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    METHODS AND SYSTEMS FOR OPERATING AN HVAC SYSTEM
    1.
    发明公开
    METHODS AND SYSTEMS FOR OPERATING AN HVAC SYSTEM 审中-公开

    公开(公告)号：US20240003576A1

    公开(公告)日：2024-01-04

    申请号：US18343538

    申请日：2023-06-28

    Applicant: Honeywell International Inc.

    Inventor： Rohil Pal ,  Aman Kumar Rai ,  Vishwanath Gupta ,  Banuprakash Balakrishna ,  Navneet Kumar ,  Deepika Sandeep

    IPC: F24F11/65

    CPC classification number: F24F11/65

    Abstract: Methods and systems for operating a Heating, Ventilating and Air Conditioning (HVAC) system in accordance with one of a plurality of operating modes. The plurality of operating modes include one or more of a health mode, an energy savings mode and a balanced mode. In some cases, the operating modes include two or more energy saving modes. The currently operating mode is selected based on the current operating conditions of the building and the desired goals of the building operator. The goals can include, for example, reducing energy usage, reducing pathogen risks, increasing air quality and/or a combination of these goals. In some cases, the operating modes are autonomously controlled. In some cases, the operating modes are manually controlled.

    AUTOMATIC MACHINE LEARNING BASED PREDICTION OF BASELINE ENERGY CONSUMPTION
    2.
    发明公开
    AUTOMATIC MACHINE LEARNING BASED PREDICTION OF BASELINE ENERGY CONSUMPTION 审中-公开

    公开(公告)号：US20230385469A1

    公开(公告)日：2023-11-30

    申请号：US17827230

    申请日：2022-05-27

    Applicant: Honeywell International Inc.

    Inventor： Ananda Vel Murugan Chandra Mohan ,  Banuprakash Balakrishna ,  Chhavi Chawla ,  Deepika Sandeep ,  Rohil Pal ,  Surjayan Ghosh ,  Swaroop Reddy Konala

    IPC: G06F30/13 ,  G06F30/27

    CPC classification number: G06F30/13 ,  G06F30/27

    Abstract: The present solution, approach or method, including an end-to-end automated data pipeline for data ingestion, storage, analysis, deployment, and a machine learning model maintenance. The present solution, approach or method, which computes a baseline using machine learning methods, may help in the following ways. Accurate real time estimation may help evaluate the deviation in the actual energy consumption, effectively identifying underlying root causes for an increase in actual consumption, as compared to the estimated energy. Triangulating the time of day and place of high energy consumption results in quicker resolution. Accurately quantifying energy savings may be helpful. Forecasting energy consumption in the future, may enable planning for future energy needs. Energy saving calculations may be done by comparing actual consumption versus baseline predicted consumption based for a specific baseline period. This solution may offer a configurable machine learning model, which takes on energy consumption patterns.

    METHOD AND SYSTEM FOR CONTROLLING A FRESH AIR INTAKE OF AN AIR HANDLING UNIT OF AN HVAC SYSTEM
    3.
    发明申请
    METHOD AND SYSTEM FOR CONTROLLING A FRESH AIR INTAKE OF AN AIR HANDLING UNIT OF AN HVAC SYSTEM 有权

    公开(公告)号：US20240384887A1

    公开(公告)日：2024-11-21

    申请号：US18319447

    申请日：2023-05-17

    Applicant: Honeywell International Inc.

    Inventor： Vishwanath Gupta ,  Kanchan Aggarwal ,  Deepika Sandeep ,  Rohil Pal ,  Madhav Kamath ,  Bhavesh S. Gupta ,  Prabhat Ranjan

    IPC: F24F11/63

    Abstract: Controlling a fresh air intake includes determining a fresh air intake damper position based on the supply air flowrate, a measure of energy delivered to the supply air flow, or a measure of humidity of the supply air flow. Determining the fresh air intake damper position is subject to a constraint that the AHU maintains one or more comfort conditions in the building space and one or more of a constraint regarding maintaining one or more Indoor Air Quality (IAQ) contaminants in the building space below one or more IAQ thresholds, minimizing energy consumption of the AHU, and maximizing the fresh air ventilation air flow into the building space. Various parameters used to determine the fresh air intake damper position may be derived from available sensed conditions so as to reduce the number of physical sensors that are required.

    OCCUPANCY ESTIMATION BASED ON MULTIPLE SENSOR INPUTS
    5.
    发明公开
    OCCUPANCY ESTIMATION BASED ON MULTIPLE SENSOR INPUTS 审中-公开

    公开(公告)号：US20240192650A1

    公开(公告)日：2024-06-13

    申请号：US18528963

    申请日：2023-12-05

    Applicant: HONEYWELL INTERNATIONAL INC.

    Inventor： Siddhant Sharad Ghungrudkar ,  Bhavesh S. Gupta ,  Prabhat Ranjan ,  Surjayan Ghosh ,  Deepika Sandeep ,  Rohil Pal

    IPC: G05B15/02 ,  F24F11/30 ,  H05B47/115

    CPC classification number: G05B15/02 ,  F24F11/30 ,  H05B47/115 ,  F24F2120/10

    Abstract: An occupancy count of the space of a building from each of a plurality of occupancy sensors may be monitored and an error parameter for each of the plurality of occupancy sensors may be identified, each error parameter representative of a difference between the occupancy count of the respective occupancy sensor and a ground truth occupancy count of the space, normalized over a period of time. An assigned weight for each of the plurality of occupancy sensors may be determined based at least in part on the respective error parameter. The estimated occupancy count of the space of the building is determined based at least in part on the occupancy count of each of the plurality of occupancy sensors and the assigned weight of each of the plurality of occupancy sensors. The BMS system is controlled based at least in part on the estimated occupancy count.

    SYSTEMS AND METHODS FOR PREDICTING OCCUPANCY FOR ONE BUILDING USING A MODEL TRAINED AT ANOTHER BUILDING
    6.
    发明公开
    SYSTEMS AND METHODS FOR PREDICTING OCCUPANCY FOR ONE BUILDING USING A MODEL TRAINED AT ANOTHER BUILDING 审中-公开

    公开(公告)号：US20240093901A1

    公开(公告)日：2024-03-21

    申请号：US17949331

    申请日：2022-09-21

    Applicant: Honeywell International Inc.

    Inventor： Rohil Pal ,  Navneet Kumar ,  Deepika Sandeep ,  Prabhat Ranjan ,  Bhavesh Gupta

    IPC: F24F11/64

    CPC classification number: F24F11/64 ,  F24F2120/10

    Abstract: A Building Management System (BMS) may be controlled in accordance with predicted occupancy using a trained model. A model is trained by providing the model with time stamped environmental data and corresponding time stamped occupancy data pertaining to a training 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. Once trained, the trained model is provided with time stamped environmental data for a use building that is derived from one or more environmental sensors of the use building. Occupancy data for the use building is not required. The trained model outputs a predicted occupancy value that represents a predicted occupancy count in the use building, and the BMS of the use building is controlled based at least in part on the predicted occupancy value.

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