Claims:WE CLAIM:

1. A method of optimizing energy consumption in a building, the method comprising:
capturing, by a processor, an ambient temperature and a load lead time associated with each of a plurality of buildings at a specific instance, wherein the ambient temperature and the load lead time is captured from a temperature sensor and an electric meter, respectively, installed within premises of each of the plurality of buildings;
comparing, by the processor, the load lead time and the ambient temperature of a target building with the load lead time and the ambient temperature of each of the plurality of buildings;
identifying, by the processor, a set of buildings, from the plurality of buildings, having higher ambient temperature and a lower load lead time as compared to the target building;
determining, by the processor, a building, from the set of buildings, having lowest load lead time, wherein the lowest load lead time indicates an optimal load lead time for the target building; and
applying, by the processor, load shifting for the target building based upon the optimal load lead time, thereby facilitating the energy optimization.

2. The method of claim 1, wherein the ambient temperature is the weather at the location of the building for a specific duration of time.
3. The method of claim 1, wherein the load lead time is the time required to reach stabilized energy consumption in a period of time.
4. A system of optimizing energy consumption in a building, the system comprising:
a processor; and
a memory coupled to the processor, wherein the processor executes a plurality of modules stored in the memory, and wherein the plurality of module comprising:
a capturing module configured to capture an ambient temperature and a load lead time associated with each of a plurality of buildings at a specific instance, wherein the ambient temperature and the load lead time is captured from a temperature sensor and an electric meter, respectively, installed within premises of each of the plurality of buildings;
a comparing module configured to compare the load lead time and the ambient temperature of a target building with the load lead time and the ambient temperature of each of the plurality of buildings;
an identifying module configured to identify a set of buildings, from the plurality of buildings, having higher ambient temperature and a lower load lead time as compared to the target building;
a determining module configured to determine a building, from the set of buildings, having lowest load lead time, wherein the lowest load lead time indicates an optimal load lead time for the target building; and
an applying module configured to apply load shifting for the target building based upon the optimal load lead time, thereby facilitating the energy optimization.

5. The system of claim 5, wherein the ambient temperature is the weather at the location of the building for a specific duration of time.
6. The system of claim 5, wherein the load lead time is the time required to reach stabilized energy consumption in a period of time.
7. A non-transitory computer readable medium embodying a program executable in a computing device for optimizing energy consumption in a building, the program comprising:
a program code for capturing an ambient temperature and a load lead time associated with each of a plurality of buildings at a specific instance, wherein the ambient temperature and the load lead time is captured from a temperature sensor and an electric meter, respectively, installed within premises of each of the plurality of buildings;
a program code for comparing the load lead time and the ambient temperature of a target building with the load lead time and the ambient temperature of each of the plurality of buildings;
a program code for identifying a set of buildings, from the plurality of buildings, having higher ambient temperature and a lower load lead time as compared to the target building;
a program code for determining a building, from the set of buildings, having lowest load lead time, wherein the lowest load lead time indicates an optimal load lead time for the target building; and
a program code for applying load shifting for the target building based upon the optimal load lead time, thereby facilitating the energy optimization. , Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of Invention:
SYSTEM AND METHOD FOR DETERMINING AN OPTIMAL LOAD
LEAD TIME FOR BUILDINGS

APPLICANT:
Tata Consultancy Services Limited
A Company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application does not claim priority from any patent application.

TECHNICAL FIELD
[002] The present disclosure described herein, in general, relates to system and method for determining an optimal load lead time for a building. Specifically, the present disclosure relates to method to reduce energy consumption for several buildings, of similar utilization type, by reducing the load lead time of the building.

BACKGROUND
[003] Typically, energy demand for a building varies over a time of day. For any building, maximum energy utilization peak is during the day when all electrical equipments and HVAC systems are turned ON and the energy utilization remains high until the building is closed. Thus, the energy consumption is usually low when the building is closed. Also, different loads in the building, including the Heating, Ventilation and Air Conditioning (HVAC), lighting and other equipments, are turned ON in the morning a few hours ahead of the official opening hours contributing to the energy consumption.

