US 12,455,091 B2
Systems and methods for predicting occupancy for one building using a model trained at another building
Rohil Pal, Lal Bangla Kanpur (IN); Navneet Kumar, Gurgaon (IN); Deepika Sandeep, Bangalore (IN); Prabhat Ranjan, Bangalore (IN); and Bhavesh Gupta, Niantic, CT (US)
Assigned to HONEYWELL INTERNATIONAL INC., Charlotte, NC (US)
Filed by Honeywell International Inc., Charlotte, NC (US)
Filed on Sep. 21, 2022, as Appl. No. 17/949,331.
Prior Publication US 2024/0093901 A1, Mar. 21, 2024
Int. Cl. F24F 11/64 (2018.01); F24F 110/10 (2018.01); F24F 110/64 (2018.01); F24F 110/66 (2018.01); F24F 110/70 (2018.01); F24F 120/10 (2018.01)
CPC F24F 11/64 (2018.01) [F24F 2110/10 (2018.01); F24F 2110/64 (2018.01); F24F 2110/66 (2018.01); F24F 2110/70 (2018.01); F24F 2120/10 (2018.01)] 12 Claims
OG exemplary drawing
 
1. A method for controlling one or more components of a Building Management System (BMS) of a use building in accordance with predicted occupancy of the use building, the predicted occupancy based upon a trained model, the method comprising:
training a model by:
providing the model with time stamped environmental data and corresponding time stamped occupancy data pertaining to a training building that is different from the use building, wherein the time stamped environmental data is derived from one or more environmental sensors of the training building and the corresponding time stamped occupancy data is derived from one or more occupancy sensors of the training building;
training the model over time using the time stamped environmental data and the corresponding time stamped occupancy data, resulting in a trained model;
employing the trained model in the use building, where employing the trained model in the use building comprises:
providing the trained model with time stamped environmental data pertaining to the use building, wherein the time stamped environmental data is derived from one or more environmental sensors of the use building;
receiving a raw occupancy count value produced by the trained model;
applying one or more normalization factors to the raw occupancy count value to produce a predicted occupancy value, wherein the one or more normalization factors account for one or more differences between the training building and the use building including one or more of:
a volumetric difference between the training building and the use building; and
a maximum occupancy count difference between the training building and the use building;
the trained model outputting the predicted occupancy value that represents a predicted occupancy count in the use building; and
operating the BMS of the use building based at least in part on the predicted occupancy value.