Methods and systems for zone level occupancy prediction and energy optimization

Abstract

A method and system to predict occupancy status is disclosed. The method comprises receiving, via at least one processor, occupancy data of one or more zones via a plurality of sensors for a first period of time; determining occupancy trends for each zone for the first period of time using a trained machine learning (ML) model; mapping the determined occupancy trends for each zone with fluctuations in occupancy of each zone in real-time and booking status of each zone; and predicting occupancy of each zone for a second period of time and a threshold time for each zone 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 the one or more temperature set points for each zone at the threshold time based at least on the prediction.

What is claimed is: 
     
         1 . A method comprising:
 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, wherein the occupancy data comprises at least a number of occupants within each zone for the first period of time;   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, 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, 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;   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; and   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.   
     
     
         2 . The method of  claim 1 , wherein the ML model for each of the one or more zones is trained based at least on the received occupancy data. 
     
     
         3 . The method of  claim 1 , wherein 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. 
     
     
         4 . The method of  claim 1 , wherein 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 2 ) 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, wherein opening within each zone corresponds to a number of windows and doors present in each zone. 
     
     
         6 . The method of  claim 1 , wherein 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. 
     
     
         7 . The method of  claim 1 , wherein 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,   wherein 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.   
     
     
         8 . The method of  claim 7 , wherein the heating cycle and the cooling cycle are identified based at least on the change in the one or more temperature set points, and 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. 
     
     
         9 . The method of  claim 1 , wherein the at least one processor is configured to train the ML model using one or more Artificial Intelligence (AI)/Machine Learning (ML) techniques. 
     
     
         10 . A system comprising:
 a memory;   at least one processor communicatively coupled to the memory, wherein 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, wherein the occupancy data comprises at least a number of occupants within each zone for the first period of time; 
 determine 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; 
 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; 
 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; and 
 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. 
   
     
     
         11 . The system of  claim 10 , wherein 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. 
     
     
         12 . The system of  claim 10 , wherein 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. 
     
     
         13 . The system of  claim 10 , wherein 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 2 ) sensors. 
     
     
         14 . The system of  claim 10 , wherein 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 a number of windows and doors present in each zone. 
     
     
         15 . The system of  claim 10 , wherein 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, years. 
     
     
         16 . The system of  claim 10 , wherein 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,   wherein 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.   
     
     
         17 . The system of  claim 16 , wherein the heating cycle and the cooling cycle are identified based at least on change in the one or more temperature set points, and 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. 
     
     
         18 . 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 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.   
     
     
         19 . The non-transitory machine-readable information storage medium of  claim 18 , wherein 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. 
     
     
         20 . The non-transitory machine-readable information storage medium of  claim 18 , wherein 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.