METHOD AND SYSTEM TO ACHIEVE HEATING AND COOLING TEMPERATURE WITHIN ONE OR MORE ZONES WHEN OCCUPIED

Description

TECHNOLOGICAL FIELD

The present invention relates to building management systems (BMS), and more particularly relates to a method and system to achieve heating and cooling temperature within one or more zones when occupied.

BACKGROUND

HVAC (heating, ventilation, and air conditioning) system refers to the technology used to provide indoor comfort and maintain air quality in buildings, vehicles, and other enclosed spaces. The HVAC system provides heating by various means, such as furnaces, boilers, heat pumps, and electric heaters. The HVAC system generates heat to warm the indoor environment during colder weather conditions. Typically, the HVAC system facilitates to exchange indoor air with outdoor air to improve air quality and remove contaminants such as odors, moisture, and pollutants. The HVAC system include fans, ductwork, and filters to distribute fresh air and remove stale air from buildings. Moreover, the HVAC system also involves cooling and dehumidifying indoor air to maintain comfortable temperatures during hot weather. The HVAC system uses air conditioners, chillers, evaporative coolers, and heat pumps to remove heat from indoor air and circulate cool air throughout the space.

The HVAC system is required to heat or cool the enclosed spaces to an optimum level that is required by a user. To obtain this optimum level or temperature, a building manager or the user is required to activate the HVAC system to allow the HVAC system to heat or cool the enclosed spaced at the desired temperature. However, it is a big challenge for the building manager to determine how long ago the HVAC system should be activated before the enclosed space is being occupied. In order to heat or cool any enclosed space, multiple parameters need to be considered such as capacity of the HVAC system, number of openings etc. Therefore, it becomes humanly impossible for any building manager to predict this time. Therefore, to achieve the desired temperature, the building manager often keep the HVAC system turned on for more than a specific period of time that results in increasing an operating cost of the HVAC system.

Applicant has identified a number of deficiencies and problems, and through applied effort, ingenuity, and innovation, many of these identified problems have been solved by developing solutions that are included in embodiments of the present disclosure, many examples of which are described in detail herein.

BRIEF SUMMARY

The following presents a simplified summary to provide a basic understanding of some aspects of the present disclosure. This summary is not an extensive overview and is intended to neither identify key or critical elements nor delineate the scope of such elements. Its purpose is to present some concepts of the described features in a simplified form as a prelude to the more detailed description that is presented later.

In an example embodiment, a method is disclosed. The method comprises training, via at least one processor, a machine learning (ML) model based at least on historical temperature data and one or more input conditions of one or more zones. The historical temperature data comprises temperature of each of the one or more zones over a period of time and the one or more input conditions comprises one or more historical temperature set points, historical occupancy data, or one or more static parameters of each zone. The method further comprises receiving, via the at least one processor, temperature data from one or more sensors and one or more input conditions of the one or more zones for a predefined time period in real-time. The one or more input conditions comprises one or more temperature set points to be set for each zone and occupancy data for each zone. The method further comprises determining a threshold time period to achieve heating or cooling temperature within the one or more zones when occupied using the trained ML model, based at least on the received temperature data and one or more input conditions of the one or more zones for the predefined time period. The threshold time period corresponds to time required for each zone to achieve one or more temperature set points related to heating or cooling when occupied. Thereafter, the method comprises adjusting, via the at least one processor, the one or more temperature set points for the one or more zones at the determined threshold time period using the trained ML model to achieve heating or cooling temperature within the one or more zones when occupied.

In some embodiments, the static parameters comprise at least one of size of the one or more zones, shape of the one or more zones, a number of door openings in the one or more zones, a number of windows and doors present in each zone, floor height of the one or more zones, or location of the one or more zones. In some embodiments, the occupancy data comprises occupancy of the one or more zones and a number of occupants within each of the one or more zones.

In some embodiments, the one or more sensors comprises at least one of a temperature sensor, a humidity sensor, or occupancy sensors. In some embodiments, the one or more zones comprises at least one of a building, a warehouse, a storage unit, or an office space.

In some embodiments, the ML model corresponds to a statistical model and a piecewise linear model that uses one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. In some embodiments, the one or more temperature set points corresponds to a set point at which the temperature is adjusted for heating or cooling of the one or more zones.

In another example embodiment, a system is disclosed. The system comprises a memory and at least one processor communicatively coupled to the memory. The at least one processor is configured to train a machine learning (ML) model based at least on historical temperature data and one or more input conditions of one or more zones. The historical temperature data comprises temperature of each of the one or more zones over a period of time and the one or more input conditions comprises one or more historical temperature set points, historical occupancy data, or one or more static parameters of each zone. The at least one processor is further configured to receive temperature data from one or more sensors and one or more input conditions of the one or more zones for a predefined time period in real-time, wherein the one or more input conditions comprises one or more temperature set points to be set for each zone and occupancy data for each zone. Further, the at least one processor is configured to determine a threshold time period to achieve heating or cooling temperature within the one or more zones when occupied using the trained ML model, based at least on the received temperature data and the one or more input conditions of the one or more zones for the predefined time period. In some embodiments, the threshold time period corresponds to time required for each zone to achieve one or more temperature set points related to heating or cooling when occupied. Thereafter, the at least one processor is configured to adjust the one or more temperature set points for the one or more zones at the determined threshold time period using the trained ML model to achieve heating or cooling temperature within the one or more zones when occupied.

