System and method for monitoring and controlling energy consumption data in heating, ventilation, and air conditioning system

Abstract

Present invention describes system (100) and method (400) for monitoring and controlling energy consumption data in Heating, Ventilation, and Air Conditioning (HVAC) system (300) System comprising plurality of sensors (107) to collect one or more operational parameters associated with operation of electronic devices (300a-300n) implemented in HVAC system (300). Processor (104) is configured to: receive one or more operational parameters from sensors, monitor in real-time one or more operational parameters for electronic devices, analyze one or more operational parameters in real-time to detect change in one or more operational parameters. Detecting change is used to identify energy saving scenarios in regions with implemented HVAC. Control unit (103) generates one or more control parameters to control operational settings of one or more electronic devices (300a-300n) in response to one or more energy saving scenarios. Operational settings are used to reduce energy consumption by one or more electronic devices (300a-300n).

Description

[0001] SYSTEM AND METHOD FOR MONITORING AND CONTROLLING ENERGY CONSUMPTION DATA IN HEATING, VENTILATION, AND AIR CONDITIONING

[0002] SYSTEM

[0003] FIELD OF THE INVENTION

[0004] The present disclosure relates to an energy management system. More particularly, it is related to a method and system for system for monitoring and controlling energy consumption data in an Heating, Ventilation, and Air Conditioning (HVAC) system.

[0005] BACKGROUND OF THE INVENTION

[0006] The following description of related art is intended to provide background information pertaining to the field of the present disclosure. This section may include certain aspects of the art that may be related to various aspects of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.

[0007] Heating, ventilation, and air conditioning (HVAC) systems are essential for maintaining optimal indoor environmental conditions. The HVAC systems regulate temperature, humidity, and air quality within enclosed spaces, such as homes, offices and industrial facilities. The HVAC system is an important part of residential or commercial structures such as single-family homes, apartment buildings, hotels, and senior living facilities; medium to large industrial and office buildings such as skyscrapers and hospitals; vehicles such as cars, trains, airplanes, ships and submarines; and in marine environments, where safe and healthy building conditions are regulated with respect to temperature and humidity, using fresh air from outdoors.

[0008] Traditional HVAC systems often consume excessive energy, particularly during peak cooling or heating periods, thus leading to inefficient usage of electrical energy. The inefficient usage of electrical energy results in higher operational costs and increased environmental impact. Recent advancements in the HVAC systems have introduced efficient mechanical components and control systems. However, these systems still lack intelligence to dynamically optimize energy consumption while ensuring user comfort. Building Management System (BMS) has been employed to control various parameters of the HVAC systems, including temperature, humidity, and ventilation rates. However, these systems are not intelligent enough to optimize energy usage of the various parameters related to HVAC unit while maintaining thermal comfort of the users.

[0009] SUMMARY OF THE INVENTION

[0010] Existing Heating, ventilation, and air conditioning (HVAC) systems are lacking in intelligence to effectively monitoring and optimize energy consumptions of various HVAC parameters while ensuring thermal comfort of users. The existing HVAC systems often rely on fixed schedules and predefined set points, limiting their ability to adapt to real time conditions and optimize energy usage.

[0011] The present invention relates to a system and method for monitoring and controlling energy consumption data in an HVAC system.

[0012] It is an object of the present disclosure to mitigate, alleviate or eliminate one or more of the above-identified deficiencies and disadvantages in the prior art and solve at least the above- mentioned problem.

[0013] According to a first aspect, there is provided a system for monitoring and controlling energy consumption data in a Heating, Ventilation, and Air Conditioning (HVAC) system. The system comprises a plurality of sensors. The plurality of sensors configured to collect one or more operational parameters associated with operation of one or more electronic devices implemented in the HVAC system. The system also comprises a processor. The processor is configured to receive the one or more operational parameters from the plurality of sensors. The processor is further configured to monitor in real-time the one or more operational parameters for each electronic device of the one or more electronic devices. The processor is further configured to analyze the one or more operational parameters in real-time to detect a change in the one or more operational parameters. Detecting the change is of use to identify one or more energy saving scenarios in one or more regions with the implemented HVAC. The system also comprises a control unit communicatively coupled to the processor. The control unit is configured to generate one or more control parameters to control one or more operational settings of the one or more electronic devices in response to the one or more energy saving scenarios. The one or more operational settings are of use to reduce the energy consumption by the one or more electronic devices.

[0014] According to a second aspect there is provided a method for monitoring and controlling energy consumption data in a Heating, Ventilation, and Air Conditioning (HVAC) system. The method comprises collecting, through a plurality of sensors, one or more operational parameters associated with operation of one or more electronic devices configured implemented in the HVAC system. The method further comprises receiving, through a processor, the one or more operational parameters from the plurality of sensors. The method further comprises monitoring, through the processor, in real-time the one or more operational parameters for each electronic device of the one or more electronic devices. The method further comprises analyzing, through the processor, the one or more operational parameters in real-time by using one or more using Artificial Intelligent (Al) methods to detect a change in the one or more operational parameters. Detecting the change is of use to identify one or more energy saving scenarios in one or more regions with the implemented HVAC. The method further comprises generating, through a control unit, one or more control parameters to control one or more operational settings of the one or more electronic devices in response to the one or more energy saving scenarios. The one or more operational settings is of use to reduce the energy consumption by the one or more electronic devices.

[0015] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

[0016] BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS

[0017] The foregoing will be apparent from the following more particular description of the example embodiments, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the example embodiments. FIG. 1 illustrates a network implementation of a system 100 for monitoring and controlling energy consumption data in a Heating, Ventilation, and Air Conditioning (HVAC) system, according to some embodiments of the invention;

[0018] FIG. 2 illustrates a block diagram of the system 100 for monitoring and controlling energy consumption data in the HVAC system, according to some embodiments of the invention;

[0019] FIG. 3 A illustrates a working flow of the system 100 to generate a plurality of paths for monitoring and controlling energy consumption data in the HVAC system 300, according to some embodiments of the invention;

[0020] FIG. 3B illustrates additional details of the working workflow of the system 100, according to some embodiments of the invention;

[0021] FIG. 4 illustrates a flow chart for a method for monitoring and controlling energy consumption data in the HVAC system through the system 100 as shown in Fig. 1, according to some embodiments of the invention; and

[0022] FIG. 5 discloses an example computing environment, according to some embodiments of the invention.

[0023] Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may not have been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure.

[0024] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0025] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein. The present invention provides a system and method for monitoring and controlling energy consumption data in a Heating, Ventilation, and Air Conditioning (HVAC) system.

[0026] FIG. 1 discloses a network implementation of a system 100 arranged to communicate with a cloud server 500 and an HVAC system 300 including a plurality of devices 300a-300n. The plurality of devices 300a-300n are configured to communicate with each other via a network 200. The network implementation further includes a server connected to the system 100. The server may be further connected to the plurality of devices 300a-300n through the network 200. The network communication system further includes a server connected to the system 100. The server may be further connected to the plurality of devices 300a-300n through the network 200. The system 100 is used to control the various parameters of the HVAC system 300 by sending control commands to optimize the energy consumption of the HVAC system 300.

[0027] It may be understood that the server (local server / remote server / cloud server) 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 network server, a cloud-based computing environment, or a smart phone, and the like. It may be understood that the system 100 may correspond to a variety of portable device. Further, it may be understood that the system 100 may be, but not limited to, power saving device.

[0028] In an example implementation, the network 200 may be a wireless network, a wired network, or a combination thereof. The network 200 can be implemented as one of the different types of networks, such as intranet, Local Area Network, LAN, Wireless Personal Area Network, WPAN, Wireless Local Area Network, WLAN, wide area network, WAN, the Internet, and the like. The network 200 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, MQ Telemetry Transport, MQTT, Extensible Messaging and Presence Protocol, XMPP, 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 communication network 200 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

[0029] In accordance with the embodiments disclosed herein, the server is configured for establishing the communication between the system 100 and the plurality of communication devices 300a- 300n. For example, the server is configured to receive security threat data from a plurality of sources through the device 300a-300n.

[0030] Further, a cloud server 500 is configured to receive various parameters from the system 100 and process the various parameters regarding the one or more devices 300a-300n configured in the HVAC system 300 using the machine learning model and Al algorithms.

[0031] FIG. 2 is an example block diagram of the system 100. The system 100 is configured to cause performance of the method 400 (as depicted in FIG. 4 later) for monitoring and controlling energy consumption data in the HVAC system 300. The system 100 in FIG. 2 comprises one or more modules. The one or more modules may comprise a memory 101, a decision-making module 102, a control unit 103, a processor 104, an acquisition unit 105, and a transceiver 106. The processor 104, memory 102, and the recognition unit 106 may be operatively connected to each other.

