Methods and Systems for Zone Level Occupancy Prediction and Energy Optimization

The present invention relates to building management systems (BMS), and more particularly relates to a method and system for predicting zone level occupancy for energy optimization.

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.

Efficiency of individual HVAC components, such as furnaces, boilers, air conditioners, heat pumps, and ventilation fans, plays a significant role in overall system efficiency. However, to maintain a comfortable temperature inside the enclosed space, it becomes essential to keep the HVAC system activated by a building's manager such that when the enclosed space is occupied, the enclosed space is already has a comfortable temperature for the occupants to live. However, by keeping the HVAC system activated, the energy consumption of the building increases drastically. Managing a building's HVAC system means finding a balance between keeping people comfortable and not spending too much money on heating or cooling. However, in big buildings, it is difficult to estimate when each zone of the building will be occupied. Therefore, HVAC managers usually pick a time in the morning to start heating or cooling and then keep it going all day, even when some zones are not being utilized. The big buildings use a lot of energy for heating and cooling, that costs a lot of money, due to which HVAC managers want to wait as long as possible before starting heating or cooling the zone to avoid wasting energy on empty zones. Adding to the limitation, HVAC managers can only choose one time for the whole building to start heating or cooling, even though not every zone becomes occupied at the same time as a result, some zones end up using energy to stay comfortable even when nobody is there.

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.

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, an occupancy data of one or more zones via a plurality of sensors for a first period of time. The occupancy data comprises at least a number of occupants within each zone for the first period of time. The method comprises determining, via the at least one processor, one or more occupancy trends for each zone for the first period of time using a trained machine learning (ML) model. The occupancy trends comprises occupancy of the one more zones and a number of occupants within each of the one or more zones within the first time period. The method further comprises mapping, via the at least one processor, the determined one or more occupancy trends for each of the one or more zones for the first period of time with fluctuations in occupancy of each of the one or more zones in real-time and booking status of each of the one or more zones. Further, the method comprises predicting, via the at least one processor, occupancy of each of the one or more zones for a second period of time and a threshold time required for each of the one or more zones to heat or cool at one or more temperature set points using the trained ML model based at least on the mapping. Thereafter, the method comprises adjusting, via the at least one processor, the one or more temperature set points for each of the one or more zones at the threshold time based at least on the prediction.

In some embodiments, the ML model for each of the one or more zones is trained based at least on the received occupancy data. In some embodiments, the at least one processor is configured to train the ML model using one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques.

In some embodiments, the booking status of the one or more zones corresponds to the one or more zones pre-booked to be occupied by one or more users.

In some embodiments, the plurality of sensors corresponds to a plurality of zone level occupancy sensors comprising at least one lightning sensors, Wi-Fi Access Points and Bluetooth low energy (BLE) sensors, access readers, or carbon dioxide (CO) 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, opening within each zone corresponds to a number of windows and doors present in each zone.

In some embodiments, the first period of time corresponds to historical time zone and the second period of time corresponds to future time period. Further, the time period comprises at least day, time, season, months, years.

In some embodiments, the one or more temperature set points comprises at least one heating set point that initializes a heating cycle to increase temperature of the one or more zones, and at least one cooling set point that initializes a cooling cycle to decrease temperature of the one or more zones. Further, the heating cycle or the cooling cycle is initialized based at least on the threshold time required for each of the one or more zones to heat or cool at the one or more temperature set points.

In some embodiments, the heating cycle and the cooling cycle are identified based at least on the change in the one or more temperature set points. In some embodiments, the heating cycle and the cooling cycle end when the temperature of the one or more zones reaches the one or more temperature set points.

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 an occupancy data of one or more zones via a plurality of sensors for a first period of time. The occupancy data comprises at least a number of occupants within each zone for the first period of time. The at least one processor is configured to determine one or more occupancy trends for each zone for the first period of time using a trained machine learning (ML) model. The occupancy trends comprises occupancy of the one more zones and a number of occupants within each of the one or more zones within the first time period. The at least one processor is further configured to map the determined one or more occupancy trends for each of the one or more zones for the first period of time with fluctuations in occupancy of each of the one or more zones in real-time and booking status of each of the one or more zones. Further, the at least one processor is configured to predict occupancy of each of the one or more zones for a second period of time and a threshold time required for each of the one or more zones to heat or cool at one or more temperature set points using the trained ML model based at least on the mapping. Thereafter, the at least one processor is configured to adjust the one or more temperature set points for each of the one or more zones at the threshold time based at least on the prediction.

