METHODS AND SYSTEMS FOR DETERMINING AN OPTIMAL START AND STOP TIME OF AIR HANDLING UNITS

Description

TECHNOLOGICAL FIELD

The present invention relates to heating, ventilation, and air conditioning (HVAC) systems, and more particularly relates to a method and system for determining an optimal start and stop time (OSS) of an Air Handling Unit (AHU) of a HVAC system.

BACKGROUND

Conventional heating, ventilation, and air conditioning (HVAC) systems comprising chillers, boilers, Air Handling Units (AHUs), heat pumps, and cooling towers and condensers, follow fixed schedule for heating or cooling a building. The fixed schedule is formed without considering occupancy factors, temperature factors, and other similar factors of the building. Further, the fixed schedule leads to excessive energy use by the AHUs as well as discomfort for occupants inside the building. Further, optimal control of the AHUs is crucial for effective heating or cooling within the building. However, the present AHUs do not factor in the use of occupancy data to predict when heating or cooling will be needed in a particular zone in a building, and to start and stop the AHUs accordingly. The start and stop time of the AHUs not only ensures occupants comfort but also saves energy being used by the AHUs. Therefore, the current AHUs are not able to optimize energy-saving measures alongside existing optimizations, and fail to offer a significant opportunity for overall energy conservation.

The inventors have identified numerous areas of improvement in the existing technologies and processes, which are the subjects of embodiments described herein. Through applied effort, ingenuity, and innovation, many of these deficiencies, challenges, and problems have been solved by developing solutions that are included in embodiments of the present disclosure, some examples of which are described in detail herein.

BRIEF SUMMARY

The following presents a simplified summary in order 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 one example embodiment, a method is disclosed. The method comprises receiving, via at least one processor, a set of parameters associated with one or more zones in a real time. The set of parameters comprises one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. Further, the method comprises determining, via the at least one processor, an occupancy status of each of the one or more zones in the real time, based at least on the received set of parameters. Further, the method comprises determining, via the at least one processor, at least one start time and stop time for an Air Handling Unit (AHU) for each of the one or more zones using historical data and each model of a plurality of models. The plurality of models comprises at least one of a linear model, a non-linear model, and an artificial intelligence (AI) based model. The AI based model uses an AI technique for determining the at least one start time and stop time. Further, the method comprises determining, via the at least one processor, a weighted average value for the determined at least one start time and stop time for the AHU, based at least on a predefined weight allocated to each of the at least one start time and stop time determined for each model. The predefined weight corresponds to a value assigned to the at least one start time and stop time determined using each model and is fine-tuned using the AI technique. Thereafter, the method comprises determining, via the at least one processor, an optimal start and stop (OSS) time of the AHU for each of the one or more zones using the plurality of models, based at least on the determined weighted average value.

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 one or more static parameters comprises at least one of wall thermal resistance, heat transfer coefficient, and convection and conduction heat transfer of each of the one or more zones over a predefined period of time and in the real time.

In some embodiments, the one or more internal parameters comprises at least one of lowest cooling time for optimal cooling, high speed cooling factor, maximum outside temperature for switching of the AHU, and optimum stop factor over a predefined period of time and in the real time.

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 over the predefined period of time and in the real time.

In some embodiments, the temperature data corresponds to one or more temperature set points, temperature inside of each of the one or more zones, and temperature outside of each of the one or more zones over a predefined period of time and in the real time. Further, the predefined period of time corresponds to a monitored time period comprising at least one of hours, days, months, quarters, or years in which the set of parameters is received. In some embodiments, the optimal start time corresponds to a time at which the AHU gets actuated before occupancy within the one or more zones to achieve the one or more temperature set points at the time of occupancy within the one or more zones, and the optimal stop time corresponds to a time at which the AHU gets deactivated before an end of the occupancy within the one or more zones to maintain the one or more temperature set points till the end of the occupancy within the one or more zones.

In some embodiments, the method further comprises switching, via the at least one processor, between a free cooling mode and a mechanical mode for the AHU based at least on the temperature data of each of the one or more zones. In some embodiments, the free cooling mode is activated when the temperature outside of each of the one or more zones is less than the temperature inside of each of the one or more zones, and the mechanical mode is activated when the temperature outside of each of the one or more zones is greater than the temperature inside of each of the one or more zones. In some embodiments, the at least one processor is configured to conserve energy consumed by the AHU by deactivating or reducing cooling capacity of the AHU in the free cooling mode, and the at least one processor is configured to operate the AHU based at least one the determined OSS for each of the one or more zones in the mechanical mode.

In some embodiments, the set of parameters are received from a building management system (BMS) comprising one or more sensors, wherein the one or more sensors comprises at least one of a temperature sensor, a humidity sensor, or an occupancy sensor.

In some embodiments, the method further comprises sending, via the at least one processor, one or more commands to the AHU for each of the one or more zones for reaching the one or more temperature set points based at least on the determined OSS time. The one or more commands corresponds to an ON command or an OFF command.

In some embodiments, the linear model and the non-linear model use one or more mathematical techniques for determining the at least one start time and stop time.

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 receive a set of parameters associated with one or more zones in a real time. The set of parameters comprises one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. Further, the at least one processor is configured to determine an occupancy status of each of the one or more zones in the real time, based at least on the received set of parameters. Further, the at least one processor is configured to determine at least one start time and stop time for an Air Handling Unit (AHU) for each of the one or more zones using historical data and each model of a plurality of models. The plurality of models comprises at least one of a linear model, a non-linear model, and an artificial intelligence (AI) based model. The AI based model uses an AI technique for determining the at least one start time and stop time. Further, the at least one processor is configured to determine a weighted average value for the determined at least one start time and stop time for the AHU, based at least on a predefined weight allocated to each of the at least one start time and stop time determined for each model. The predefined weight corresponds to a value assigned to the at least one start time and stop time determined using each model and is fine-tuned using the AI technique. Thereafter, the at least one processor is configured to determine an optimal start and stop (OSS) time of the AHU for each of the one or more zones using the plurality of models, based at least on the determined weighted average value.

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 receiving a set of parameters associated with one or more zones in a real time, wherein the set of parameters comprises one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones; determining an occupancy status of each of the one or more zones in the real time, based at least on the received set of parameters; determining at least one start time and stop time for an Air Handling Unit (AHU) for each of the one or more zones using historical data and each model of a plurality of models, wherein the plurality of models comprises at least one of a linear model, a non-linear model, and an artificial intelligence (AI) based model, and wherein the AI based model uses an AI technique for determining the at least one start time and stop time; determining a weighted average value for the determined at least one start time and stop time for the AHU, based at least on a predefined weight allocated to each of the at least one start time and stop time determined for each model, wherein the predefined weight corresponds to a value assigned to the at least one start time and stop time determined using each model and is fine-tuned using the AI technique; and determining an optimal start and stop (OSS) time of the AHU for each of the one or more zones using the plurality of models, based at least on the determined weighted average value.