In some embodiments, the static parameters comprise at least one of size of the one or more zones, shape of the one or more zones, a number of door openings in the one or more zones, a number of windows and doors present in each zone, floor height of the one or more zones, or location of the one or more zones. In some embodiments, the occupancy data comprises occupancy of the one or more zones and a number of occupants within each of the one or more zones. In some embodiments, the one or more sensors comprises at least one of a temperature sensor, a humidity sensor, or occupancy sensors. In some embodiments, the one or more zones comprises at least one of a building, a warehouse, a storage unit, or an office space. In some embodiments, the ML model corresponds to a statistical model and a piecewise linear model that uses one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. In some embodiments, the one or more temperature set points corresponds to a set point at which the temperature is adjusted for heating or cooling of the one or more zones.

In another example embodiment, a non-transitory machine-readable information storage medium is disclosed. The non-transitory machine-readable information storage medium comprising one or more instructions which when executed by at least one processor to perform operations comprising training a machine learning (ML) model based at least on historical temperature data and one or more input conditions of one or more zones, wherein the historical temperature data comprises temperature of each of the one or more zones over a period of time and the one or more input conditions comprises one or more historical temperature set points, historical occupancy data, or one or more static parameters of each zone; receiving temperature data from one or more sensors and one or more input conditions of the one or more zones for a predefined time period in real-time, wherein the one or more input conditions comprises one or more temperature set points to be set for each zone and occupancy data for each zone; determining a threshold time period to achieve heating or cooling temperature within the one or more zones when occupied using the trained ML model, based at least on the received temperature data and the one or more input conditions of the one or more zones for the predefined time period, wherein the threshold time period corresponds to time required for each zone to achieve one or more temperature set points related to heating or cooling when occupied; and adjusting the one or more temperature set points for the one or more zones at the determined threshold time period using the trained ML model to achieve heating or cooling temperature within the one or more zones when occupied.

In some embodiments, the static parameters comprise at least one of size of the one or more zones, shape of the one or more zones, a number of door openings in the one or more zones, a number of windows and doors present in each zone, floor height of the one or more zones, or location of the one or more zones. In some embodiments, the occupancy data comprises occupancy of the one or more zones and a number of occupants within each of the one or more zones. In some embodiments, the one or more sensors comprises at least one of a temperature sensor, a humidity sensor, or occupancy sensors. In some embodiments, the one or more zones comprises at least one of a building, a warehouse, a storage unit, or an office space. In some embodiments, the ML model corresponds to a statistical model and a piecewise linear model that uses one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. In some embodiments, the one or more temperature set points corresponds to a set point at which the temperature is adjusted for heating or cooling of the one or more zones.

The above summary is provided merely for purposes of summarizing some exemplary embodiments to provide a basic understanding of some aspects of the disclosure. Accordingly, it will be appreciated that the above-described embodiments are merely examples and should not be construed to narrow the scope or spirit of the disclosure in any way. It will be appreciated that the scope of the disclosure encompasses many potential embodiments in addition to those here summarized, some of which are further explained within the following detailed description and its accompanying drawings.

BRIEF DESCRIPTION OF THE DRA WINGS

Having thus described certain example embodiments of the present disclosure in general terms, reference will hereinafter be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

FIG. 1 illustrates a network diagram of a system in accordance with an example embodiment of the present disclosure;

FIG. 2 illustrates a block diagram of a server in accordance with an example embodiment of the present disclosure;

FIG. 3 illustrates an exemplary scenario of an architecture of the system to achieve heating or cooling within one or more zones at a threshold time period in accordance with an example embodiment of the present disclosure;

FIG. 4 illustrates a flowchart of a method to achieve heating or cooling within the one or more zones at the threshold time period in accordance with an example embodiment of the present disclosure;

FIG. 5A illustrates a graphical representation of weekly seasonality of a trained ML model in accordance with an example embodiment of the present disclosure;

FIG. 5B illustrates a graphical representation of daily seasonality of the trained ML model in accordance with an example embodiment of the present disclosure;

FIG. 6 illustrates a graphical representation of the time recovery curve associated with the system in accordance with an example embodiment of the present disclosure; and

FIG. 7 illustrates a table having calculation of the trained ML model for initiating a cooling cycle for the one or more zones for a month in accordance with an example embodiment of the present disclosure.

DETAILED DESCRIPTION

The exemplary embodiments described herein provide detail for illustrative purposes and are subject to many variations in structure and design. It should be appreciated, however, that the embodiments are not limited to a particularly disclosed embodiment shown or described. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but these are intended to cover the application or implementation without departing from the spirit or scope of the claims.

Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The terms “a,” “an,” and “the” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced object. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Like numerals represent like parts in the figures.

Embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings in which like numerals represent like elements throughout the several figures, and in which example embodiments are shown. Embodiments of the present disclosure may, however, be embodied in alternative forms and should not be construed as being limited to the embodiments set forth herein. The examples set forth herein are non-limiting examples and are merely examples among other possible examples.

The present disclosure provides various embodiments of system and method to achieve heating and cooling temperature within one or more zones when occupied. Embodiments may be configured to receive historical temperature data and one or more input conditions of the one or more zones. Embodiments may be configured to determine one or more environmental conditions comprising a temperature data, humidity data, and occupancy data of the one or more zones using one or more sensors. Embodiments may be configured to train a machine learning (ML) model based at least on the one or more environmental conditions and the one or more input conditions. Embodiments may be configured to determine a threshold time period to achieve heating or cooling temperature within the one or more zones when occupied using the trained ML model. Embodiments may be configured to adjust one or more temperature set points for the one or more zones at the determined threshold time period using the trained ML model to achieve heating or cooling temperature within the one or more zones when occupied.

FIG. 1 illustrates a network diagram of a system 100, in accordance with an example embodiment of the present disclosure. The system 100 may comprise a network 102 communicatively coupled to a Heating, Ventilation and Air Conditioning (HVAC) unit 104 of a building (not shown), one or more sensors 106, a server 108, and a user device 110.

In some embodiments, the network 102 may be a communication network such as internet or a cloud network, that may be configured to allow computing devices and processing systems to communicate with each other through wired network, wireless network, or a combination of both. In some embodiments, the network 102 may refer to as a distributed infrastructure that is configured to exchange of data, information, and resources among interconnected computing devices and systems. The network 102 may be designed to facilitate communication and collaboration across various locations, devices, and platforms. Those skilled in the art will recognize that wired devices may include, but are not limited to, wired networks such as Wide Area Networks (WANs) or Local Area Networks (LANs), while wireless devices may include wireless communications established via Radio Frequency (RF) signals or infrared signals. Various devices in the system 100 may connect to the network 102 in accordance with various wired and wireless communication protocols such as Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), and 2G, 3G, or 4G communication protocols.

The HVAC unit 104 may be installed in the building. Further, the HVAC unit 104 may be configured to regulate an indoor environment of the building by controlling temperature, humidity, and air quality inside the building. The HVAC unit 104 may comprise a heat pump for heating, an air conditioner for cooling, a ventilation system for fresh air exchange and a network of ducts to distribute treated air throughout the building. In some embodiments, the HVAC unit 104 may be operated in one or more cycles in response to temperature variations inside the building. Further, when heating is required, the HVAC unit 104 may be configured to provide warm air inside the building. Further, when cooling is required, the HVAC unit 104 may provide cold air inside to building to maintain the ambient temperature inside the building. In some embodiments, the HVAC unit 104 installed in the building may be designed to maintain an ambient temperature within one or more zones inside the building. In some embodiments, the HVAC unit 104 may be installed in various types of buildings, including residential homes, commercial establishments, industrial facilities, and institutional buildings. In some embodiments, the HVAC unit 104 installed within the building may be configured to achieve the ambient temperature by initiating a cooling cycle or a heating cycle inside the one or more zones, based on a temperature data, a humidity data and an occupancy data of the one or more zones of the building in a real-time.

In some embodiments, the one or more sensors 106 may be installed at the one or more zones of the building. In some embodiments, the one or more sensors 106 may be configured to determine the temperature data, the humidity data and the occupancy data of the one or more zones. In some embodiments, the one or more sensors 106 may comprise at least one of a temperature sensor, a humidity sensor and a plurality of zone level occupancy sensors. Further, the temperature sensor may be configured to detect temperature of the one or more zones. Further, the temperature sensor may be configured to operate between a temperature range of 0-60 degree Celsius. In some embodiments, the temperature sensor may correspond to at least one of a thermocouple, thermistor, resistance temperature detector (RTD) or infrared (IR) sensor.

In an exemplary embodiment, when the temperature sensor corresponds to the thermocouple. Further, the thermocouple may comprise a pair of metallic wires having a junction. Further, the thermocouple may be configured to provide at least one signal when supplied with a pre-defined threshold voltage. In some embodiments, the at least one signal may correspond to an output voltage that may be directly proportional to the temperature gradient of the one or more zones. In some embodiments, the at least one signal corresponds to the temperature data of the one or more zones. In another exemplary embodiment, the temperature sensor corresponds to the thermistor. Further, the thermistor may also be referred as a temperature based resistor. The thermistor may be configured to generate the at least one signal when supplied with the pre-defined threshold voltage. in some embodiments, the at least one signal may be proportional to the temperature gradient of the one or more zones. In some embodiments, the at least one signal corresponds to the temperature data of the one or more zones

In another exemplary embodiment, when the temperature sensor corresponds to the RTD or the IR sensor, the RTD or the IR sensor may be configured to provide the at least one signal. In some embodiments, the at least one signal may provide the temperature data of the one or more zones. In some embodiments, the temperature sensor may be configured to generate at least one signal upon supplied with a pre-defined threshold input voltage. In some embodiments, the at least one signal may be configured to provide the temperature data corresponding to the temperature of the one or more zones.

In some embodiments, the humidity sensor may be configured to detect moisture content present within the one or more zones. Further, the humidity sensor may be configured to operate between a range of 0% to 100%. Further, the humidity sensor may comprise at least one of a capacitive humidity sensor, a resistive humidity sensor or a thermal humidity sensor. Further, the humidity sensor may be configured to generate at least one signal. In some embodiments, the at least one signal may be configured to provide the humidity data corresponding to a humidity level of the one or more zones.

In some embodiments, when the humidity sensor corresponds to the capacitive humidity sensor. The capacitive humidity sensor may comprise at least two electrodes that may be configured to generate a capacitance when supplied with the pre-defined threshold voltage. Further, the capacitance between the at least two electrodes may be proportional to humidity of the one or more zones. In some embodiments, the capacitive humidity sensor may be configured to generate one or more signals corresponds to the humidity data of the one or more zones.

In another exemplary embodiment, when the humidity sensor corresponds to the resistive humidity sensor. The resistive humidity sensor may comprise at least two electrodes coated with a layer of moisture sensitive material. Further, the resistive humidity sensor may be configured to provide the one or more signals proportional to the humidity of the one or more zones. In another exemplary embodiment, when the humidity sensor corresponds to the thermal humidity sensor. The thermal humidity sensor may be configured to generate the one or more signals corresponding to the humidity of the one or more zones. Further, the one or more zones may correspond to the humidity data of the one or more zones.

In some embodiments, the plurality of zone level occupancy sensors may comprise at least one lightning sensors, Wi-Fi Access Points and Bluetooth low energy (BLE) sensors, access readers, and/or CO₂ sensor. In some embodiments, the plurality of zone level occupancy sensors may be configured to detect the occupancy data of the one or more zones. In an exemplary embodiment, when the at least one lightning sensors may be configured to determine the occupancy data of the one or more zones. Further, the at least one lightening sensors may be configured to detect disturbances or changes in the electromagnetic field of the one or more zones, caused by human presence. The at least one lightning sensors may utilize passive infrared (IR) signals which detects infrared radiations emitted by the human body.

In another exemplary embodiment, the Wi-Fi Access Points and Bluetooth low energy (BLE) sensors may be configured to determine the occupancy data of the one or more zones. Further, the Wi-Fi Access Points and Bluetooth low energy (BLE) sensors may be configured to detect presence of the devices equipped with Wi-Fi or BLE capabilities. The Wi-Fi access points may be configured to monitor the signals from nearby devices connected to the network. Further, the BLE sensors may be configured to determines presence of the BLE-enabled devices in proximity. In some embodiments, when the devices equipped with the Wi-Fi or BLE capabilities connects/disconnects, the occupancy data may be detected by the Wi-Fi Access Points and Bluetooth low energy (BLE) sensors. In some embodiments, the one or more sensors 106 may be configured to provide the temperature data, humidity data and the occupancy data to the server 108 in real time. Further, the server 108 may be configured to regulate operation of the HVAC unit 104 to maintain the ambient temperature inside the building, based at least on the temperature data, humidity data and the occupancy data.

In one example embodiment, the building may be a corporate building for accommodating employees. The HVAC unit 104 installed within the corporate building may be configured to provide a comfortable environment at the one or more zones of the building such as a reception, cafeteria and hall-way. The heating or cooling cycle may be initiated in case of any uncomfortable temperature inside the corporate building. Further, the one or more sensors 106 may be configured to detect the temperature data, humidity data and the occupancy data of the corporate building. Further, in case of any sudden change in the temperature data, humidity data or the occupancy data, the one or more sensors 106 may be configured to provide the temperature data, the humidity data to the server 108. The server 108 further controls operation of the HVAC unit 104.

In some embodiments, the server 108 may be a computer or software module that is configured to provide centralized resources, data, or services to the user device 110 operated by a user. The server 108 may be configured to handle and manage one or more computational tasks and data processing within the system 100. In some embodiments, the server 108 may include storage systems, such as hard drives or storage arrays, to store and manage large volumes of data and information accessible to network users. In some embodiments, the server 108 may further provide centralized control and management capabilities, allowing network administrators to configure, monitor, and maintain network resources, security settings, and user access permissions from a single location.

In some embodiments, the server 108 may be configured to receive the temperature data, humidity data and the occupancy data via the network 102. In some embodiments, the server 108 may be configured to receive the temperature data, humidity data and the occupancy data from the one or more zones within the building. In some embodiments, the server 108 may be provided with a historical temperature data, historical temperature set points and one or more input conditions. In some embodiments, the one or more input conditions further comprises the one or more static parameters of each zone. Further, the one or more static parameters may comprise at least one of size of the one or more zones, shape of the one or more zones, a number of door openings in the one or more zones, a number of windows and doors present in each zone floor height of the one or more zones, or location of the one or more zones.

In some embodiments, the server 108 may be configured to determine a threshold time period to achieve the ambient temperature within the one or more zones when occupied, based on the historical temperature data, the historical temperature set points and the one or more input conditions along with the temperature data, humidity data and the occupancy data from the one or more zones within the building in real time. Further, the server 108 may be configured to determine one or more temperature set points to achieve the ambient temperature within the building. In some embodiments, the server 108 may determine a threshold time period to achieve the ambient temperature within the building using one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques.

In one example embodiment, the one or more AI/ML techniques may correspond to natural language processing (NLP), clustering or unsupervised learning, reinforcement learning (RL) or any other AI/ML techniques known in the art. For instance, the NLP may enable the system 100 to interpret and analyze textual data from one or more sources such as maintenance logs or sensor readings. Additionally, clustering or unsupervised learning may be employed to categorize the temperature data based on similarity or patterns, to facilitate the identification of recurring issues or anomalies. Furthermore, the RL technique may be utilized to dynamically adjust the ambient temperature thresholds or response strategies based on the temperature data and feedback, to optimize the server 108 performance over time. The one or more AI/ML techniques may enable the server 108 to autonomously learn, adapt, and improve a signal generation process, to provide actionable insights and support proactive maintenance efforts.