[0032] The memory 101 is arranged to store a plurality of instructions to be executed by the processor 104. The memory 101 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, EPROM, Electrically Erasable and Programmable ROM, EEPROM, flash memories, hard disks, optical disks, and magnetic tapes. The processor 104 is configured to execute the plurality of modules. A controlling circuitry (not shown in FIG.) may be adapted to control the steps as executed by the processor 104.

[0033] The acquisition unit 105 may include, but not limited to, a plurality of sensors 107. The plurality of sensors 107 is configured to collect one or more operational parameters 108 associated with operation of one or more electronic devices 300a-300n implemented in the HVAC system 300. In an example, the one or more operational parameters 108 may include and not limited to external environmental conditions, internal condition in the one or more regions with implemented one or more electronic devices 300a- 300n, load on each electronic device, and efficiency of the one or more electronic devices 300a- 300n over a period of time.

[0034] The processor 104 is integrated to each of an acquisition unit 105 and a transceiver 106 and configured to receive the one or more operational parameters 108 from the plurality of sensors 107. In an example, the plurality of sensors 107 may include and not limited to sensors for measuring environmental temperature, humidity, CO2 levels, occupancy, and power consumption and workload of each electronic device, performance of the one or more electronic device 300a-300n, climatic conditions in a predefined area with implemented one or more electronic devices 300a-300n. In an embodiment, the plurality of sensors 107 either operate on LoRa network, Wifi or Modbus depending on the context of one or more building types. In an example, the plurality of sensors 107 may include remote sensors which are powered through small solar panels. In an example, the one or more building type may include and not limited to office buildings, apartment buildings, hotels, hospitals, high rise buildings, suburban homes, building with industrial facilities, etc. Further, the one or more building type may be classified as medium building such as offices, hotel or large building such as retail, mall, hospital, hotel.

[0035] The processor 104 is further configured to monitor in real-time the one or more operational parameters 108 for each electronic device of the one or more electronic devices 300a-300n. The processor 104 analyze the one or more operational parameters 108 in real-time to detect a change in the one or more operational parameters 108. Detecting the change in the one or more operational parameters 108 is used to identify one or more energy saving scenarios in one or more regions with the implemented HVAC 300.

[0036] The control unit 103 is communicatively coupled to the processor 104. The control unit 103 is configured to generate one or more control parameters to control one or more operational settings of the one or more electronic devices 300a-300n in response to the one or more energy saving scenarios. The transceiver 106 is configured to communicate the one or more operational parameters 108 collected by the plurality of sensors 107 to the decision-making module 102 / the cloud server 500. The control unit 103 is further configured to control the one or more operational parameters 108 of the plurality of device 300a-300n based on an output of the decision-making module 102. In an example, the one or more operational settings may include and not limited to temperature setpoints for the one or more electronic devices 300a- 300n, airflow rate via the one or more electronic devices 300a-300n, compressor speeds in the one or more electronic device, pressure set point for the one or more electronic devices, load of component of the one or more electronic devices, switch off / on state of the one or more electronic devices 300a-300n, changing positions of damper in the BMS system, or setting intervals to keep the one or more electronics devices 300a-300n switched on / off In an example, the processor 104 is configured to transmit the one or more control parameters to at least one of a control system of the HVAC and to one or more control modules in a Building Management System (BMS). In an example, the pressure set point may refer to a desired level of air pressure within the system. Maintaining a correct pressure point is crucial for optimal system performance and energy efficiency. The control parameters are used by the at least one of the control systems of the HVAC and one or more control modules to control the one or more operational settings of the one or more electronic devices 300a- 300n in response to the one or more energy saving scenarios. In an example, the BMS in buildings may be read-only, write based locally and / or cloud-based. The system 100 may integrate to various BMS via one or more control system of the BMS that uses industry standard communication networks like MQTT, Bacnet and Modbus. Thus, establishing a two-way communication with the existing BMS systems in the building. The system 100 may be designed to be compatible with the existing BMS or existing control unit / control systems, providing energy-saving intelligence and improving overall efficiency of the system 100.