The above summary is provided merely for purposes of summarizing some example embodiments to provide a basic understanding of some aspects of the invention. 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 invention in any way. It will be appreciated that the scope of the invention encompasses many potential embodiments in addition to those here summarized, some of which will be further described below.

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 for determining an optimal start and stop time (OSS) of an Air Handling Unit (AHU) of a heating, ventilation, and air conditioning (HVAC) 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. 3A illustrates a user interface (UI) showing one or more internal parameters of one or more zones in accordance with an example embodiment of the present disclosure;

FIG. 3B illustrates the UI of a building management system (BMS) for determining the start time and the stop time of the AHU in accordance with an example embodiment of the present disclosure;

FIG. 4 illustrates a flowchart showing a method for determining at least one start time and stop time for the AHU using a linear model in accordance with an example embodiment of the present disclosure;

FIG. 5A illustrates a graphical representation of determination of at least one start time for the AHU using the linear model in accordance with an example embodiment of the present disclosure;

FIG. 5B illustrates a graphical representation of determination of at least one stop time for the AHU using the linear model in accordance with an example embodiment of the present disclosure;

FIG. 6 illustrates a flowchart showing a method for determining at least one start time and stop time for the AHU using a non-linear model in accordance with an example embodiment of the present disclosure;

FIG. 7 illustrates a flowchart showing a method for determining at least one start time and stop time for the AHU using an artificial intelligence (AI) based model in accordance with an example embodiment of the present disclosure;

FIG. 8 illustrates a block diagram showing determination of a weighted average value, based at least on a predefined weight allocated to each of the at least one start time and stop time determined using the linear model, the non-linear model, and the AI based model in accordance with an example embodiment of the present disclosure;

FIG. 9 illustrates switching between a free cooling mode and a mechanical mode of the AHU in accordance with an example embodiment of the present disclosure; and

FIG. 10 illustrates a flowchart showing a method for determining optimal start and stop time (OSS) of the AHU in accordance with an example embodiment of the present disclosure.

DETAILED DESCRIPTION

Some embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments are shown. Indeed, various embodiments may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. As discussed herein, the protection devices may be referred to use by humans, but may also be used to raise and lower objects unless otherwise noted.

The components illustrated in the figures represent components that may or may not be present in various embodiments of the invention described herein such that embodiments may include fewer or more components than those shown in the figures while not departing from the scope of the invention. Some components may be omitted from one or more figures or shown in dashed line for visibility of the underlying components.

The present disclosure provides various embodiments of methods and systems for determining optimal start and stop time of an Air Handling Unit (AHU). Embodiments may be configured to be executed by at least one processor for determining the optimal start and stop time of the AHU. Embodiments may be configured to receive a set of parameters associated with one or more zones in a real time, comprising one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. Embodiments may be configured to determine an occupancy status of each of the one or more zones in the real time, based at least on the received set of parameters.

Embodiments may be configured to determine at least one start time and stop time for an Air Handling Unit (AHU) for each of the one or more zones using historical data and each model of a plurality of models. The plurality of models comprising at least one of a linear model, a non-linear model, and an artificial intelligence (AI) based model. Embodiments may be configured to determine a weighted average value for the determined at least one start time and stop time for the AHU, based at least on a predefined weight allocated to each of the at least one start time and stop time determined for each model. Embodiments may be configured to determine an optimal start and stop (OSS) time of the AHU for each of the one or more zones using the plurality of models, based at least on the determined weighted average value.

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) system 104 of one or more zones (not shown), one or more sensors 106, a server 108, and a user device 110. Further, the HVAC system 104 may comprise an Air Handling Unit (AHU) 112.

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.

In some embodiments, the network 102 may be communicatively coupled to the HVAC system 104. The HVAC system 104 may be installed in one or more zones. In some embodiments, the one or more zones may comprise at least one of a building, a warehouse, a storage unit, or an office space. Further, the HVAC system 104 may be configured to regulate an indoor environment of the one or more zones by controlling temperature, humidity, and air quality inside the one or more zones. The HVAC system 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 one or more zones. In some embodiments, the HVAC system 104 may be operated in one or more cycles in response to temperature variations inside the one or more zones.

Further, when heating is required, the HVAC system 104 may be configured to provide warm air inside the one or more zones. Further, when cooling is required, the HVAC system 104 may provide cold air inside to one or more zones to maintain the ambient temperature inside the one or more zones. In some embodiments, the HVAC system 104 installed in the one or more zones may be designed to maintain an ambient temperature within the one or more zones. In some embodiments, the HVAC system 104 may be installed in other various types of one or more zones, including residential homes, commercial establishments, industrial facilities, and institutional buildings. In some embodiments, the HVAC system 104 installed within the one or more zones 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 in a real time.

In some embodiments, the HVAC system 104 may comprise the AHU 112. The AHU 112 may be responsible for circulating and conditioning air within one or more zones. The AHU 112 may draw air from either outside or recirculate indoor air, passing the air through filters to remove contaminants like dust and pollen, thereby improving indoor air quality. Inside the AHU 112, heating and cooling elements may adjust the air temperature based on thermostat settings, ensuring comfort. Additionally, the AHU 112 may include humidifiers or dehumidifiers to regulate humidity levels. In some embodiments, the AHU 112 may be equipped with fans that may facilitate the movement of air through ductwork, distributing the air evenly throughout the one or more zones. In more complex systems, a group of AHU 112 may be utilized to serve each zone from the one or more zones, each zone equipped with dampers and mixing boxes to regulate airflow, maintain consistent temperature and air quality. The AHU 112 may maintain comfort, air quality, and energy efficiency in diverse residential, commercial, and industrial settings. Furthermore, the AHU 112 may integrate with other components of the HVAC system 104 components to optimize energy efficiency and overall system performance. The AHU 112 may be configured to collaborate closely with heating and air conditioning systems to regulate the temperature of the conditioned air before distribution.

In some embodiments, the one or more sensors 106 may be installed within a building management system (BMS) (not shown). The BMS may be installed at the one or more zones. In some embodiments, the one or more sensors 106 may be configured to determine a set of parameters associated with the one or more zones in a real time. The set of parameters may comprise one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each 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, or an occupancy sensor. Further, the temperature sensor may be configured to detect temperature of the one or more zones. 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, 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 predefined 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 be referred as a temperature based resistor. The thermistor may be configured to generate the at least one signal when supplied with the predefined 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, 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 predefined 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 other embodiments, the humidity sensor may correspond 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 predefined 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 embodiment, the humidity sensor may correspond 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, then 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 occupancy sensor may be configured to detect the occupancy data of the one or more zones. The occupancy sensor may be configured to detect the presence or absence of people within the one or more zones. The occupancy sensor may be configured to detect motion or heat signatures associated with human presence within the one or more zones. 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 the real time. Further, the server 108 may be configured to regulate operation of the AHU 122 of the HVAC system 104 to maintain the ambient temperature inside the one or more zones, based at least on the temperature data, humidity data, and the occupancy data.

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 set of parameters associated with the one or more zones in the real time. The set of parameters may comprise one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. The server 108 may be configured to determine an occupancy status of each of the one or more zones in the real time, based at least on the received set of parameters. The server 108 may be configured to determine at least one start time and stop time for an Air Handling Unit (AHU) for each of the one or more zones using historical data and each model of a plurality of models. The plurality of models may comprise at least one of a linear model, a non-linear model, and an artificial intelligence (AI) based model. In some embodiments, the AI based model uses an AI technique for determining the at least one start time and stop time.

In some embodiments, the server 108 may be configured to determine a weighted average value for the determined at least one start time and stop time for the AHU, based at least on a predefined weight allocated to each of the at least one start time and stop time determined for each model. The predefined weight may correspond to a value assigned to the at least one start time and stop time determined using each model and is fine-tuned using the AI technique. Thereafter, the server 108 may be configured to determine an optimal start and stop (OSS) time of the AHU for each of the one or more zones using the plurality of models, based at least on the determined weighted average value.

In some embodiments, the server 108 may further be configured to send the determined OSS along with the set of parameters to the user device 110. The user device 110 may be equipped by a manager of the one or more zones or other service professionals responsible for addressing the AHU. In some embodiments, the determined OSS by the server 108 may provide a summarized data to the user that is easy to understand and take action to turn on or turn off the AHU. 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. FIG. 2 is described in conjunction with FIG. 1.

In some embodiments, the server 108 may comprise at least one processor 202, a memory 204, an input/output circuitry 206, and a communication circuitry 208. In some embodiments, the at least one processor 202 may be configured to receive the set of parameters associated with the one or more zones in the real time. In some embodiments, the set of parameters may be received from the BMS comprising the one or more sensors 106. Further, the set of parameters may comprise one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. In some embodiments, the one or more static parameters may comprise at least one of wall thermal resistance, heat transfer coefficient, and convection and conduction heat transfer of each of the one or more zones over the predefined period of time and in the real time.

In some embodiments, the one or more internal parameters may comprise at least one of lowest cooling time for optimal cooling, high speed cooling factor, maximum outside temperature for switching of the AHU 112, and optimum stop factor over the predefined period of time and in the real time. In one example embodiment, the lowest cooling time for optimal cooling may correspond to minimum cool down time for optimal start of the AHU 112. In another example embodiment, the high speed cooling factor may correspond to cool down rate for optimal start of the AHU 112. In some embodiments, the occupancy data may comprise occupancy of the one or more zones and the number of occupants within each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the temperature data may correspond to one or more temperature set points, temperature inside of each of the one or more zones, and temperature outside of each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the predefined period of time may correspond to the monitored time period comprising at least one of hours, days, months, quarters, or years in which the set of parameters is received.

In some embodiments, the at least one processor 202 may be configured to determine the occupancy status of each of the one or more zones in the real time, based at least on the received set of parameters. Further, the at least one processor 202 may be configured to determine at least one start time and stop time for the AHU 112 for each of the one or more zones using historical data and each model of the plurality of models. In some embodiments, the plurality of models may comprise at least one of the linear model, the non-linear model, and the AI based model. The linear model and the non-linear model may use one or more mathematical techniques for determining the at least one start time and stop time. Further, the AI based model may use the AI technique for determining the at least one start time and stop time.

In one example embodiment, the one or more mathematical techniques may correspond to one or more mathematical techniques based on physics-based models. The physics-based models may comprise principles of fluid dynamics, heat transfer, and thermodynamics to represent the behavior of the AHU 112. For instance, in a linear model, equations derived from mass and energy balances can be utilized to describe the airflow and temperature changes within each of the one or more zones, allowing for the prediction of at least start time and stop time. In some embodiments, non-linear models may incorporate more intricate equations to capture complex interactions between variables, such as airflow rate, temperature gradients, and the AHU 112 inertia. Furthermore, the one or more mathematical techniques such as numerical simulations, finite element analysis, or computational fluid dynamics may be employed to solve the equations, determine the start time and stop time for the AHU 112. Overall, the one or more mathematical techniques based on physics-based models may offer a robust framework for understanding and optimizing the operation of the AHU 112, ensuring efficient and effective management of indoor environments.

In another example embodiment, the AI technique may correspond to linear regression, non-linear regression, artificial neural networks (ANNs), random forests, and support vector machines (SVMs). The linear regression and non-linear regression may utilize historical data to establish relationships between the set of parameters and the operation of the AHU 112. Additionally, ANNs may leverage complex architecture to autonomously learn and predict the at least one start time and stop time based on the historical data. Further, the ANNs may capture nonlinear relationships and adapt to the set of parameters, to optimize the AHU 112 schedules for energy efficiency and occupant comfort. Furthermore, random forests may aggregate multiple decision trees trained on different subsets of the data, to enhance predictive accuracy and robustness. Further, the SVMs may find the hyperplane that best separates data points into classes or predicts a continuous outcome for modeling complex relationships in the operation of the AHU 112. Additionally, genetic algorithms may be utilized to iteratively evolve optimal solutions by mimicking natural selection processes, improving efficiency and adaptability over time in determining the start and stop time of the AHU 112 based on historical data and the AI based model.

It will be apparent to one skilled in the art that the server 108 may utilize one or more mathematical techniques and AI techniques known in the art, without departing from the scope of the disclosure.

In some embodiments, the at least one processor 202 may be configured to determine the weighted average value for the determined at least one start time and stop time for the AHU 112. The at least one processor 202 may be configured to determine the weighted average value based at least on the predefined weight allocated to each of the at least one start time and stop time determined for each model. Further, the predefined weight may correspond to the value assigned to the at least one start time and stop time determined using each model. The predefined weight may be fine-tuned using the AI technique.

In some embodiments, the at least one processor 202 may be configured to determine the OSS time of the AHU 112 for each of the one or more zones using the plurality of models. The at least one processor 202 may be configured to determine the OSS time based at least on the determined weighted average value. In some embodiments, the optimal start time may correspond to the time at which the AHU 112 gets actuated before occupancy within the one or more zones to achieve the one or more temperature set points at the time of occupancy within the one or more zones. Further, the optimal stop time may correspond to the time at which the AHU 112 gets deactivated before the end of the occupancy within the one or more zones to maintain the one or more temperature set points till the end of the occupancy within the one or more zones.

In some embodiments, the at least one processor 202 may be configured to switch between a free cooling mode and a mechanical mode for the AHU 112 based at least on the temperature data of each of the one or more zones. Further, the free cooling mode may be activated when the temperature outside of each of the one or more zones is less than the temperature inside of each of the one or more zones. And the mechanical mode may be activated when the temperature outside of each of the one or more zones is greater than the temperature inside of each of the one or more zones. In some embodiments, the at least one processor 202 may be configured to conserve energy consumed by the AHU 112 by deactivating or reducing cooling capacity of the AHU 112 in the free cooling mode. In some embodiments, the at least one processor 202 may be configured to operate the AHU 112 based at least one the determined OSS for each of the one or more zones in the mechanical mode.