In some embodiments, the server 108 may be configured to operate the HVAC unit 104 via the network 102 to achieve the ambient temperature inside the building for the threshold time period at the one or more temperature set points. Further, the HVAC unit 104 may comprise a Variable Air Volume (VAV) controller (not shown), a VAV box (not shown), and a building management supervisor (BMS) (not shown). Further, the HVAC unit 104 may be installed at various points at the one or more zones. In some embodiments, the server 108 may be communicatively paired with the VAV controller. Further, the server 108 may be configured to provide input to the VAV controller. In some embodiments, the input may correspond to the one or more temperature set points. Further, the at least one controller may be configured to drive the one or more heating or cooling actuators to initialize the heating cycle or the cooling cycle.

In one case, when the VAV controller may be configured to drive the one or more heating actuator to increase the temperature of the one or more zones, based at least on the threshold time period required for each of the one or more zones to heat at one or more temperature set points. In another case, when the VAV controller may be configured to drive the one or more cooling actuator to decrease the temperature of the one or more zones, based at least on the threshold time period required for each of the one or more zones to cool at one or more temperature set points

In some embodiments, the server 108 may further be configured to send the one or more temperature set points along with the threshold time period to achieve the ambient temperature within the building to the user device 110. The user device 110 may be equipped by a manager of the building or other service professionals responsible for addressing the threshold time period and the one or more temperature set points to achieve the ambient temperature inside the building. In some embodiments, the generated threshold time period and the one or more temperature set points by the server 108 may provide a summarized temperature data to the user that is easy to understand and take action. In some embodiments, the user device 110 may include personal computers such as desktop computers, laptop computers, tablets, smartphones, or mobile devices.

It will be apparent to one skilled in the art that above-mentioned components of the system 100 have been provided only for illustration purposes, without departing from the scope of the disclosure.

FIG. 2 illustrates a block diagram of the server 108, in accordance with an example embodiment of the present disclosure. The server 108 comprises at least one processor 202 having a memory 204, a machine learning (ML) model 206, an input/output circuitry 208, and a communication circuitry 210.

In some embodiments, the one or more sensors 106 may be communicatively paired with the at least one processor 202 of the server 108. The at least one processor 202 may include suitable logic, circuitry, and/or interfaces that are operable to execute one or more instructions stored in the memory 204 to perform predetermined operations. In one embodiment, the at least one processor 202 may be configured to decode and execute any instructions received from one or more other electronic devices or server(s). The at least one processor 202 may be configured to execute one or more computer-readable program instructions, such as program instructions to carry out any of the functions described in this description. Further, the at least one processor 202 may be implemented using one or more processor technologies known in the art. Examples of the at least one processor 202 includes, but are not limited to, one or more general purpose processors and/or one or more special purpose processors.

Further, the at least one processor 202 may be configured to receive the temperature data, the humidity data and the occupancy data of one or more zones from the one or more sensors 106 via the network for a pre-defined threshold time period. In some embodiments, the temperature data, the humidity data and the occupancy data may be received from the one or more sensors 106 for the pre-defined threshold time period. In some embodiments, the historical temperature data, a historical humidity data, and a historical occupancy data may correspond to one or more input conditions.

In some embodiments, the at least one processor 202 may include suitable logic, circuitry, and/or interfaces that are operable to execute one or more instructions stored in the memory 204 to perform predetermined operations. In one embodiment, the at least one processor 202 may be configured to decode and execute any instructions received from one or more other electronic devices or server(s). The at least one processor 202 may be configured to execute one or more computer-readable program instructions, such as program instructions to carry out any of the functions described in this description. Further, the at least one processor 202 may be implemented using one or more processor technologies known in the art. Examples of the at least one processor 202 include, but are not limited to, one or more general purpose processors and/or one or more special purpose processors (e.g., digital signal processors or Field Programmable Gate Array (FPGA) processor). In some embodiments, the at least one processor 202 may be configured to store the historical temperature set points, the historical temperature data, the historical humidity data, and the historical occupancy data into the memory 204.

Further, the memory 204 may also be configured to store a set of instructions and data executed by the at least one processor 202. The memory 204 may include the one or more instructions that are executable by the at least one processor 202 to perform specific operations. It is apparent to a skilled artisan that the one or more instructions stored in the memory 204 enable the hardware of the system 100 to perform the predetermined operations. Some of the commonly known memory 204 implementations include, but are not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, Compact Disc Read-Only Memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, Random Access Memories (RAMs), Programmable Read-Only Memories (PROMs), Erasable PROMs (EPROMs), Electrically Erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions.

In some embodiments, the at least one processor 202 may be configured to fetch the historical temperature data, the historical humidity data, the historical occupancy data and the static parameters from the memory 204. Further, the at least one processor 202 may be configured to provide one or more datasets over a period of time, based at least on the historical temperature data, the historical humidity data, the historical occupancy data and the static parameters fetched from the memory 204. Further, the at least one processor 202 may be configured to train the ML model 206, based at least on the one or more datasets.

In some embodiments, the at least one processor 202 may be configured to validate the trained ML model 206 using one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. For instance, the at least one processor 202 may employ supervised learning algorithms such as linear regression or decision trees to validate the one or more datasets based on the historical temperature data, the historical humidity data, the historical occupancy data and the static parameters. In some embodiments, the at least one processor 202 may be configured to determine the threshold time period to achieve heating and cooling temperature within the one or more zones when occupied using the trained ML model 206. In some embodiments, the threshold time may correspond to time required for each zone to achieve heating or cooling when occupied.

In an exemplary embodiment, the trained ML model 206 may correspond to a time recovery model. In some embodiments, the at least one processor 202 may be configured to determine the threshold time for heating or cooling the one or more zones by using the time recovery model. Further, the time recovery model utilizes the historical temperature data and the one or more input conditions to determine the threshold time. the time recovery model may be trained by the one or more datasets. Further, the one or more datasets may comprise data regarding the performance of the HVAC unit 104 in response to the temperature data, the humidity data and the occupancy data. Further, the time recovery model may be configured to learn dynamic relationships between the temperature data, the humidity data and the occupancy data and a period of time utilized by the one or more zones to recover to the heating or cooling temperature.

Further, the time recovery model may be configured to assess the temperature data, the humidity data and the occupancy data to determine the threshold time period for the one or more zones to recover to the heating or cooling temperature. In some embodiments, the at least one processor 202 may be configured to adjust the one or more temperature set points for the one or more zones. Further, the at least one processor 202 may be configured to adjust the one or more temperature set points at the determined threshold time period using the trained ML model 206. Further, the at least one processor 202 may be configured to achieve the heating or cooling temperature within the one or more zones when occupied by adjusting the one or more temperature set points.

In some embodiments, the one or more temperature set points may comprise at least one heating set point that initializes a heating cycle to increase the temperature of the one or more zones. Further, the one or more temperature set points may comprise at least one cooling set point that initializes a cooling cycle to decrease the temperature of the one or more zones. The heating cycle or the cooling cycle may be initialized based at least on the threshold time required for each of the one or more zones to heat or cool at one or more temperature set points. In some embodiments, the heating cycle and the cooling cycle may be identified by change in the one or more temperature set points. Further, the heating cycle and the cooling cycle may end when the temperature of the one or more zones reaches the one or more temperature set points.

In some embodiments, the system 100 may further comprise the input/output circuitry 208. The input/output circuitry 208 may enable a user to communicate or interface with the system 100, via the user device 110. The one or more user devices may include N number of user devices. In some embodiments, the input/output circuitry 208 may act as a medium to transmit input from the interface to and from the system 100. In some embodiments, the input/output circuitry 208 may refer to the hardware and software components that facilitate the exchange of information between one or more user devices and the system 100. In one example, the system 100 may include a graphical user interface (GUI) (not shown) as input circuitry to allow the one or more users to input data. The input/output circuitry 208 may include various input devices such as keyboards, barcode scanners, GUI for the one or more users to provide data and various output devices such as displays, printers for the one or more users to receive data. In another example, the input/output circuitry 208 may include various output circuitry such as a display to show the adjusted one or more temperature set points.

In some embodiments, the system 100 may further comprise the communication circuitry 210. The communication circuitry 210 may allow the system 100 to exchange data or information with other system 100s or apparatuses. Further, the communication circuitry 210 may include network interfaces, protocols, and software modules responsible for sending and receiving data or information. In some embodiments, the communication circuitry 210 may include Ethernet ports, Wi-Fi adapters, or communication protocols like HTTP or MQTT for connecting with other systems. The communication circuitry 210 may further include components such as communication modules (e.g., Wi-Fi, Ethernet, cellular), transceivers, antennas, and protocols (e.g., TCP/IP, MQTT, SNMP) for exchanging data with other systems or network devices. The communication circuitry 210 may allow the system 100 to stay up-to-date and accurately track the at least one normalized alerts.

In some embodiments, the input/output circuity and the communication circuitry 210 may be configured to integrate the at least one normalized alarm data with other systems such as Supervisory Control and Data Acquisition (SCADA), Building Management Systems (BMS), Enterprise Asset Management (EAM) systems, or third-party monitoring platforms for centralized monitoring, analysis, and control by operators and automated processes.

It will be apparent to one skilled in the art that above-mentioned components of the system 100 have been provided only for illustration purposes, without departing from the scope of the disclosure.

FIG. 3 illustrates an exemplary scenario of an architecture 300 of the system 100 to achieve heating or cooling within one or more zones at a threshold time period. FIG. 3 is described in conjunction with FIGS. 1 and 2.

In some embodiments, the architecture 300 of the system 100 may include one or more zones 302 of the building, the one or more sensors 106 comprising an occupancy sensor 304, a temperature sensor 306, and a humidity sensor 308. Further, the architecture 300 of the system 100 may include a variable air volume (VAV) controller 310, a VAV box 312, and a building management supervisor (BMS) 314.

In some embodiments, the occupancy sensor 304, the temperature sensor 306, and the humidity sensor 308 may collect real-time data within the one or more zones 302. The collected data may provide inputs comprising the occupancy data, the temperature data and the humidity data. The collected data may be processed by the VAV controller 310 to adjust the airflow to the one or more zones 302 based at least on the occupancy data, the temperature data and the humidity data. The airflow may be adjusted for optimal comfort while minimizing energy consumption. Further, the VAV box 312 may work in tandem with the VAV controller 310 to regulate the volume of conditioned air delivered to one or more zones 302, to achieve to achieve heating or cooling temperature within the one or more zones 302 at the one or more temperature set points.