[0037] The processor 104 is further configured to control the decision-making module 102 implementing Artificial Intelligent (Al) methods and Machine Learning (ML) models and the control unit 103. The processor 104 is configured to apply the one or more Al methods and ML models to analyze the one or more operational parameters 108 in real-time and detect the change in the one or more operational parameters 108. The processor 104 is configured to train and update the ML model over past one or more operational parameters 108 comprising external environmental conditions, internal conditions in the one or more regions with the implemented one or more electronic devices 300a-300n, load on each electronic device, efficiency of the one or more electronic devices 300a-300n over a period of time with respect to one or more building types, feedback from one or more users on execution of the one or more operational parameters 108. The ML model is trained on a reinforcement learning algorithm.

[0038] In an example, the ML model is re-trained in real-time with respect to changes in performance of the HVAC system 300 in response to the of the one or more operational settings and external environmental conditions. The one or more operational settings of the one or more electronic devices 300a-300n are updated in response to re-training of the ML model. The system 100 may include a pre-trained database having different energy consumption scenarios of the one or more devices 300a-300n of the HVAC system 300. The Al method / algorithm is trained to be used for developing / training the machine learning model by using the Al algorithms for processing the one or more operational parameters 108 sensed from the HVAC system 300. Based on the ML model thus generated, the system may generate one or more control param eters / control commands for setting or selecting most appropriate operational settings for the one or more devices 300a-300n to optimize the energy consumption and reduce overall cost of the electricity. The one or more control param eters / control commands control the operation of the one or more devices 300a-300n from the plurality of devices 300a-300n in the HVAC system 300.

[0039] In an example, the one or more control param eters / control commands are shared to control each of fan speed of different components, supply air temperature setpoint, coolant temperature, pressure at different points of the HVAC, both user and system defined set points, damper positions, valves status, temperature of condenser and evaporator, condenser fluid pressure set point, evaporator fluid pressure set point, flow rates of air flowing through, Chiller Valve opening percentage, Variable Air Volume (VAV) airflow rate / VAV damper opening, frequency of Air handling Unit (AHU) fan, Return Air temperature (zone to AHU) set point, Supply Air Temperature Setpoint (AHU), Chilled Water loop temperature, DX Cooling coil outlet node setpoint temperature, Fresh Air (AHU) damper opening percentage.

[0040] In an exemplary embodiment, the system 100 may process the data for generating energy optimization outputs in terms of the one or more control parameters (also may referred to as control commands) through the processor 104 configured in the system 100 or the system 100 may alternatively share the one or more operational parameters 108 through the transceiver 106 with the cloud server 500 in real-time for processing the one or more operational parameters 108 over the cloud sever 500 instead of processing the one or more operational parameters locally through the system 100.

[0041] In case of processing the one or more operational parameters 108 at the cloud server 500, the cloud server 500 implements the Al methods / algorithms and ML models and for processing the one or more operational parameters 108. The system 100 may use historical data (sensed originally through the HVAC system 300) to train the ML model by using the Al algorithms. The system 100 then uses the ML model for processing the one or more operational parameters 108 and generates one or more paths (as depicted later in FIG. 3 A) to be followed by the HVAC system 300 for optimizing the energy consumption by the HVAC system 300. The one or more operational parameters 108 comprising indoor and outdoor conditions along with HVAC setpoints and variables are transmitted to the cloud server 500 and are fed as an input to the ML models for processing the one or more operational parameters 108 by using the Al methods.

[0042] In another exemplary embodiment, FIG. 3 A shows a working flow of the system 100 / cloud server 500 implementing machine learning model (Al methods / algorithms) to generate a plurality of paths (path 1 to path 10) to be followed by the HVAC system 300 for optimizing the energy consumption. The one or more paths suggests optimized operation of devices by controlling the one or more operational settings of the devices in the HVAC system 300. More particularly, at step 301, the processor 104 / cloud server 500 receives the one or more operational parameters as input.

[0043] At step 302, the ML model selects a path, among the plurality of paths (path 1 to path 10), having the most optimized energy consumption value for the one or more operational parameters. At step 303, the ML model generates the one or more control param eters / control commands to be sent to the HVAC system 300 based on the optimized energy value of the selected path. In a case, the selected path does not have the most optimized energy consumption value, another path is selected and the process is repeated until the most optimized energy values are generated using the ML model.

[0044] The generated one or more control param eters / control commands are transmitted to the HVAC system 300 for controlling one or more operational settings of each device of the plurality of devices 300a-300n of the HVAC system 300.