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. Examples of the at least one processor 202 include, but are not limited to, one or more general purpose processors (e.g., INTEL® or Advanced Micro Devices® (AMD) microprocessors) and/or one or more special purpose processors (e.g., digital signal processors or Xilinx® System On Chip (SOC) Field Programmable Gate Array (FPGA) processor).

In some embodiments, the memory 204 may be configured to store a set of instructions and data executed by the at least one processor 202. Further, the memory 204 may include the one or more instructions that are executable by the at least one processor 202 to perform specific operations. The memory 204 may be configured to include the instructions to receive the set of parameters associated with one or more zones in the real time. The memory 204 may be configured to include the instructions to determine the occupancy status of each of the one or more zones in the real time, based at least on the received set of parameters. Further, the memory 204 may be configured to include the instructions to determine at least one start time and stop time for the AHU 112 for each of the one or more zones using historical data and each model of the plurality of models. The memory 204 may be configured to include the instructions to determine the weighted average value for the determined at least one start time and stop time for the AHU 112. The memory 204 may be configured to include the instructions to determine the OSS time of the AHU 112 for each of the one or more zones using the plurality of models. It is apparent to a person with ordinary skill in the art that the one or more instructions stored in the memory 204 enable the hardware of the server 108 to perform the predetermined operations. Some of the commonly known memory 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 server 108 may further comprise the input/output circuitry 206. The input/output circuitry 206 may enable a user to communicate or interface with the server 108, via the user device 110. The user device 110 may include N number of user devices. In some embodiments, the input/output circuitry 206 may act as a medium to transmit input from the interface to and from the server 108. In some embodiments, the input/output circuitry 206 may refer to the hardware and software components that facilitate the exchange of information between user device 110 and the server 108. In one example, the user device 110 may include a graphical user interface (GUI) (not shown) as an input circuitry to allow the one or more users to input the one or more internal parameters. The input/output circuitry 206 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 206 may include various output circuitry such as a display to show the determined OSS time.

In some embodiments, the server 108 may further comprise the communication circuitry 208. The communication circuitry 208 may allow the server 108 to exchange data or information with other systems or apparatuses. Further, the communication circuitry 208 may include network interfaces, protocols, and software modules responsible for sending and receiving data or information. In some embodiments, the communication circuitry 208 may include Ethernet ports, Wi-Fi adapters, or communication protocols like HTTP or MQTT for connecting with other systems. The communication circuitry 208 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 208 may allow the server 108 to stay up-to-date and accurately track the determined OSS time.

It will be apparent to one skilled in the art the above-mentioned components of the server 108 have been provided only for illustration purposes, without departing from the scope of the disclosure.

FIG. 3A illustrates a user interface (UI) 300 showing the one or more internal parameters of the one or more zones in accordance with an example embodiment of the present disclosure. FIG. 3A is described in conjunction with FIGS. 1-2.

In some embodiments, the UI 300 comprises a cooling case instruction block 302 and a factor adaption selection block 312. The cooling case instruction block 302 may instruct a user to input the one or more internal parameters comprising at least one of lowest cooling time for optimal cooling 304 denoted by “P7”, high speed cooling factor 306 denoted by “P8”, maximum outside temperature 308 for switching of the AHU 112 denoted by “P9”, and optimum stop factor 310 denoted by “P10”, over the predefined period of time and in the real time. In one example embodiment, the user may input the one or more internal parameters as the lowest cooling time for optimal cooling 304 as 0 minutes, the high speed cooling factor 306 as 10 minutes per degree Fahrenheit, the maximum outside temperature 308 for switching of the AHU 112 as 75 degree Fahrenheit, and the optimum stop factor 310 as 10 minutes per degree Fahrenheit.

Further, the UI 300 may comprise the factor adaption selection block 312. The factor adaption selection block 312 may comprise at least one enable button 314, one disable button 316, and one new start/enable button 318. In some embodiments, when the factor adaption selection block 312 may be enabled, then the one or more internal parameters such as high speed cooling factor may get adapted based at least on zone conditions and the type of the zone. Further, the UI 300 may comprise at least one “OK” button 320, and “Cancel” button 322. The “OK” button 320 may be configured to confirm and save the one or more internal parameters provided as input by the user. The “Cancel” button 322 may be configured to discard the one or more internal parameters provided as input by the user in order to again enter the one or more internal parameters.

FIG. 3B illustrates a UI 324 of the BMS for determining the start time and the stop time of the AHU 112 in accordance with an example embodiment of the present disclosure. FIG. 3B is described in conjunction with FIGS. 1-3A.

In some embodiments, the user interface (UI) 324 may comprise a plurality of pins to receive the occupancy data and the temperature data of each of the one or more zones. The plurality of pins may comprise a pin XD1 326, a pin X2 328, a pin X3 330, a pin X4 332, and a pin XD5 334. In some embodiments, the occupancy data may be received by the pin XD1 326. In one example embodiment, the pin XD1 326 may be denoted by “Occ” on the UI 324. Further, the temperature data may correspond to one or more temperature set points, temperature inside of each of the one or more zones, and temperature outside of each of the one or more zones over the predefined period of time and in the real time. The one or more temperature set points may be received by the pin X2 328. In one example embodiment, the pin X2 may be denoted by “TSet”. The temperature inside of each of the one or more zones may be received by the pin X3 330. In one example embodiment, the pin X3 may be denoted by “TRm”. The temperature outside of each of the one or more zones may be received by the pin X4 332. In one example embodiment, the pin X4 may be denoted by “TOat”. Further, the mode of the operation of the AHU 112 may be received by the pin XD5 334. The mode of the operation of the AHU 112 may be decided based at least on the inputs provided by the user via the UI 300.

In one example embodiment, the pin XD1 326, the pin X2 328, the pin X3 330, the pin X4 332, and the pin XD5 334 may comprise one or more indicators to indicate whether the occupancy data and the temperature data are received or not. The one or more indicators may turn in green color to indicate that the occupancy data and the temperature data are received. The one or more indicators may turn in red color to indicate that the occupancy data and the temperature data are not received. In some embodiments, the pin XD1 326, may be denoted as “XD1”, the pin X2 328 may be denoted as “X2”, the pin X3 330 may be denoted as “X3”, the pin X4 332 may be denoted as “X4”, and the pin XD5 334 may be denoted as “XD5”, for further reference in the detailed description of the FIGS. 5A-5B.

