Method and system to achieve heating and cooling temperature within one or more zones when occupied

Abstract

A method and system to achieve heating and cooling temperature within one or more zones when occupied is disclosed. The method comprises training, via at least one processor, a machine learning (ML) model based at least on historical temperature data and input conditions of one or more zones; receiving temperature data from one or more sensors and one or more input conditions of the one or more zones for a predefined time period in real-time; determining a threshold time period to achieve heating or cooling temperature within the one or more zones when occupied using the trained ML model, based at least on the received temperature data and input conditions of the one or more zones for the predefined time period; and adjusting the one or more temperature set points to achieve heating or cooling temperature within the one or more zones when occupied.

What is claimed is: 
     
         1 . A method comprising:
 training, via at least one processor, a machine learning (ML) model based at least on historical temperature data and one or more input conditions of one or more zones, wherein the historical temperature data comprises temperature of each of the one or more zones over a period of time and the one or more input conditions comprises one or more historical temperature set points, historical occupancy data, or one or more static parameters of each zone;   receiving, via the at least one processor, temperature data from one or more sensors and one or more input conditions of the one or more zones for a predefined time period in real-time, wherein the one or more input conditions comprises one or more temperature set points to be set for each zone and occupancy data for each zone;   determining, via the at least one processor, a threshold time period to achieve heating or cooling temperature within the one or more zones when occupied using the trained ML model, based at least on the received temperature data and one or more input conditions of the one or more zones for the predefined time period, wherein the threshold time period corresponds to time required for each zone to achieve one or more temperature set points related to heating or cooling when occupied; and   adjusting, via the at least one processor, the one or more temperature set points for the one or more zones at the determined threshold time period using the trained ML model to achieve heating or cooling temperature within the one or more zones when occupied.   
     
     
         2 . The method of  claim 1 , wherein the static parameters comprises at least one of size of the one or more zones, shape of the one or more zones, a number of door openings in the one or more zones, a number of windows and doors present in each zone, floor height of the one or more zones, or location of the one or more zones. 
     
     
         3 . 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. 
     
     
         4 . The method of  claim 1 , wherein the one or more sensors comprises at least one of a temperature sensor, a humidity sensor, or occupancy sensors. 
     
     
         5 . 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. 
     
     
         6 . The method of  claim 1 , wherein the ML model corresponds to a statistical model and a piecewise linear model that uses one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. 
     
     
         7 . The method of  claim 1 , wherein the one or more temperature set points corresponds to a set point at which the temperature is adjusted for heating or cooling of the one or more zones. 
     
     
         8 . A system comprising:
 a memory;   at least one processor communicatively coupled to the memory, wherein the at least one processor is configured to:
 train a machine learning (ML) model based at least on historical temperature data and one or more input conditions of one or more zones, wherein the historical temperature data comprises temperature of each of the one or more zones over a period of time and the one or more input conditions comprises one or more historical temperature set points, historical occupancy data, or one or more static parameters of each zone; 
 receive temperature data from one or more sensors and one or more input conditions of the one or more zones for a predefined time period in real-time, wherein the one or more input conditions comprises one or more temperature set points to be set for each zone and occupancy data for each zone; 
 determine a threshold time period to achieve heating or cooling temperature within the one or more zones when occupied using the trained ML model, based at least on the received temperature data and the one or more input conditions of the one or more zones for the predefined time period, wherein the threshold time period corresponds to time required for each zone to achieve one or more temperature set points related to heating or cooling when occupied; and 
 adjust the one or more temperature set points for the one or more zones at the determined threshold time period using the trained ML model to achieve heating or cooling temperature within the one or more zones when occupied. 
   
     
     
         9 . The system of  claim 8 , wherein the static parameters comprises at least one of size of the one or more zones, shape of the one or more zones, a number of door openings in the one or more zones, a number of windows and doors present in each zone, floor height of the one or more zones, or location of the one or more zones. 
     
     
         10 . The system of  claim 8 , 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. 
     
     
         11 . The system of  claim 8 , wherein the one or more sensors comprises at least one of a temperature sensor, a humidity sensor, or occupancy sensors. 
     
     
         12 . The system of  claim 8 , wherein the one or more zones comprises at least one of a building, a warehouse, a storage unit, or an office space. 
     
     
         13 . The system of  claim 8 , wherein the ML model corresponds to a statistical model and a piecewise linear model that uses one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. 
     
     
         14 . The system of  claim 8 , wherein the one or more temperature set points corresponds to a set point at which the temperature is adjusted for heating or cooling of the one or more zones. 
     
     
         15 . 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:
 training a machine learning (ML) model based at least on historical temperature data and one or more input conditions of one or more zones, wherein the historical temperature data comprises temperature of each of the one or more zones over a period of time and the one or more input conditions comprises one or more historical temperature set points, historical occupancy data, or one or more static parameters of each zone;   receiving temperature data from one or more sensors and one or more input conditions of the one or more zones for a predefined time period in real-time, wherein the one or more input conditions comprises one or more temperature set points to be set for each zone and occupancy data for each zone;   determining a threshold time period to achieve heating or cooling temperature within the one or more zones when occupied using the trained ML model, based at least on the received temperature data and the one or more input conditions of the one or more zones for the predefined time period, wherein the threshold time period corresponds to time required for each zone to achieve one or more temperature set points related to heating or cooling when occupied; and   adjusting the one or more temperature set points for the one or more zones at the determined threshold time period using the trained ML model to achieve heating or cooling temperature within the one or more zones when occupied.   
     
     
         16 . The non-transitory machine-readable information storage medium of  claim 15 , wherein the static parameters comprises at least one of size of the one or more zones, shape of the one or more zones, a number of door openings in the one or more zones, a number of windows and doors present in each zone, floor height of the one or more zones, or location of the one or more zones. 
     
     
         17 . The non-transitory machine-readable information storage medium of  claim 15 , 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. 
     
     
         18 . The non-transitory machine-readable information storage medium of  claim 15 , wherein the one or more sensors comprises at least one of a temperature sensor, a humidity sensor, or occupancy sensors. 
     
     
         19 . The non-transitory machine-readable information storage medium of  claim 15 , wherein the one or more zones comprises at least one of a building, a warehouse, a storage unit, or an office space. 
     
     
         20 . The non-transitory machine-readable information storage medium of  claim 15 , wherein the ML model corresponds to a statistical model and a piecewise linear model that uses one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques.