Methods and systems for determining an optimal start and stop time of air handling units

Abstract

A method and system for determining optimal start and stop time of Air Handling Units (AHU) are disclosed. The method comprises receiving, via at least one processor, a set of parameters associated with one or more zones in real time; determining an occupancy status of each of one or more zones based at least on received set of parameters; determining at least one start time and stop time for an Air Handling Unit (AHU) for each of one or more zones using historical data and each model of a plurality of models; determining a weighted average value for the determined at least one start time and stop time for the AHU based on a predefined weight allocated to each of at least one start time and stop time; and determining an optimal start and stop (OSS) time of AHU for each zone based at least on weighted average value.

What is claimed is: 
     
         1 . A method comprising:
 receiving, via at least one processor, 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, 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;   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, 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, 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, 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, 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.   
     
     
         2 . The method of  claim 1 , wherein the one or more zones comprises at least one of a building, a warehouse, a storage unit, or an office space. 
     
     
         3 . The method of  claim 1 , wherein 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. 
     
     
         4 . The method of  claim 1 , wherein 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. 
     
     
         5 . The method of  claim 1 , wherein 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. 
     
     
         6 . The method of  claim 1 , wherein 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. 
     
     
         7 . The method of  claim 6 , wherein 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. 
     
     
         8 . The method of  claim 6 , wherein 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. 
     
     
         9 . The method of  claim 6  further comprising 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. 
     
     
         10 . The method of  claim 9 , wherein 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. 
     
     
         11 . The method of  claim 9 , wherein 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. 
     
     
         12 . The method of  claim 1 , wherein 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. 
     
     
         13 . The method of  claim 6  further comprising 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, wherein the one or more commands corresponds to an ON command or an OFF command. 
     
     
         14 . The method of  claim 1 , wherein 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. 
     
     
         15 . A system comprising:
 a memory; and   at least one processor communicatively coupled to the memory, wherein the at least one processor is configured to:
 receive 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; 
 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; 
 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, 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; 
 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, 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 
 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. 
   
     
     
         16 . The system of  claim 15 , wherein 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, and wherein 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 the predefined period of time and in the real time, and wherein 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, and wherein 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 the predefined period of time and in the real time. 
     
     
         17 . The system of  claim 16 , wherein 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. 
     
     
         18 . The system of  claim 16 , wherein the at least one processor is configured to switch 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. 
     
     
         19 . The system of  claim 18 , wherein 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, and wherein 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. 
     
     
         20 . A 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.