In some embodiments, the UI 324 may further comprise at least one of a “OnOff” indicator 336, “OptOn” indicator 338, “OptOff” indicator 340, and Energy Optimized Ventilation (EOV) indicator 342. The “OnOff” indicator 336, the “OptOn” indicator 338, and the “OptOff” indicator 340 may indicate the mode of the operation of the AHU 112 that is received on the pin XD5 334. In one example embodiment, the “OnOff” indicator 336 may indicate whether the AHU 112 is in operation or not. In another example embodiment, the “OptOn” indicator 338 may indicate that the AHU 112 is operating based on the determined start time and stop time. In yet another example embodiment, the “OptOff” indicator 340 may indicate that the AHU 112 is not operating based on the determined start time and stop time. In one example embodiment, when the AHU 112 is operating, the “OnOff” indicator 336 may turn in green color as shown in the FIG. 3B and when the AHU 112 is not operating, the “OnOff” indicator 336 may turn in red color.

Further, the EOV indicator 342 may indicate whether the AHU 112 is operating in the received mode of operation of the AHU 112 or not. In one example embodiment, at least one of the “OnOff” indicator 336, the “OptOn” indicator 338, the “OptOff” indicator 340, and EOV indicator 342 may comprise one or more indicators. The one or more indicators may turn in green color to indicate running status of the AHU 112 based on the received mode of operation of the AHU 112. The one or more indicators may turn in red color to indicate halted status of the AHU 112 based on the received mode of operation of the AHU 112.

It will be apparent to one skilled in the art that the UI 300 and the UI 324 of the system 100 may comprise one or more components apart from the one or more blocks, the plurality pins, or indicators, without departing from the scope of the disclosure.

FIG. 4 illustrates a flowchart showing a method 400 for determining at least one start time and stop time for the AHU 112 using the linear model in accordance with an example embodiment of the present disclosure. FIG. 4 is described in conjunction with FIGS. 1-3B.

At operation 402, the at least one processor 202 may be configured to receive the set of parameters associated with one or more zones in a real time. In some embodiments, the set of parameters may comprise one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. In some embodiments, one or more zones may comprise at least one of the building, the warehouse, the storage unit, or the office space. In some embodiments, the one or more static parameters may comprise at least one of wall thermal resistance, heat transfer coefficient, and convection and conduction heat transfer of each of the one or more zones over the predefined period of time and in the real time.

In some embodiments, the one or more internal parameters may comprise at least one of the lowest cooling time for the optimal cooling 304, the high speed cooling factor 306, the maximum outside temperature 308 for switching of the AHU 112, and the optimum stop factor 310 over the predefined period of time and in the real time. In some embodiments, the occupancy data may comprise occupancy of the one or more zones and the number of occupants within each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the temperature data may correspond to one or more temperature set points, temperature inside of each of the one or more zones, and temperature outside of each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the predefined period of time may correspond to the monitored time period comprising at least one of hours, days, months, quarters, or years in which the set of parameters is received.

At operation 404, the at least one processor 202 may be configured to determine the occupancy data available for each of the one or more zones. In some embodiments, the at least one processor 202 may be configured to determine the occupancy data available from the received first set of parameters. In one case, if the occupancy data is not available, then proceed to operation 406. At operation 406, the at least one processor 202 may be configured to determine the occupancy data for each of the one or more zones. In some embodiments, the occupancy sensor may be configured to determine the occupancy data of each of the one or more zones. The occupancy sensor may be configured to detect the presence or absence of people within each of the one or more zones.

In another case, if the occupancy data is available, then proceed to operation 408. At operation 408, the at least one processor 202 may be configured to determine at least one start time and stop time for the AHU 112 for each of the one or more zones using historical data and the linear model. In some embodiments, the linear model may use one or more mathematical techniques for determining the at least one start time and stop time.

At operation 410, the at least one processor 202 may be configured to generate one or more commands based at least on the at least one start time and the stop time for each of the one or more zones. In some embodiments, the one or more commands may correspond to the ON command or the OFF command. At operation 412, the at least one processor 202 may be configured to send the one or more commands to the BMS to operate the AHU 112, based at least on the one or more commands. In one example embodiment, the operation may correspond to turning on or turning off the AHU 112. In some embodiments, the BMS may be configured to continuously monitor the set of parameters associated with the one or more zones in the real time. Further, the BMS may be configured to continuously monitor the set of parameters using one or more sensors 106. The one or more sensors 106 may comprise at least one of the temperature sensor, the humidity sensor, or the occupancy sensor. Thereafter, the at least one processor 202 may be configured to receive the set of parameters associated with the one or more zones, from the BMS, as explained in operation 402.

FIG. 5A illustrates a graphical representation 500 of determination of the at least one start time for the AHU 112 using the linear model in accordance with an example embodiment of the present disclosure. FIG. 5A is described in conjunction with FIGS. 1-4.

In some embodiments, the graphical representation 500 may represent the determination of the at least one start time for the AHU 112 using the linear model, as illustrated by 502. The x-axis of the graphical representation 500 may represent temperature inside of each of the one or more zones. The y-axis of the graphical representation 500 may represent the at least one start time. The at least one start time denoted by t_VKE may be calculated using the formula:

t V ⁢ K ⁢ E = ( X ⁢ 3 - X ⁢ 2 ) * P ⁢ 8

In one example, the X3 is received as 78 degrees Fahrenheit ° (F), the X2 as 72° F., and the P8 as 10 minutes (m) per° F., the t_VKE may be calculated as 60 minutes. The at least one start time is calculated to be 60 minutes that indicates the AHU 112 gets actuated 60 minutes before occupancy within the one or more zones to achieve the one or more temperature set points at the time of occupancy within the one or more zones.

FIG. 5B illustrates a graphical representation 504 of determination of the at least one stop time for the AHU 112 using the linear model in accordance with an example embodiment of the present disclosure. FIG. 5B is described in conjunction with FIGS. 1-4.

In some embodiments, the graphical representation 504 may represent the determination of the at least one stop time for the AHU 112 using the linear model, as illustrated by 506. The x-axis of the graphical representation 504 may represent temperature outside of each of the one or more zones. The y-axis of the graphical representation 504 may represent the at least one stop time. In some embodiments, base time of 120 m added with a correction factor denoted by t_corr may be considered for determining the at least one stop time. The t_corr may be calculated using the formula—

t c ⁢ o ⁢ r ⁢ r = ( X ⁢ 2 - X ⁢ 3 ) * P ⁢ 1 ⁢ 0

Further, the at least one at least one stop time denoted by t_VKA may be calculated, based on the t_corr, using the formula—

t V ⁢ K ⁢ A = ( 120 ⁢ min + t c ⁢ o ⁢ r ⁢ r ) ⁢ X ⁢ 4 - P ⁢ 9 X ⁢ 2 - P ⁢ 9

For example, the X2 is received as 70° F., X3 as 80° F., and P10 as 10 min per° F., the t_corr may be calculated as minus (−) 100 min. Further, X4 may be received as 80° F., and P9 as 90° F., the t_VKA may be calculated as 10 minutes. The at least one stop time is calculated to be 10 minutes that indicates the AHU 112 gets deactivated 10 minutes before the end of the occupancy within the one or more zones to maintain the one or more temperature set points till the end of the occupancy within the one or more zones.