[004] Available energy-saving opportunities in the context of buildings is by optimizing energy on large scale where several buildings may be considered at a time or that does finer optimization in a single building. A lot of existing research and development work in energy-saving concentrate on optimizing energy consumption in a single building by drilling down to specific components such as HVAC systems, the energy meters, building occupancy, and so on. While existing methods may be useful and necessary for finer optimization of energy in a single building, these may be very effort intensive to apply across several hundred buildings, for instance.

SUMMARY
[005] This summary is provided to introduce aspects related to system and method for determining an optimal load lead time for building and aspects of which are further described below in the detailed description. The summary is also provided to introduce aspects relating to energy consumption for several buildings, of similar utilization type or managed by same enterprise, by reducing the load lead time of the building. This summary is not intended to identify essential features of the claimed disclosure nor is it intended for use in determining or limiting the scope of the claimed disclosure.

[006] In one implementation, a method to optimize energy consumption in a building is disclosed. The method may comprise capturing an ambient temperature and a load lead time associated with each of a plurality of buildings at a specific instance. The ambient temperature and the load lead time may be captured from a temperature sensor and an electric meter respectively. The temperature sensor and an electric meter may be installed within premises of each of the plurality of buildings. The method may comprise comparing the load lead time and the ambient temperature of a target building with the load lead time and the ambient temperature of each of the plurality of buildings. The method may comprise identifying a set of buildings, from the plurality of buildings, having higher ambient temperature and a lower load lead time as compared to the target building. The method may further comprise determining a building, from the set of buildings, having lowest load lead time. The lowest load lead time indicates an optimal load lead time for the target building. The method may comprise applying load shifting for the target building based upon the optimal load lead time, thereby facilitating the energy optimization. In one embodiment, the aforementioned method may be executed by a processor using set of instructions stored in a memory.

[007] In one implementation, a system to optimize energy consumption in a building is disclosed. The system may comprise a processor and a memory coupled to the processor. The processor may execute a plurality of modules stored in the memory. The plurality of modules may comprise a capturing module, a comparing module, an identifying module, determining module and an applying module. The capturing module may be configured to capture an ambient temperature and a load lead time associated with each of a plurality of buildings at a specific instance. It may be understood that the ambient temperature and the load lead time may be captured from a temperature sensor and an electric meter, respectively, installed within premises of each of the plurality of buildings. The comparing module may be configured to compare the load lead time and the ambient temperature of a target building with the load lead time and the ambient temperature of each of the plurality of buildings. The identifying module may be configured to identify a set of buildings, from the plurality of buildings, having higher ambient temperature and a lower load lead time as compared to the target building. The determining module may be configured to determine a building, from the set of buildings, having lowest load lead time. It may be understood that the lowest load lead time indicates an optimal load lead time for the target building. The applying module may be configured to apply load shifting for the target building based upon the optimal load lead time, thereby facilitating the energy optimization.

[008] In one implementation, a non-transitory computer readable medium embodying a program executable in a computing device for optimizing energy consumption in a building is disclosed. The program may comprise a program code for capturing an ambient temperature and a load lead time associated with each of a plurality of buildings at a specific instance. The ambient temperature and the load lead time may be captured from a temperature sensor and an electric meter, respectively, installed within premises of each of the plurality of buildings. The program may comprise a program code for comparing the load lead time and the ambient temperature of a target building with the load lead time and the ambient temperature of each of the plurality of buildings. The program may comprise a program code for identifying a set of buildings, from the plurality of buildings, having higher ambient temperature and a lower load lead time as compared to the target building. The program may comprise a program code for determining a building, from the set of buildings, having lowest load lead time. The lowest load lead time indicates an optimal load lead time for the target building. The program may comprise a program code for applying load shifting for the target building based upon the optimal load lead time, thereby facilitating the energy optimization.