In some embodiments, the at least one processor is further configured to train the ML model for each of the one or more zones based at least one the received occupancy data using one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. In some embodiments, the booking status of the one or more zones corresponds to the one or more zones pre-booked to be occupied by one or more users. In some embodiments, the plurality of sensors corresponds to a plurality of zone level occupancy sensors comprising at least one lightning sensors, Wi-Fi Access Points and Bluetooth low energy (BLE) sensors, access readers, or carbon dioxide (CO) 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, opening within each zone corresponds to a number of windows and doors present in each zone.

In some embodiments, the first period of time corresponds to historical time zone and the second period of time corresponds to future time period. Further, the time period comprises at least day, time, season, months, years. In some embodiments, the one or more temperature set points comprises at least one heating set point that initializes a heating cycle to increase temperature of the one or more zones, and at least one cooling set point that initializes a cooling cycle to decrease temperature of the one or more zones. Further, the heating cycle or the cooling cycle is initialized based at least on the threshold time required for each of the one or more zones to heat or cool at the one or more temperature set points.

In some embodiments, the heating cycle and the cooling cycle are identified based at least on the change in the one or more temperature set points. In some embodiments, the heating cycle and the cooling cycle end when the temperature of the one or more zones reaches the one or more temperature set points.

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 an occupancy data of one or more zones via a plurality of sensors for a first period of time, wherein the occupancy data comprises at least a number of occupants within each zone for the first period of time; determining one or more occupancy trends for each zone for the first period of time using a trained machine learning (ML) model, wherein the occupancy trends comprises occupancy of the one more zones and a number of occupants within each of the one or more zones within the first time period; mapping the determined one or more occupancy trends for each of the one or more zones for the first period of time with fluctuations in occupancy of each of the one or more zones in real-time and booking status of each of the one or more zones; predicting occupancy of each of the one or more zones for a second period of time and a threshold time required for each of the one or more zones to heat or cool at one or more temperature set points using the trained ML model based at least on the mapping; and adjusting the one or more temperature set points for each of the one or more zones at the threshold time based at least on the prediction.

In some embodiments, the booking status of the one or more zones corresponds to the one or more zones pre-booked to be occupied by one or more users. In some embodiments, the first period of time corresponds to historical time zone and the second period of time corresponds to future time period, wherein the time period comprises at least day, time, season, months, or years.

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.

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 to predict occupancy. Embodiments may be configured to be executed by at least one processor for predicting the occupancy of the one or more zones. Embodiments may be configured to predict the occupancy of the one or more zones, the one or more zones comprises at least one of a building, a warehouse, a storage unit, or an office space, wherein opening within each zone corresponds to number of windows and doors present in each zone. Embodiments may be configured to receive an occupancy data of one or more zones via a plurality of sensors for a first period of time. Embodiments may be configured to receive the occupancy data that comprises at least a number of occupants within each zone for the first period of time.

Embodiments may be configured to receive the occupancy data of the one or more zones via the plurality of sensors that corresponds to a plurality of zone level occupancy sensors comprising at least one lightning sensors, Wi-Fi Access Points and Bluetooth low energy (BLE) sensors, access readers, and/or COsensor. Embodiments may be configured to generate at least one machine learning (ML) model for each of the one or more zones for the first period of time by using the at least one processor. Embodiments may be configured to train via the at least one processor, the ML model for each of the one or more zones based at least on the received occupancy data. Embodiments may be configured to determine one or more occupancy trends for each zone for the first period of time using a trained machine learning (ML) model.

Embodiments may be configured to determine the one or more occupancy trends that comprises occupancy of the one more zones and number of occupants within each of the one or more zones within the first time period. Embodiments may be configured to map the determined occupancy trends for each of the one or more zones for the first period of time with fluctuations in occupancy of each of the one or more zones in real-time and booking status of each of the one or more zones. Embodiments may be configured to map the map the booking status of the one or more zones that corresponds to the one or more zones pre-booked to be occupied by one or more users. Embodiments may be configured to predict occupancy of each of the one or more zones for a second time period and a threshold time required for each of the one or more zones to heat or cool at one or more temperature set points using the trained ML model based at least on the mapping. Embodiments may be configured to adjust one or more temperature set points for each of the one or more zones at the threshold time based at least on the prediction.

illustrates a network diagram of a system, in accordance with an example embodiment of the present disclosure. The systemmay comprise a networkcommunicatively coupled to a heating, ventilation, and air conditioning (HVAC) unit (not shown) installed within a building, a plurality of sensors, a server, and a user device.

In some embodiments, the networkmay 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 networkmay refer to as a distributed infrastructure that is configured to exchange of data, information, and resources among interconnected computing devices and systems. The networkmay 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 systemmay connect to the networkin 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.

Further, the HVAC unit may be installed within the buildingfor regulating and maintaining internal temperatures. In some embodiments, the HVAC unit may be configured to provide heating by various means, such as furnaces, boilers, heat pumps, and electric heaters. The HVAC unit may be configured to generate heat to warm the indoor environment during colder weather conditions. Further, the HVAC unit may be configured to facilitate to exchange indoor air with outdoor air to improve air quality and remove contaminants such as odors, moisture, and pollutants. In some embodiments, the HVAC unit may include one or more fans (not shown), a ductwork (not shown), and one or more filters (not shown) to distribute fresh air and remove stale air from the building. In some embodiments, the HVAC may also be configured to cool and dehumidify indoor air to maintain comfortable temperatures during hot weather. The HVAC unit may be configured to use air conditioners, chillers, evaporative coolers, and heat pumps to remove heat from indoor air and circulate cool air throughout the space.

In some embodiments, the plurality of sensorsmay be communicatively coupled to the HVAC unit via the network. The plurality of sensorsmay be configured to determine occupancy data of one or more zones within the buildingand may also be configured to determine one or more temperature set points for each of the one or more zones within the building. In some embodiments, the plurality of sensors may further be configured to be communicatively coupled to the serverfor communicating the determined occupancy data of one or more zones within the buildingone or more temperature set points for each of the one or more zones to the serverfor further computing. In some embodiments, the plurality of sensorsmay include plurality of zone level occupancy sensors comprising at least one lightning sensors, Wi-Fi Access Points and Bluetooth low energy (BLE) sensors, access readers, and/or COsensor. The zone level occupancy sensors comprising the at least one lightning sensors, the Wi-Fi Access Points and Bluetooth low energy (BLE) sensors, the access readers, and/or the COsensor may be configured to collectively detect the presence of occupants and measure environmental parameters within the one or more zones of the building.

In some embodiments, the lightning sensors may include motion sensors, occupancy sensors, infrared sensors, ultrasonic sensors, and video camera sensors. The motion sensors are configured to detect movement within a threshold detection range. Further, the occupancy sensors are configured to detect the presence or absence of people within a defined area of the one or more zones. Further, the infrared sensors may be configured to detect the presence of human by detecting changes in infrared radiation, such as body heat emitted by humans. Further, the ultrasonic sensors may be configured to emit ultrasonic waves and detect changes in the reflected waves caused by moving objects, including people within the one or more zones. Further, the video camera sensors may be configured to be equipped with image processing algorithms that may be configured to detect the presence of people by analyzing video footage and detecting human shapes or movements.

In some embodiments, the servermay be a computer or software module that is configured to provide centralized resources, data, or services to the user deviceoperated by a user. The servermay be configured to handle and manage one or more computational tasks and data processing within the system. In some embodiments, the servermay 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 servermay 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 servermay be configured to receive the occupancy data of one or more zones via the plurality of sensorsfor a first period of time. In some embodiments, the first period of time may correspond to historical time zone. The time period may comprise at least day, time, season, months, years. Further, the servermay be configured to determine one or more occupancy trends for each zone for the first period of time using a trained machine learning (ML) model. The servermay be configured to generate at least one machine learning (ML) model for each of the one or more zones for the first period of time. Further, the servermay be configured to train the ML model for each of the one or more zones based at least one the received occupancy data.