FIG. 6 illustrates a flowchart showing a method 600 for determining at least one start time and stop time for the AHU 112 using the non-linear model in accordance with an example embodiment of the present disclosure. FIG. 6 is described in conjunction with FIGS. 1-5B.

At operation 602, the at least one processor 202 may be configured to receive the set of parameters associated with one or more zones in the real time. In some embodiments, the set of parameters may comprise one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. In some embodiments, one or more zones may comprise at least one of the building, the warehouse, the storage unit, or the office space. In some embodiments, the one or more static parameters may comprise at least one of wall thermal resistance, heat transfer coefficient, and convection and conduction heat transfer of each of the one or more zones over the predefined period of time and in the real time.

In some embodiments, the one or more internal parameters may comprise at least one of the lowest cooling time for the optimal cooling 304, the high speed cooling factor 306, the maximum outside temperature 308 for switching of the AHU 112, and the optimum stop factor 310 over the predefined period of time and in the real time. In some embodiments, the occupancy data may comprise occupancy of the one or more zones and the number of occupants within each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the temperature data may correspond to one or more temperature set points, temperature inside of each of the one or more zones, and temperature outside of each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the predefined period of time may correspond to the monitored time period comprising at least one of hours, days, months, quarters, or years in which the set of parameters is received.

At operation 604, the at least one processor 202 may be configured to determine the occupancy data available for each of the one or more zones. In some embodiments, the at least one processor 202 may be configured to determine the occupancy data available from the received first set of parameters. In one case, if the occupancy data is not available, then proceed to operation 606. At operation 606, the at least one processor 202 may be configured to determine the occupancy data for each of the one or more zones. In some embodiments, the occupancy sensor may be configured to determine the occupancy data of each of the one or more zones. The occupancy sensor may be configured to detect the presence or absence of people within each of the one or more zones.

In another case, if the occupancy data is available, then proceed to operation 608. At operation 608, the at least one processor 202 may be configured to determine at least one start time and stop time for the AHU 112 for each of the one or more zones using historical data and the non-linear model. In some embodiments, the non-linear model may use one or more mathematical techniques for determining the at least one start time and stop time.

At operation 610, the at least one processor 202 may be configured to generate one or more commands based at least on the at least one start time and the stop time for each of the one or more zones. In some embodiments, the one or more commands may correspond to the ON command or the OFF command. At operation 612, the at least one processor 202 may be configured to send the one or more commands to the BMS to operate the AHU 112, based at least on the one or more commands. In one example embodiment, the operation may correspond to turning on or turning off the AHU 112. In some embodiments, the BMS may be configured to continuously monitor the set of parameters associated with the one or more zones in the real time. Further, the BMS may be configured to continuously monitor the set of parameters using the one or more sensors 106. The one or more sensors 106 may comprise at least one of the temperature sensor, the humidity sensor, or the occupancy sensor. Thereafter, the at least one processor 202 may be configured to receive the set of parameters associated with the one or more zones, from the BMS, as explained in operation 602.

FIG. 7 illustrates a flowchart showing a method 700 for determining at least one start time and stop time for the AHU 112 using the AI based model in accordance with an example embodiment of the present disclosure. FIG. 7 is described in conjunction with FIGS. 1-6.

At operation 702, the at least one processor 202 may be configured to receive the set of parameters associated with one or more zones in a real time. In some embodiments, the set of parameters may comprise one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. In some embodiments, the one or more zones may comprise at least one of the building, the warehouse, the storage unit, or the office space. In some embodiments, one or more static parameters may comprise at least one of wall thermal resistance, heat transfer coefficient, and convection and conduction heat transfer of each of the one or more zones over the predefined period of time and in the real time.

In some embodiments, the one or more internal parameters may comprise at least one of the lowest cooling time for optimal cooling 304, the high speed cooling factor 306, the maximum outside temperature 308 for switching of the AHU 112, and the optimum stop factor 310 over the predefined period of time and in the real time. In some embodiments, the occupancy data may comprise occupancy of the one or more zones and the number of occupants within each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the temperature data may correspond to one or more temperature set points, temperature inside of each of the one or more zones, and temperature outside of each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the predefined period of time may correspond to the monitored time period comprising at least one of hours, days, months, quarters, or years in which the set of parameters is received.

At operation 704, the at least one processor 202 may be configured to determine the occupancy data available for each of the one or more zones. In some embodiments, the at least one processor 202 may be configured to determine the occupancy data available from the received first set of parameters. In one case, if the occupancy data is not available, then proceed to operation 706. At operation 706, the at least one processor 202 may be configured to determine the occupancy data for each of the one or more zones. In some embodiments, the occupancy sensor may be configured to determine the occupancy data of each of the one or more zones. The occupancy sensor may be configured to detect the presence or absence of people within each of the one or more zones.

In another case, if the occupancy data is available, then proceed to operation 708. At operation 708, the at least one processor 202 may be configured to determine at least one start time and stop time for the AHU 112 for each of the one or more zones using historical data and the AI based model. In some embodiments, the AI based model may use the AI technique for determining the at least one start time and stop time.

At operation 710, the at least one processor 202 may be configured to generate one or more commands based at least on the at least one start time and the stop time for each of the one or more zones. In some embodiments, the one or more commands may correspond to the ON command or the OFF command. At operation 712, the at least one processor 202 may be configured to send the one or more commands to the BMS to operate the AHU 112, based at least on the one or more commands. In one example embodiment, the operation may correspond to turning on or turning off the AHU 112. In some embodiments, the BMS may be configured to continuously monitor the set of parameters associated with the one or more zones in the real time. Further, the BMS may be configured to continuously monitor the set of parameters using the one or more sensors 106. The one or more sensors 106 may comprise at least one of the temperature sensor, the humidity sensor, or the occupancy sensor. Thereafter, the at least one processor 202 may be configured to receive the set of parameters associated with the one or more zones, from the BMS, as explained in operation 702.

FIG. 8 illustrates a block diagram showing determination of the weighted average value, based at least on the predefined weight allocated to each of the at least one start time and stop time determined using the linear model, the non-linear model, and the AI based model in accordance with an example embodiment of the present disclosure. FIG. 8 is described in conjunction with FIGS. 1-7.

As illustrated in FIGS. 1-2, the at least one processor 202 may be configured to determine at least one start time and stop time for the AHU 112 for each of the one or more zones using historical data and each model of the plurality of models. The plurality of models may comprise at least one of the linear model, the non-linear model, and the AI based model. Further, the at least one processor 202 may be configured to allocate predefined weights to each of the at least one start time and stop time determined for each model, as illustrated by 802. In one example embodiment, the allocation of the predefined weight to each of the at least one start time and stop time determined for each model may correspond to multiplication of the predefined weight to each of the at least one start time and stop time determined for each model. In one example embodiment, the predefined weights may be denoted by w₁, w₂, w₃. The predefined weight w₁ may be allocated to the at least one start time and stop time determined for the linear model. In some embodiments, the linear model is referred to as “Model 1”. The predefined weight w₂ may be allocated to the at least one start time and stop time determined for the non-linear model. In some embodiments, the non-linear model is referred to as “Model 2”. The predefined weight w₃ may be allocated to the at least one start time and stop time determined for the AI based model. In some embodiments, the AI based model is referred to as “Model 3”.