BRIEF DESCRIPTION OF THE DRAWINGS
[009] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the disclosure, there is shown in the present documents example constructions of the disclosure; however, the disclosure is not limited to the specific methods and apparatus disclosed in the document and drawings.
[0010] The detailed description is described with reference to the accompanying figures. 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 drawings to refer like features and components.
[0011] Figure 1 illustrates a network implementation of a system for determining an optimal load lead time for buildings, in accordance with an embodiment of the present disclosure.
[0012] Figure 2 illustrates the system, in accordance with an embodiment of the present disclosure.
[0013] Figure 3 illustrates a graph comparing the ambient temperature with the lead time for various buildings of similar nature, in accordance with an embodiment of the present disclosure.
[0014] Figure 4 illustrates a graph comparing hours with energy consumed to compute demand shifting, in accordance with an embodiment of the present disclosure.
[0015] Figure 5 illustrates a flow chart depicting a method for determining an optimal load lead time for buildings, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0016] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any system(s) and method(s) similar or equivalent to those described herein may be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods are now described.
[0017] Systems and methods for determining an optimal load lead time for building are described in the present disclosure. In one aspect, a building, having an HVAC unit installed, is connected to sensors that capture data including the ambient temperature and a load lead time associated with the building. In one implementation, the load lead time can be derived from the energy meter readings. It is understood that there exits an identified set of buildings of similar nature or utilization type or managed by the same enterprise and located across various cities. In the present disclosure, the data related to opening and closing time of the building may be captured. Further, the hour-wise energy consumption data from energy meters and/or other sources over a period of time may be obtained. Furthermore, the ambient weather data including the temperature data for the set period of time may be captured. Based on the captured data the average load lead time of each of the plurality of building is determined. Once the average load lead time for each building of similar nature or utilization type or managed by same enterprise is determined, the best load lead time of the building is determined. The best load lead time is the benchmark for the set of buildings and such buildings are termed as “better buildings”. Thus, a better building can be defined as the building having a higher ambient temperature but smaller load lead time in comparison to the other buildings from the set of buildings.
[0018] In one aspect of the disclosure, based on the load lead time of the better buildings, the load shifting is implemented on the other buildings from the set of buildings. The load shifting is implemented gradually to reach the load lead time of the better building, thus leading to conservation of energy.
[0019] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0020] While aspects of system and method for determining an optimal load lead time for building as described in the present disclosure may be implemented in any number of different systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system.
[0021] Referring now to Figure 1, a network implementation 100 of a system 102 for determining an optimal load lead time for building is illustrated, in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 may be communicatively coupled to a plurality of sensors via communication network 106. Although the present disclosure is explained considering that the system 102 is implemented as a server communicatively coupled to a Heating, Ventilation and Air Conditioning (HVAC) system installed in a commercial building, retail outlet, facility and the like, however, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server and the like. In one implementation, the system 102 may be implemented in a cloud-based environment in which the user may operate individual computing systems configured to execute remotely located applications. It will be understood that sensor devices 104-1, 104-2…104-N, collectively referred to as sensor devices 104, are capable of communicating with the system 102 via the communication network 106. Examples of the sensor devices 104 may include, but are not limited to, a temperature sensor, an electric meter, a load sensor and a smart phone.
[0022] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0023] Referring to Figure 2, the system 102 is illustrated in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204 and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions or modules stored in the memory.
[0024] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may enable the system 102 to communicate with computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable etc. and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[0025] The memory 206 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memory, hard disks, optical disks, a compact disks (CDs), digital versatile disc or digital video disc (DVDs) and magnetic tapes. The memory 206 may include modules 208 and data 210. Additionally or alternatively, the memory 206 can be configured to store instructions which when executed by the processor(s) causes the system to behave in a manner as described in various embodiments.