In some embodiments, the VAV box 312 may be integrated with the HVAC unit 104 in Heating, Ventilation and Air Conditioning (HVAC) unit and one or more heating or cooling actuators. Further, the VAV controller 310 may be configured to operate the one or more heating or cooling actuators to achieve heating or cooling within the one or more zones 302 at the determined threshold time period. In some embodiments, the VAV box 312 may comprise a damper 316. In some embodiments, the damper 316 may be configured to regulate the volume of the conditioned air entering into the one or more zones 302.

In some embodiments, the damper 316 may comprise a plurality of blades 318. In some embodiments, the plurality of blades 318 may be configured to move from a first position to a second position to regulate the volume of the conditioned air entering into the one or more zones 302. In some embodiments, the movement of the plurality of blades 318 from the first position to the second position may be provided by at least one motorized actuator. In some embodiments, when the temperature data of the real time deviates from the one or more temperature set points, the at least one processor 202 provides at least one signal to the VAV controller 310. Further, the VAV controller 310 direct the at least one motorized actuator to change positioning of the plurality of blades 318.

In some embodiments, the VAV box 312 may be connected with the one or more zones 302 through at least one duct 320. In some embodiments, the at least one duct 320 may be configured to enable transferring of the conditioned air from the VAV box 312 to the one or more zones 302. Further, the at least one duct 320 may be coated with a material that may prevent any deviation in the temperature of the conditioned air transferred from the VAV box 312 to the one or more zones 302.

In some embodiments, the architecture 300 of the system 100 may further comprise the BMS 314. In some embodiments, the BMS 314 may be configured to coordinate the operation of the VAV box 312 through the VAV controller 310. In some embodiments, the BMS 314 may correspond to a central control system for operating the VAV box 312. Further, the BMS 314 may be configured to receive input from the at least one processor 202. Further, the input may correspond to the temperature data, the humidity data and the occupancy data of real-time. In some embodiments, when the temperature data diverges from the one or more temperature set points, the BMS 314 communicates with the VAV controller 310. In some embodiments, upon receiving command from the BMS 314, the VAV controller 310 may be configured to adjust positioning of the damper 316 to regulate the volume of the conditioned air entering into the one or more zones 302. Further, the BMS 314 may monitor and coordinate the operation of the components of the architecture 300 to facilitate communication and integration within the system 100.

Further, the one or more sensors 106 and the VAV controller 310 may operate synergistically to achieve heating or cooling temperature within the one or more zones 302 at the one or more temperature set points, when occupied using the trained ML model 206. By continuously monitoring the occupancy data, the temperature data and the humidity data, the one or more sensors 106 may provide valuable inputs to the VAV controller 310 to dynamically adjust airflow and ventilation strategies in response to achieve heating or cooling temperature within the one or more zones 302. The VAV box 312 may serves as a critical intermediary, executing the VAV controller 310 to modulate the airflow delivered to individual zone of the one or more zones 302, thereby ensuring precise climate control tailored to the temperature data, the humidity data and the occupancy data. Further, the BMS 314 supervisor may provide the supervision and management to maintain the interactions between components and ensures the smooth operation of the system 100. The architecture 300 may provide an integrated framework to enhance one or more occupants comfort to optimize energy efficiency, and enable management of the one or more zones 302.

FIG. 4 illustrates a flowchart of a method 400 to achieve heating or cooling within the one or more zones 302 at the threshold time period, in accordance with an example embodiment of the present disclosure. FIG. 4 is described in conjunction with FIGS. 1-3.

At operation 402, the at least one processor 202 may be configured to train a ML model 206 using the historical temperature data and the one or more input conditions of the one or more zones 302. In some embodiments, the historical temperature data may correspond to the temperature data of the one or more zones 302 monitored for the pre-defined threshold time period. Further, the one or more input conditions may comprise the historical temperature data, and the historical occupancy data and the one or more statistical parameters. Further, the one or more statistical parameters comprises at least one of size of the one or more zones 302, shape of the one or more zones 302, a number of door openings in the one or more zones 302, a number of windows and doors present in each zone, floor height of the one or more zones 302, or location of the one or more zones 302.

For example, the at least one processor 202 train the ML model 206 based at least on a historical temperature data and one or more input conditions of a conference room. The at least one processor 202 fetches the historical data and the one or more input conditions from memory 204 and trains the ML model 206.

At operation 404, the at least one processor 202 may be configured to receive the temperature data from the one or more sensors 106 and the one or more input conditions of the one or more zones 302 for the pre-defined threshold time period in real-time. In some embodiments, the one or more sensors 106 may comprise the temperature sensor 306, the humidity sensor 308, and the occupancy sensor 304. In some embodiments, the one or more input conditions may further comprise the humidity data, and the occupancy data. In some embodiments, the occupancy data may correspond to number of occupants present inside the one or more zones 302.

For example, the at least one processor 202 receives temperature data, and the one or more input conditions from the one or more sensors 106. Further, the one or more sensors 106 may correspond to the occupancy sensor 304, temperature sensor 306, and the humidity sensor 308. The one or more sensors 106 detects temperature and humidity inside the conference room along with presence of occupants inside the conference room.

At operation 406, the at least one processor 202 may be configured to determine the threshold time period to achieve heating or cooling temperature within the one or more zones 302 when occupied using the trained ML model 206, based at least one the received temperature data and the one or more input conditions of the one or more zones 302 for the predefined time period. In some embodiments, the at least one processor 202 may be configured to provide one or more datasets to the trained ML model 206. In some embodiments, the trained ML model 206 may be configured to validate the training data by using one or more model 206s and one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. Further, upon validating the one or more datasets, the at least one processor 202 may be configured to determine the threshold time period to achieve heating or cooling temperature of each of the one or more zones 302.