In some embodiments, the servermay be configured to generate and train the at least one model using one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. For instance, the servermay employ supervised learning algorithms such as linear regression or decision trees to predict occupancy levels based on historical occupancy data collected from the plurality of sensorswithin each zone. Additionally, unsupervised learning techniques like clustering may be utilized to identify patterns and anomalies in occupancy behavior. Through iterative training and refinement processes, the servermay enhance the accuracy and effectiveness of the ML models, to enable the system to make more informed decisions regarding resource allocation and building management strategies tailored to each zone's specific occupancy patterns.

In some embodiments, the servermay be configured to map the determined occupancy trends for each of the one or more zones for the first period of time with fluctuations in occupancy of each of the one or more zones in real-time and booking status of each of the one or more zones. In some embodiments, the booking status of the one or more zones corresponds to the one or more zones pre-booked to be occupied by one or more users. Further, the servermay be configured to predict occupancy of each of the one or more zones for a second time period. In some embodiments, the second period of time may correspond to future time period. Further, the servermay be configured to predict a threshold time required for each of the one or more zones to heat or cool at one or more temperature set points using the trained ML model based at least on the mapping. The servermay be configured to adjust one or more temperature set points for each of the one or more zones at the threshold time based at least on the prediction.

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 servermay further be configured to send determined occupancy data and determined temperature set points to the user device. The user devicemay be equipped by an operator, manager of the building or other service professionals responsible for monitoring and operating the HVAC unit. In some embodiments, the occupancy data and the determined temperature set points may provide data regarding the expected occupancy in each of the one or more zones and based on which the temperature set points can be validated to improve efficiency of the HVAC unit installed within the building. In some embodiments, the user devicemay 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 systemhave been provided only for illustration purposes, without departing from the scope of the disclosure.

illustrates a block diagram of the server, in accordance with an example embodiment of the present disclosure. The servermay comprise at least one processor, a memory, an input/output circuitry, and a communication circuitry.

In some embodiments, the plurality of sensorsmay be configured to detect occupancy data of one or more zones. The plurality of sensorsmay correspond to a plurality of zone level occupancy sensors. Further, 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 COsensor. 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, opening within each zone of the one or more zones may correspond to number of windows and doors present in each zone.

As discussed earlier, the lightning sensors may include motion sensors, occupancy sensors, infrared sensors, ultrasonic sensors, and video camera sensors. The motion sensors may be configured to detect movement within a threshold detection range. Further, the occupancy sensors are configured to detect the presence or absence of people within a defined area of the one or more zones. Further, the infrared sensors may be configured to detect the presence of human by detecting changes in infrared radiation, such as body heat emitted by humans. Further, the ultrasonic sensors may be configured to emit ultrasonic waves and detect changes in the reflected waves caused by moving objects, including people within the one or more zones. Further, the video camera sensors may be configured to be equipped with image processing algorithms that may be configured to detect the presence of people by analyzing video footage and detecting human shapes or movements.

In some embodiments, the at least one processormay be operationally coupled to the plurality of sensors. The at least one processormay be configured to receive the occupancy data of one or more zones via the plurality of sensorsfor a first period of time. In some embodiments, the first period of time may correspond to historical time zone. The time period may comprise at least day, time, season, months, years. The one or more processorsmay be configured to determine one or more occupancy trends for each zone for the first period of time using a trained machine learning (ML) model. The at least one processormay be configured to generate at least one machine learning (ML) model for each of the one or more zones for the first period of time. Further, the at least one processormay be configured to train the ML model for each of the one or more zones based at least one the received occupancy data.