[0026] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 208 may include a capturing module 212, a comparing module 214, an identifying module 216, a determining module 218, an applying module 220 and other modules 222. The other modules 222 may include programs or coded instructions that supplement applications and functions of the system 102. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system 102.
[0027] The data 210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a database 224 and other data 226. The other data 226 may include data generated as a result of the execution of one or more modules in the other modules 222.
[0028] In one implementation, the capturing module 212 captures the ambient temperature and a load lead time associated with each of a plurality of buildings at a specific instance. In one aspect, the ambient temperature may be captured from at least one of the sensor(s) 104 coupled to the system 102. In one embodiment, a temperature sensor or any other sensor capable of sensing the ambient temperature may be employed to capture the ambient temperature. Further, the load lead time may be detected by at least one of the sensor(s) 104, e.g. an electric meter. In one embodiment, the temperature sensor and the electric meter may be installed in the building. In another embodiment, the sensor(s) may be a wirelessly connected with the building in order to detect load lead time with respect to the building. In accordance with the disclosure, the load lead time is the time required to reach stabilized energy consumption in a period of time for a building. Further, in one aspect of the invention, the sensor(s) 104 sensing the ambient temperature and the lead time may be installed within the HVAC system itself or wirelessly connected to the HVAC system or any other system(s) capable to provide data relevant to ambient temperature and energy utilization of the building.
[0029] In one implementation, the comparing module 214 compares the load lead time and the ambient temperature of a target building with the load lead time and the ambient temperature of each of the plurality of buildings. In the present disclosure, the target building is a building from the plurality of buildings for which the energy consumption is required to be optimized. It is to be noted that the plurality of buildings is of comparable type or similarity of energy consumption. Once the data with respect to load lead time and the ambient temperature is captured, the comparing module 214 compares the data to reach a comparison of the ambient temperature with the lead time for various buildings. The data may be presented in a graphical manner or any such similar manner for ease of comparison by the user.
[0030] In one implementation, the identifying module 216 identifies a set of buildings, from the plurality of buildings, having higher ambient temperature and a lower load lead time as compared to the target building. Based on the comparison of the ambient temperature with the lead time for various buildings derived from the comparing module 214, a building with higher ambient temperature and a lower load lead time is determined. Such building is termed as a “better building” possessing the optimal load lead time in accordance with the disclosure. Further to obtain the
[0031] In one implementation, the determining module 218 determines, from the set of buildings, the better building having lowest load lead time. The better building may be understood as the building from the set of buildings having a higher ambient temperature but smaller load lead time in comparison to the other buildings from the set of buildings. The smallest load lead time may also be understood as the optimal load lead time. The better building signifies that even if the ambient temperature is high the load lead time is small which is an effective utilization of energy without causing any inconvenience to the occupants of the building.
[0032] In one implementation, the applying module 220, applies load shifting for the target building based upon the optimal load lead time of the better building. The load shifting reduces the load lead time to further attain the energy peak time. This in a manner facilitates in the optimization of energy.
[0033] Figure 3 is a graph illustrating comparison of the ambient temperature with the lead time for various buildings of similar nature, in accordance with an embodiment of the present disclosure. In an example, five buildings namely ‘A’, ‘B’, ‘C’, ‘D’ and ‘E’ in a set are considered with similar type and energy requirements located in different cities. A different load lead time is considered for each building. Further as shown in the Figure 3, graph is plotted with respect to load lead time and the average ambient temperature of all the five buildings. Later, the optimal lead time for each building taking into account the ambient weather and the operating pattern of other buildings of the set is determined.