[0045] FIG. 3B illustrates additional details of the workflow of the system 100, in accordance with another embodiment of the present invention. In FIG. 3B, the one or more operational parameters such as component setpoints of the devices, indoor conditions, outdoor conditions, and status of variables of the devices are fed to the processor 104 / the cloud server 500. In an example, one or more operational parameters such as temperature, CO2 concentration, humidity, occupancy, etc. are collected through the plurality of sensors 107 to identify the indoor condition. Similarly, one or more operational parameters such as temperature, humidity, occupancy, etc. are collected through the plurality of sensors 107 to identify the outdoor condition. The environment state 320 (one or more operational parameters parameters) when fed to the Al algorithm implemented in association with the system 100, enables the processor 100 to decide an optimal action (operational setting) to be taken in terms of the one or more control param eters / control commands. The control commands may vary from one HVAC system to other HVAC system. The control commands may include adjusting / controlling fan speed of different components, supply air temperature setpoint, coolant temperature, pressure at different points of the HVAC, both user and system defined set points, damper positions, valves status, temperature of condenser and evaporator, condenser fluid pressure set point, evaporator fluid pressure set point, flow rates of air flowing through, Chiller Valve opening percentage, Variable Air Volume (VAV) airflow rate / VAV damper opening, frequency of Air handling Unit (AHU) fan, Return Air temperature (zone to AHU) set point, Supply Air Temperature Setpoint (AHU), Chilled Water loop temperature, DX Cooling coil outlet node setpoint temperature, Fresh Air (AHU) damper opening percentage.

[0046] In another embodiment, Fig 4 shows a flow chart for a method 400 for monitoring and controlling energy consumption data in the HVAC system 300 in accordance with the present invention. The method 400 may be executed by the system 100 as discussed above.

[0047] The order in which the steps of the method 400 is described is not intended to be construed as a limitation, and any number of the described method steps may be combined in any order to implement the method 400 or alternate methods. Additionally, individual steps may be deleted from the method 400 without departing from the scope of the invention as defined in the claims.

[0048] At step 402, the method 400 comprises collecting the one or more operational parameters associated with operation of one or more electronic devices 300a-300n configured implemented in the HVAC system 300. The one or more operational parameters are sensed by the plurality of sensors 107 configured in the acquisition unit 105 of the system 100.

[0049] At step 404, the method 400 comprises receiving, through the processor 104, the one or more operational parameters from the plurality of sensors 107.

[0050] At step 406, the method 400 comprises monitoring the one or more operational parameters for each electronic device of the one or more electronic devices 300a-300n through the processor in real-time.

[0051] At step 408, the method 400 comprises analyzing the one or more operational parameters through the processor 104 in real-time by using the one or more using Artificial Intelligent (Al) methods to detect the change in the one or more operational parameters. Detecting the change is used to identify the one or more energy saving scenarios in one or more regions with the implemented HVAC.

[0052] At step 410, the method 400 comprises generating the one or more control parameters to control the one or more operational settings of the one or more electronic devices 300a-300n through the control unit 104 in response to the one or more energy saving scenarios. The one or more operational settings are used to reduce the energy consumption by the one or more electronic devices 300a-300n.

[0053] The additional details of the method 400 are similar to the details of the system 100 and hence are not repeated for the sake of brevity.

[0054] FIG. 5 illustrates an example computing environment 500 implementing the system 100, and method 400 as shown in FIGS. 2 and 3 for providing the user access to the security threat data in the virtual reality. As depicted in FIG. 5, the computing environment 500 comprises at least one data processor 506 that is equipped with a control module 502 and an Arithmetic Logic Unit, ALU 504, a plurality of networking devices 508 and a plurality Input output, I / O devices 510, a memory 512, a storage 514. The data processing module 506 may be responsible for implementing the system 100, and the method 400 as shown in FIGS. 2 and 3 respectively. For example, the data processing unit 506 in some embodiments be equivalent to the controlling circuitry of the platform described above in conjunction with FIGS. 2 and 3. The data processing unit 506 is capable of executing software instructions stored in memory 512. The data processing unit 506 receives commands from the control module 502 in order to perform its processing. Further, any logical and arithmetic operations involved in the execution of the instructions are computed with the help of the ALU 504.