In some embodiments, the at least one processormay be configured to generate and train the at least one model using one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. For instance, the at least one processormay employ supervised learning algorithms such as linear regression or decision trees to predict occupancy levels based on historical occupancy data collected from the plurality of sensorswithin each zone. Additionally, unsupervised learning techniques like clustering may be utilized to identify patterns and anomalies in occupancy behavior. Through iterative training and refinement processes, the at least one processormay enhance the accuracy and effectiveness of the ML models, to enable the serverto make more informed decisions regarding resource allocation and building management strategies tailored to each zone's specific occupancy patterns.

The at least one processormay be configured to map the determined occupancy trends for each of the one or more zones for the first period of time with fluctuations in occupancy of each of the one or more zones in real-time and booking status of each of the one or more zones. In some embodiments, the booking status of the one or more zones corresponds to the one or more zones pre-booked to be occupied by one or more users. The at least one processormay be configured to predict occupancy of each of the one or more zones for a second time period. In some embodiments, the second period of time may correspond to future time period. Further, the at least one processormay be configured to predict a threshold time required for each of the one or more zones to heat or cool at one or more temperature set points using the trained ML model based at least on the mapping. The at least one processormay be configured to adjust one or more temperature set points for each of the one or more zones at the threshold time based at least on the prediction.

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.

The at least one processormay include suitable logic, circuitry, and/or interfaces that are operable to execute one or more instructions stored in the memoryto perform predetermined operations. In one embodiment, the at least one processormay be configured to decode and execute any instructions received from one or more other electronic devices or server(s). The at least one processormay 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 processor may be implemented using one or more processor technologies known in the art. Examples of the at least one processorinclude, 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 memorymay be configured to store a set of instructions and data executed by the at least one processor. Further, the memorymay include the one or more instructions that are executable by the at least one processorto perform specific operations. The memorymay be configured to include the instructions to receive an occupancy data of one or more zones via the plurality of sensorsfor a first period of time. The memorymay be configured to include the instructions to determine one or more occupancy trends for each zone for the first period of time using the trained machine learning (ML) model. Further, the memorymay be configured to include the instructions to map the determined occupancy trends for each of the one or more zones for the first period of time with fluctuations in occupancy of each of the one or more zones in real-time and booking status of each of the one or more zones.

The memorymay be configured to include the instructions to predict occupancy of each of the one or more zones of the buildingfor a second time period and a threshold time required for each of the one or more zones to heat or cool at one or more temperature set points using the trained ML model based at least on the mapping. The memorymay be configured to include the instructions to adjust one or more temperature set points for each of the one or more zones at the threshold time based at least on the prediction. It is apparent to a person with ordinary skill in the art that the one or more instructions stored in the memoryenable the hardware of the serverto 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 servermay further comprise the input/output circuitry. The input/output circuitrymay enable a user to communicate or interface with the server, via the user device. The user devicemay include N number of user devices. In some embodiments, the input/output circuitrymay act as a medium to transmit input from the interface to and from the server. In some embodiments, the input/output circuitrymay refer to the hardware and software components that facilitate the exchange of information between user deviceand the server. In one example, the user devicemay include a graphical user interface (GUI) (not shown) as input circuitry to allow the one or more users to input data. The input/output circuitrymay 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 circuitrymay include various output circuitry such as a display to show the adjusted one or more temperature set points.

In some embodiments, the servermay further comprise the communication circuitry. The communication circuitrymay allow the serverto exchange data or information with other systems or apparatuses. Further, the communication circuitrymay include network interfaces, protocols, and software modules responsible for sending and receiving data or information. In some embodiments, the communication circuitrymay include Ethernet ports, Wi-Fi adapters, or communication protocols like HTTP or MQTT for connecting with other systems. The communication circuitrymay 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 circuitrymay allow the serverto stay up-to-date and accurately track the at least one normalized alerts.

In some embodiments, the input/output circuitryand the communication circuitrymay 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 the above-mentioned components of the serverhave been provided only for illustration purposes, without departing from the scope of the disclosure.

illustrate an architecture of the systemfor zone level optimization, in accordance with an example embodiment of the present disclosure.are described in conjunction with. In some embodiments, the architecture of the systemmay include one or more zones, a plurality of sensors comprising an occupancy sensor, and a temperature/humidity sensor. Further, the architecture of the systemmay include a HVAC unit, a HVAC controller, and the user device.