[0034] Firstly for computation of optimal lead time, building A as shown in the graph of Figure 3 is considered. All the points plotted on the graph and situated to the right side of the building ‘A’ and below the building ‘A’ in the graph are measured. It may be understood that the buildings with higher ambient temperature than building ‘A’ and lower lead time than the building ‘A’ are considered. It may be noted that the buildings ‘B’, ‘C’, ‘D’ and ‘E’ fulfills the above criteria with respect to building ‘A’. Further, it may be noted from the graph that from the buildings ‘B’, ‘C’, ‘D’ and ‘E’, the building ‘C’ corresponds to the lowest lead time. In this embodiment, the building ‘C’ has the lead time of 1.5 hours and the ambient temperature greater than the building ‘A’. Hence, the building ‘C’ is termed as the “better building” having the optimal load lead time. Therefore, building A’s lead time is re-assigned as 1.5 hours. Thus, based upon the re-assignment of the load lead time, it may be noted that an air-conditioner in the building ‘A’ can be turned on 1.5 hours later than the present turn-on time (of 3 hours in advance) which leads to energy optimization. The optimization in the energy may be possible when the buildings consume energy without any inconvenience to the occupants of the building.
[0035] Referring, further to the Figure 3, similar methodology as above is applied for the buildings ‘B’, ‘C’, ‘D’ and ‘E’ to compute the building with higher ambient temperature and lower lead time than the building under consideration. On applying the above computation of optimal load lead time for the building ‘B’, the building ‘C’ correspond to the lowest lead time thus requiring the building ‘B’ to re-assign the lead time to be 30 minutes hours later than the present turn-on time of the building ‘B’. On considering the building ‘C’, it can be noted that there are no buildings to the right side of the building ‘C’ and below of the building ‘C’. Thus, the lead time of the building ‘C’ is optimum and may not be altered further. It may be noted that that similar to the building ‘C’, the lead time of the building ‘E’ is not required to be altered. Further, on considering the building ‘D’, the building ‘E’ is to the right side of the building ‘D’ and below the building ‘D’. Hence, the building ‘E’ correspond to the lowest lead time thus requiring the building ‘D’ to re-assign the lead time to be 2 hours later than the present turn-on time of the building ‘D’. In this manner, the set of buildings may be compared based on the lead time and the ambient temperature.
[0036] Figure 4 is a graph that compares the hours with energy consumed, in accordance with an embodiment of the present disclosure. In one aspect of the disclosure, demand shifting is computed based on the difference between the actual/ current load lead time and the optimized/ new load lead time of the building. The graph illustrates the optimization in energy by varying the load lead time with the ambient temperature.
[0037] Referring now to Figure 5, a method 500 for determining an optimal load lead time for buildings is shown, in accordance with an embodiment of the present disclosure. The method 500 may be described in general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 500 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[0038] The order in which the method 500 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 500 or alternate methods. Additionally, individual blocks may be deleted from the method 500 without departing from the spirit and scope of the disclosure described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 500 may be considered to be implemented in the above described system 102.
[0039] At block 502, an ambient temperature and a load lead time associated with each of a plurality of buildings for a specific instance may be captured. The ambient temperature and the load lead time may be captured from a temperature sensor and an electric meter, respectively, installed within premises of each of the plurality of buildings
[0040] At block 504, the load lead time and the ambient temperature of a target building may be compared with the load lead time and the ambient temperature of each of the plurality of buildings.
[0041] At block 506, the building with higher ambient temperature and a lower load lead time as compared to the target building may be identified.
[0042] At block 508, the building, from the plurality of buildings, having lowest load lead time may be determined. The lowest load lead time indicates an optimal load lead time for the target building.
[0043] At block 510, load shifting for the target building may be applied. The load shifting is based upon the optimal load lead time which further facilitates energy optimization.
[0044] Although implementations for system and method for determining an optimal load lead time for buildings have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for determination of optimal load lead time for buildings.
[0045] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[0046] Some embodiments enable a system and a method for reducing the energy consumption for several buildings, of similar utilization type or managed by same enterprise.
[0047] Some embodiments enable a system and a method for reducing the energy consumption by applying load shifting for each building.
[0048] Some embodiments enable a system and a method that is computationally inexpensive and lightweight due to less input and specific requirements.