For example, the at least one processor 202 validates the trained ML model 206 by using one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. Further, the at least one processor 202 determines the threshold time period to achieve heating or cooling of the one or more zones 302.

At operation 408, the at least one processor 202 may be configured to adjust the one or more temperature set points for the one or more zones 302 at the determined threshold time period using the trained ML model 206 to achieve heating or cooling temperature within the one or more zones 302 when occupied. In some embodiments, the one or more temperature set points may comprise the at least one heating set point and the at least one cooling set point. Further, the one or more temperature set points may be configured to initiate the heating cycle of the HVAC unit 104 and the cooling cycle the HVAC unit 104 to increase or decrease temperature of the one or more zones 302.

For example, the at least one processor 202 adjusts the one or more temperature set points for the conference room at the determined threshold time period using the trained ML model 206 to achieve the heating or cooling temperature within the conference room when occupied. Further, the heating or cooling is initiated by a HVAC unit 104 installed on the conference room.

FIG. 5A illustrates a graphical representation 500 of weekly seasonality of the trained ML model 206, in accordance with an example embodiment of the present disclosure. FIG. 5B illustrates a graphical representation 504 of daily seasonality of the trained ML model 206, in accordance with an example embodiment of the present disclosure.

In some embodiments, the graphical representation 500, 504 of the system 100 may represent weekly and daily seasonality parts of the trained ML model to determine occupancy trends. In some embodiments, the curve 500 may correspond to a day to week standpoint of the trained ML model. Further, the curve 504 as shown in FIG. 5B may represent a time of day standpoint of the trained ML model. In some embodiments, the graphical representation 500 and the graphical representation 504 of the system 100 may represent weekly and daily seasonality parts of the trained ML model 206 to determine the threshold time period to achieve heating or cooling temperature for the one or more zones 302.

FIG. 6 illustrates a graphical representation 600 of the time recovery curve associated with the system 100, in accordance with one or more embodiments of the present disclosure. FIG. 6 is described in conjunction with FIGS. 1-5B.

In some embodiments, the time recovery curve may be configured to represent response of the ML model 206 to adjust the HVAC unit 104 using the at least one processor 202, based at least on the historical temperature data. Further, the HVAC unit 104 may experience one or more disturbances while initiating the heating cycle or the cooling cycle. In some embodiments, the disturbances may comprise at least one of the temperature data, the humidity data or the occupancy data. Further, due the one or more disturbances the HVAC unit 104 may experience deviations from the one or more temperature set points determined by the at least one processor 202.

In some embodiments, the time recovery curve may comprise a first curve 602 representing response of the ML model 206 and a second curve 604 representing the historical temperature data. The graphical representation 600 comprises an X-axis and a Y-axis. Further, the X-axis may correspond to a period of time took by the HVAC unit 104 to recover to the one or more temperature set points. The Y-axis may correspond to the one or more temperature set points.

FIG. 7 illustrates a table 700 having calculation of the ML model 206 for initiating a cooling cycle for the one or more zones 302 for a month, in accordance with an example embodiment of the present disclosure.

In some embodiments, the table 700 may comprise a plurality of rows and columns. The plurality of columns may correspond to estimated records (e.g., YHAT) 702, DATE 704, TIME_TO_RECOVERY 706, DELTA 708, SWITCH_SETPOINT_AT 710, ORIGINAL_SETPOINT_CHANGE 712 and TIME_SAVED 714. In some embodiments, the table 700 includes data associated with the cooling cycle of the HVAC unit 104 to achieve the heating or cooling temperature for the one or more zones 302 to achieve the one or more temperature set points.

The present disclosure may provide efficiency, comfort, and energy optimization. Firstly, the system 100 trains the ML model 206 by using the historical temperature data and the one or more input conditions along with temperature data, humidity data and occupancy data in real-time, fetched by the at least one processor 202 to predict the threshold time period for each of the one or more zones 302 to achieve cooling or heating temperature. Further, the system 100 may ensure low energy consumption and proper comfort for the occupants of the one or more zones 302. Secondly, the system 100 may adjust the one or more temperature set points for the one or more zones 302 by using the trained ML model 206 to ensure adaptability of the system 100 in accordance with any variations of the temperature data, humidity data and occupancy data in real-time. Further, the system 100 may maintain optimal indoor environmental conditions to enhance occupant comfort and productivity, contributing to a healthier and more conducive working or living environment. Additionally, the system's ability to adapt and respond dynamically to changing occupancy patterns and environmental conditions may allow for better utilization of space and resources within the building, maximizing operational efficiency and space utilization. Further, the system 100 may provide a centralized management provided by the building management supervisor to streamline system operation, and thereby facilitating remote monitoring and control, and enabling proactive maintenance and troubleshooting, ensuring continuous and reliable performance.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method, or computer program product. Accordingly, aspects of various embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module”, “system” or “sub-system.” In addition, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

The foregoing descriptions of specific embodiments have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the embodiments to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain principles and practical applications thereof, and to thereby enable others skilled in the art to best utilize the various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but these are intended to cover the application or implementation without departing from the spirit or scope of the claims. The following claims are in no way intended to limit the scope of embodiments to the specific embodiments described herein.