Further, the at least one processor 202 may be configured to determine the weighted average value for the determined at least one start time and stop time for the AHU 112 that is based at least on the allocated predefined weights w₁, w₂, w₃, as illustrated by 804. The weighted average value may be determined by adding all the predefined weights allocated to each of the at least start time and stop time for each model. Thereafter, the at least one processor 202 may be configured to determine the OSS time of the AHU 112 for each of the one or more zones using the plurality of models, based at least on the determined weighted average value.

In some embodiments, the value of the determined OSS time may vary due to temperature inside each of the one or more zones, as illustrated by 806. In some embodiments, the temperature inside each of the one or more zones is referred to as “zone air temperature”. The temperature inside each of the one or more zones may generate an error x_sp in the determined OSS time, thereby, the predefined weights may be fine-tuned, as illustrated by 808, by minimizing the error x_sp in the actual temperature inside of each of the one or more zones and one or more temperature set points by using the AI technique. The error x_sp may be minimized by subtracting the error x_sp from the zone air temperature. As a result, the OSS time may be estimated more accurately allowing to maintain the desired temperature set points before the occupancy. While assigning the predefined weight to, in particular, the AI based model, softmax scaling is applied using the formula—

W i = e i β Err ∑ j = 1 N ⁢ e j β Err

• • where β is softmax scaling coefficient. The predefined weight allocated to each of the at least one start time and stop time that is determined for each model may be varied as the plurality of models are fed with more historical data. For example, initially the predefined weight is more to the linear model and a non-linear model in comparison to the predefined weight to the AI based model. Further, after a specific time period, the predefined weight is more to the AI based model in comparison to the linear model and the non-linear model. The variation of the predefined weight may be done since with more historical data the accuracy of the AI based model increases. Further, the at least one processor 202 may be configured to determine the OSS time of the AHU 112 for each of the one or more zones using the plurality of models. The at least one processor 202 may be configured to determine the OSS time based at least on the determined weighted average value.

It will be apparent to one skilled in the art that the system 100 may comprise other models from the plurality of models with the same functionality, without departing from the scope of the disclosure.

FIG. 9 illustrates switching between a free cooling mode 902 and a mechanical mode 904 of the AHU 112 in accordance with an example embodiment of the present disclosure. FIG. 9 is described in conjunction with FIGS. 1-8.

In some embodiments, the at least one processor 202 may be configured to switch between the free cooling mode 902 and the mechanical mode 904 for the AHU 112 based at least on the temperature data of each of the one or more zones. Further, the at least one processor 202 may be configured to switch between the free cooling mode 902 and the mechanical mode 904 from an idle mode 906, autonomously. In some embodiments, the free cooling mode 902 may be activated when the temperature outside of each of the one or more zones is less than the temperature inside of each of the one or more zones. In the free cooling mode 902, coils of the AHU 112 may not function and help in reducing net energy consumption.

Further, the mechanical mode 904 may be activated when the temperature outside of each of the one or more zones is greater than the temperature inside of each of the one or more zones. The at least one processor 202 may be configured to conserve energy consumed by the AHU 112 by deactivating or reducing cooling capacity of the AHU 112 in the free cooling mode 902. The at least one processor 202 may be configured to operate the AHU 112 based at least one the determined OSS for each of the one or more zones in the mechanical mode 904. Further, the system may switch to an occupied mode 908, from the mechanical mode 904 to continue the operation of the AHU 112.

It will be apparent to one skilled in the art that the system may be developed on cloud and made to interact with the BMS by accessing and overriding the occupancy object ID/state variable available in the at least one processor 202, without departing from the scope of the disclosure.

FIG. 10 illustrates a flowchart showing a method 1000 for determining optimal start and stop time (OSS) of the AHU 112 in accordance with an example embodiment of the present disclosure. FIG. 10 is described in conjunction with FIG. 1-9.

At operation 1002, the at least one processor 202 may be configured to receive the set of parameters associated with one or more zones in the real time. In some embodiments, the set of parameters may comprise one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. In some embodiments, one or more zones may comprise at least one of the building, the warehouse, the storage unit, or the office space. In some embodiments, the one or more static parameters may comprise at least one of wall thermal resistance, heat transfer coefficient, and convection and conduction heat transfer of each of the one or more zones over the predefined period of time and in the real time.

In some embodiments, the one or more internal parameters may comprise at least one of the lowest cooling time for optimal cooling 304, the high speed cooling factor 306, the maximum outside temperature 308 for switching of the AHU 112, and the optimum stop factor 310 over the predefined period of time and in the real time. In some embodiments, the occupancy data may comprise occupancy of the one or more zones and the number of occupants within each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the temperature data may correspond to one or more temperature set points, temperature inside of each of the one or more zones, and temperature outside of each of the one or more zones over the predefined period of time and in the real time. In some embodiments, the predefined period of time may correspond to the monitored time period comprising at least one of hours, days, months, quarters, or years in which the set of parameters is received.

For example, the at least one processor 202 receives the set of parameters including one or more static parameters such as wall thermal resistance, heat transfer coefficient and internal parameters such as lowest cooling time for optimal cooling 304 of 0 min, the high speed cooling factor 306 of 10 min per° F., the maximum outside temperature 308 for switching of the AHU 112 as 75° F., and the optimum stop factor 310 of 10 min per° F., around 30 occupants in a warehouse facility.

At operation 1004, the at least one processor 202 may be configured to determine the occupancy status of each of the one or more zones in the real time, based at least on the received set of parameters. For example, based on the received set of parameters including one or more static parameters such as wall thermal resistance, heat transfer coefficient and internal parameters such as lowest cooling time for optimal cooling 304 of 0 min, the high speed cooling factor 306 of 10 min per° F., the maximum outside temperature 308 for switching of the AHU 112 as 75° F., and the optimum stop factor 310 of 10 min per° F., around 30 occupants in the warehouse facility, the at least one processor 202 determines that the warehouse facility is occupied.