[0055] The computer program is loadable into the data processing unit 506, which may, for example, be comprised in an electronic apparatus (such as the platform). When loaded into the data processing unit 506, the computer program may be stored in the memory 512 associated with or comprised in the data processing unit 506. According to some embodiments, the computer program may, when loaded into and run by the data processing module 506, cause execution of method steps according to, for example, any of the methods illustrated in FIGS. 2 and 3, or otherwise described herein. The overall computing environment 500 may be composed of multiple homogeneous and / or heterogeneous cores, multiple CPUs of different kinds, special media and other accelerators. Further, the plurality of data processing unit 506 may be located on a single chip or over multiple chips.

[0056] The algorithm comprises instructions and codes required for the implementation are stored in either the memory 512 or the storage 514 or both. At the time of execution, the instructions may be fetched from the corresponding memory 512 and / or storage 514 and executed by the data processing unit 506.

[0057] In case of any hardware implementations various networking devices 508 or external I / O devices 510 may be connected to the computing environment to support the implementation through the networking devices 508 and the I / O devices 510.

[0058] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the elements. The elements shown in FIG. 5 include blocks which can be at least one of a hardware device, or a combination of hardware device and software module.

[0059] Although the present invention has been described in considerable detail with reference to certain preferred embodiments and examples thereof, other embodiments and equivalents are possible. Even though numerous characteristics and advantages of the present invention have been set forth in the foregoing description, together with functional and procedural details, the disclosure is illustrative only, and changes may be made in detail, especially in terms of the procedural steps within the principles of the invention to the full extent indicated by the broad general meaning of the terms. Thus, various modifications are possible of the presently disclosed system and process without deviating from the intended scope of the present invention.

Claims

AMENDED CLAIMS received by the International Bureau on 29 Jul 2025(29.07.2025)WE CLAIM:

1. A system (100) for monitoring and controlling energy consumption data in a Heating, Ventilation, and Air Conditioning (HVAC) system (300), the system (100) comprising: an acquisition unit (105) comprising: a plurality of sensors (107) configured to: collect one or more operational parameters (108) associated with operation of one or more electronic devices (300a-300n) implemented in the HVAC system (300); a processor (104) coupled to the acquisition unit (105), and configured to: receive the one or more operational parameters (108) from the plurality of sensors (107); monitor in real-time the one or more operational parameters (108) for each electronic device of the one or more electronic devices (300a-300n); analyze the one or more operational parameters (108) in real-time to detect a change in the one or more operational parameters, wherein detecting the change is of use to identify one or more energy saving scenarios in one or more regions with the implemented HVAC; and a control unit (103) communicatively coupled to the processor (104), the control unit (103) configured to: generate one or more control parameters to control one or more operational settings of the one or more electronic devices (300a-300n) in response to the one or more energy saving scenarios, wherein the one or more operational settings is of use to reduce the energy consumption by the one or more electronic devices (300a-300n).

2. The system as claimed in claim 1, wherein the one or more operational parameters (108) comprises external environmental conditions, internal condition in the one or more regions with implemented one or more electronic devices (300a-300n), load on each electronic device, and efficiency of the one or more electronic devices (300a-300n) over a period of time.

3. The system as claimed in claim 1, wherein the one or more control operational settings comprises at least one of: temperature setpoints for the one or more electronic devices(300a-300n), airflow rate via the one or more electronic device (300a-300n), compressor speeds in the one or more electronic device (300a-300n), pressure set point for the one or more electronic devices (300a-300n), load of component of the one or more electronic devices (300a-300n), switch off / on state of the one or more electronic devices (300a-300n), changing positions of damper in a Building Management System (BMS), or setting intervals to keep the one or more electronics devices (300a-300n) switched on / off4. The system as claimed in claim 1, wherein the processor (104) is configured to: apply one or more Artificial Intelligent (Al) methods and Machine Learning (ML) models to analyze the one or more operational parameters (108) in real-time and detect the change in the one or more operational parameters (108); training and updating the Machine Learning (ML) model over past one or more operational parameters (108) comprising external environmental conditions, internal conditions in the one or more regions with the implemented one or more electronic devices (300a-300n), load on each electronic device, efficiency of the one or more electronic devices (300a-300n) over a period of time with respect to one or more building types, feedback from one or more users on execution of the one or more operational parameters (108), wherein the ML model is trained on a reinforcement learning algorithm.

5. The system as claimed in claim 4, wherein the ML model is re-trained in real-time with respect to changes in performance of the HVAC system (300) in response to the of the one or more operational settings and external environmental conditions; update the one or more operational settings of the one or more electronic devices (300a-300n) in response to re-training of the ML model.

