saving.

In an embodiment, the present disclosure provides a method for controlling HVAC based on transition time using real time data such as number of occupants and appliances present in the one or more locations in the structure and status of each of one or more opening components in the structure and hence provides energy optimization.

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 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 clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear 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
101 Structure
103 Energy management system
105 One or more sensors
107 One or more data sources
109 HVAC
201 I/O Interface
203 Processor
205 Memory
206 Data
207 Internal Weather data
209 External weather data 209
211 Thermal load data
213 HVAC setpoint data
215 Reference HVAC setpoint data
217 Other Data
218 Modules
219 Receiving Module
221 Thermal load detection Module
223 HVAC setpoint identification module
225 Transition time identification module
226 Machine Learning Model
227 HVAC controlling Module
229 Other Modules
231 HVAC database
3011 ,3012,3013 Location
3031,3032,3033 Opening Component
3051,3052,3053 Occupants
3071,3072,3073 Camera
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
511 Input devices
512 Output devices
513 RAM
514 ROM
515 Mail Client
516 Mail Server
517 Web Server