At operation 1006, the at least one processor 202 may be configured to determine at least one start time and stop time for the AHU 112 for each of the one or more zones using historical data and each model of the plurality of models. In some embodiments, the plurality of models may comprise at least one of the linear model, the non-linear model, and the AI based model. In one exemplary embodiment, the at least one processor 202 may be configured to determine at least one start time and stop time for the AHU 112 for the linear model, as explained in the method 400. Further, the at least one processor 202 may be configured to determine at least one start time and stop time for the AHU 112 for the non-linear model, as explained in the method 600. Further, the at least one processor 202 may be configured to determine at least one start time and stop time for the AHU 112 for the AI based model, as explained in the method 700. In some embodiments, the linear model and the non-linear model may use one or more mathematical techniques for determining the at least one start time and stop time. Further, the AI based model may use the AI technique for determining the at least one start time and stop time. For example, the start time and stop time for a period of 9 am to 6 pm for the warehouse determined by the linear model is 08:30 am and 5:30 pm respectively. Further, start time and stop time for a period of 9 am to 6 pm for the warehouse determined by the non-linear model is 08:35 am and 5:35 pm respectively. Further, the start time and stop time for a period of 9 am to 6 pm for the warehouse determined by the AI based model is 08:40 am and 5:40 pm respectively. The determined start time and stop times are crucial, ensuring that the AHU 112 activate prior to occupancy to achieve desired temperature set points and deactivate promptly after occupancy to maintain the temperature set points efficiently within the warehouse facility.

At operation 1008, the at least one processor 202 may be configured to determine the weighted average value for the determined at least one start time and stop time for the AHU 112, based at least on the predefined weight allocated to each of the at least one start time and stop time determined for each model. In some embodiments, the predefined weight may correspond to the value assigned to the at least one start time and stop time determined using each model. Further, the predefined weight may be fine-tuned using the AI technique. For example, the at least one processor 202 provides a predefined weight as 3 for the start time and stop time determined by the linear model, further 2 for the start time and stop time determined by the non-linear model and 0.5 for the start time and stop time determined by the AI based model.

At operation 1010, the at least one processor 202 may be configured to determine the OSS time of the AHU 112 for each of the one or more zones using the plurality of models, based at least on the determined weighted average value. In some embodiments, the optimal start time may correspond to the time at which the AHU 112 gets actuated before occupancy within the one or more zones to achieve the one or more temperature set points at the time of occupancy within the one or more zones. Further, the optimal stop time may correspond to the time at which the AHU 112 gets deactivated before the end of the occupancy within the one or more zones to maintain the one or more temperature set points till the end of the occupancy within the one or more zones. For example, the at least one processor 202 determines the weighted average value to be start time and stop time as 08:35 am and 5:35 pm respectively. Through techniques like softmax scaling, the at least one processor 202 fine-tunes the determined weights, enhancing the accuracy of the predictions and determining the OSS time for the warehouse facility.

Further, the method 1000 may comprise switching, via the at least one processor 202, between the free cooling mode 902 and the mechanical mode 904 for the AHU 112 based at least on the temperature data of each of the one or more zones. In some embodiments, the free cooling mode 902 may be activated when the temperature outside of each of the one or more zones is less than the temperature inside of each of the one or more zones. Further, the mechanical mode 904 may be activated when the temperature outside of each of the one or more zones is greater than the temperature inside of each of the one or more zones. The at least one processor 202 may be configured to conserve energy consumed by the AHU 112 by deactivating or reducing cooling capacity of the AHU 112 in the free cooling mode 902. The at least one processor 202 may be configured to operate the AHU 112 based at least on the determined OSS for each of the one or more zones in the mechanical mode 904. For example, with the OSS time established, the at least one processor 202 switches between the free cooling mode 902 and the mechanical mode 904 based on real time temperature data. When outdoor temperatures dip below indoor temperatures, the AHUs 112 switch to the free cooling mode 902, leveraging natural cooling to conserve energy. Conversely, when outdoor temperatures rise above indoor temperatures, the AHU 112 operates in the mechanical mode 904, maintaining comfort levels efficiently.

In some embodiments, the set of parameters may be received from the BMS comprising the one or more sensors 106. The one or more sensors 106 may comprise at least one of the temperature sensor, the humidity sensor, or the occupancy sensor. Further, the method 1000 may comprise sending, via the at least one processor 202, one or more commands to the AHU 112 for each of the one or more zones for reaching the one or more temperature set points, based at least on the determined OSS time. In some embodiments, the one or more commands may correspond to the ON command or the OFF command. For example, the at least one processor 202 issues one or more commands to the AHU 112, directing the AHU 112 to achieve and maintain the desired temperature set points within each zone. The one or more commands, including ON and OFF commands, are synchronized with the determined OSS time, optimizing energy consumption while ensuring comfort for all the occupants within the warehouse facility. In this manner, by intelligently analyzing real time data and leveraging advanced modeling techniques, the warehouse facility may maximize energy efficiency, reduces operational costs, and enhances occupant comfort, thereby exemplifying the practical implementation of the proposed method in real-world scenarios.

In some embodiments, the system 100 may comprise at least non-transitory machine-readable information storage medium comprising one or more instructions which when executed by the at least one processor 202 to perform operations comprising receiving the set of parameters associated with one or more zones in the real time. The set of parameters may comprise one or more static parameters associated with each of the one or more zones, one or more internal parameters of each of the one or more zones, occupancy data of each of the one or more zones, and temperature data of each of the one or more zones. Further, the operations may comprise determining the occupancy status of each of the one or more zones in the real time, based at least on the received set of parameters. Further, the operations may comprise determining at least one start time and stop time for the AHU 112 for each of the one or more zones using historical data and each model of the plurality of models. The plurality of models may comprise at least one of the linear model, the non-linear model, and the AI based model. The AI based model may use the AI technique for determining the at least one start time and stop time.

Further, the operations may comprise determining the weighted average value for the determined at least one start time and stop time for the AHU 112, based at least on the predefined weight allocated to each of the at least one start time and stop time determined for each model. The predefined weight may correspond to the value assigned to the at least one start time and stop time determined using each model and is fine-tuned using the AI technique. Thereafter, the operations may comprise determining the OSS time of the AHU 112 for each of the one or more zones using the plurality of models, based at least on the determined weighted average value.

The present disclosure may provide precise control over AHU operation, leading to enhanced energy efficiency and reduced operational costs by leveraging the set of parameters such as occupancy data, temperature data, and internal one or more parameters for each zone in real time. Through the utilization of plurality of models, including linear, non-linear, and AI based model, the system may exhibit adaptability to diverse environmental conditions, allowing for dynamic adjustments in AHU start and stop times to maintain optimal comfort levels. Moreover, the incorporation of historical data in the system may enable predictive analysis, facilitating proactive maintenance to prevent equipment failures and minimize downtime. Additionally, the determination of weighted average values derived from the historical data and the plurality of models may ensure robust decision-making, while the optimization of start and stop times for each zone individually contributes to improved indoor air quality and occupant comfort. Overall, the system and the method may optimize energy usage as well as enhance operational reliability and occupant satisfaction and comfort level.

Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Moreover, although the foregoing descriptions and the associated drawings describe example embodiments in the context of certain example combinations of elements and/or functions, it should be appreciated that different combinations of elements and/or functions may be provided by alternative embodiments without departing from the scope of the appended claims. In this regard, for example, different combinations of elements and/or functions than those explicitly described above are also contemplated as may be set forth in some of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.