6. The system as claimed in claim 1, wherein the processor (104) configured to: transmit the one or more control parameters to at least one of a control system of the HVAC and to one or more control modules in a Building Management System (BMS), wherein the control parameters are used by the at least one of the control system of the HVAC and one or more control modules to control the one or more operational settings of the one or more electronic devices (300a-300n) in response to the one or more energy saving scenarios.

7. The system as claimed in claim 1, wherein the plurality of sensors (107) comprises sensors for measuring environmental temperature, humidity, CO2 levels, occupancy, and power consumption and workload of each electronic device, performance of the one or more electronic device (300a-300n), climatic conditions in a predefined area with implemented one or more electronic devices (300a-300n).

8. A method (400) for monitoring and controlling energy consumption data in an Heating, Ventilation, and Air Conditioning (HVAC) system (300), the method (400) comprising: collecting (402), through a plurality of sensors (107) of an acquisition unit (105), one or more operational parameters (108) associated with operation of one or more electronic devices (300a-300n) configured implemented in the HVAC system; receiving (404), through a processor (104) that is coupled to the acquisition unit (105), the one or more operational parameters (108) from the plurality of sensors (107); monitoring (406), through the processor (104), in real-time the one or more operational parameters (108) for each electronic device of the one or more electronic devices (300a-300n); analyzing (408), through the processor (104), the one or more operational parameters (108) in real-time to detect a change in the one or more operational parameters (108), wherein detecting the change is of use to identify one or more energy saving scenarios in one or more regions with the implemented HVAC; and generating (410), through a control unit (103), one or more control parameters to control one or more operational settings of the one or more electronic devices (300a- 300n) in response to the one or more energy saving scenarios, wherein the one or more operational settings is of use to reduce the energy consumption by the one or more electronic devices (300a-300n).

9. The method (400) as claimed in claim 8, wherein the one or more operational parameters (108) comprises external environmental conditions, internal condition in the one or more regions with implemented one or more electronic devices (300a-300n), load on each electronic device, and efficiency of the one or more electronic devices (300a-300n) over a period of time.

10. The method (400) as claimed in claim 8, wherein the one or more control operational settings comprises at least one of: temperature setpoints for the one or more electronic devices (300a-300n), airflow rate via the one or more electronic device (300a-300n), compressor speeds in the one or more electronic device (300a-300n), pressure set point for the one or more electronic devices (300a-300n), load of component of the one or more electronic devices (300a-300n), switch off / on state of the one or more electronic devices (300a-300n), changing positions of damper in a Building Management System (BMS), or setting intervals to keep the one or more electronics devices (300a-300n) switched on / off11. The method (400) as claimed in claim 8, comprising: applying one or more Artificial Intelligent (Al) methods and Machine Learning (ML) models to analyze the one or more operational parameters (108) in real-time and detect the change in the one or more operational parameters (108); training and updating the Machine Learning (ML) model over past one or more operational parameters (108) comprising external environmental conditions, internal conditions in the one or more regions with the implemented one or more electronic devices (300a-300n), load on each electronic device, efficiency of the one or more electronic devices (300a-300n) over a period of time with respect to one or more building types, feedback from one or more users on execution of the one or more operational parameters (108), wherein the ML model is trained on a reinforcement learning algorithm.

12. The method (400) as claimed in claim 11, wherein the method comprising: re-training, the ML model, in real-time with respect to changes in performance of the HVAC system (300) in response to the of the one or more operational settings and external environmental conditions; updating the one or more operational settings of the one or more electronic devices (300a-300n) in response to re-training of the ML model.

13. The method (400) as claimed in claim 8, comprising: transmitting, the one or more control parameters to at least one of a control system of the HVAC and to one or more control modules in a Building Management System (BMS), wherein the control parameters are used by the at least one of the controlsystem of the HVAC and one or more control modules to control the one or more operational settings of the one or more electronic devices (300a-300n) in response to the one or more energy saving scenarios.

14. The method (400) as claimed in claim 8, wherein the plurality of sensors (107) comprises sensors for measuring environmental temperature, humidity, CO2 levels, occupancy, and power consumption and workload of each electronic device, performance of the one or more electronic device (300a-300n), climatic conditions in a predefined area with implemented one or more electronic devices (300a-300n).

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