Honeywell International Inc.

Karel Marik

Petr Endel

Ondrej Holub

Karel Macek

Joseph Majewski

Petr Stluka

Martin Strelec

Roman Navratil

Chunfu Li

Josef Rieger

Raman Samusevich

Wendy K Foslien

Jiri Rojicek

Jeffrey Mathew Rye

Geoffrey Ho

Vaclav Slimacek

Eva Jerhotova

Steven D Gabel

Jakub Hvezda

Michal Vrana

Kapil Sharma

Giancarlo Mantovani

Rylan Clark

Various embodiments described herein relate to automated setpoint generation for assets via cloud-based supervisory control. In this regard, a request to perform supervisory control with respect to an asset is received. The request comprises an asset identifier indicating an identity of the asset. In response to the request, one or more setpoints for the asset is determined based on the asset identifier. Also in response to the request, comfort constraint data indicative of one or more comfort constraints for an environment associated with the asset is determined based on the asset identifier. Furthermore, the one or more setpoints for the asset is adjusted based on the comfort constraint data.

Automated setpoint generation for an asset via cloud-based supervisory control

A building supervisory control structure having a supervisor connectable to a cloud and a heating, ventilation and air conditioning (HVAC) unit connected to the supervisor and the cloud. A safety features stack is connected to the supervisor. The safety features stack includes a connectivity check automated fail-over, an optimization watchdog, a rule-based comfort protection mechanism, savings bottlenecks overview, a human override, and a notification mechanism.

Building supervisory control system having safety features

Occupancy data over time is received for each of several spaces within a building from occupancy sensors that are disposed within each of the spaces. An occupancy value is determined for each of at least some of the several spaces based on the received occupancy data, each occupancy value representative of a percent of time that the respective space was occupied over an identified period of time. The space that had a highest occupancy value over the identified period of time is identified. A utilization value is determined for each of the spaces, wherein the utilization value is representative of a ratio of the occupancy value of the respective space and the highest occupancy value. An operation of the building is changed based at least in part on the utilization value of at least one of the plurality of spaces.

Space utilization patterns for building optimization

An integrated energy optimizer having an edge side and a cloud side. The edge side may incorporate an energy optimizer, a building management system connected to the energy optimizer, a controller connected to the building management system, and equipment connected to the controller. The cloud side may have a cloud connected to the energy optimizer and to the building management system, and a user interface connected to the cloud. Data from the field sensor may go to the optimizer and the building management system. The data may be processed at the optimizer and the building management system for proper settings at the building management system.

Cloud and edge integrated energy optimizer

Systems and methods provide techniques for performing predictive maintenance data analysis based on telemetry data. In one embodiments, a method includes at least operations configured to obtain a training telemetry data object, determine a training input data object based on the training telemetry data object, obtain a maintenance data object, and generate a trained predictive maintenance convolutional neural network based on the training input data object and the maintenance data object. The trained predictive maintenance convolutional neural network can be utilized to generate maintenance predictions for a monitored system. The maintenance prediction can include data objects with visual explanatory capabilities, such as data objects that describe heatmaps over input telemetry data.

Predictive maintenance convolutional neural networks

Method, apparatus and computer program product for generating a supply stream temperature set-point for a particular distribution channel element associated with a heating, ventilation, and air-conditioning (HVAC) system. In one example, a method includes identifying a hierarchical position of the particular distribution channel element; identifying potential set point configuration actions associated with the particular distribution channel element, wherein each potential set point configuration action is expected to cause transition of the particular distribution channel element from a current state to a future state; determining an overall cost measure for each potential set point configuration action based at least in part on the hierarchical position of the particular distribution channel element, and generating the supply stream temperature set-point based on each overall cost measure associated with a potential set point configuration action.

Distributed set point configuration in heating, ventilation, and air-conditioning systems

Method, apparatus and computer program product for managing a heating, ventilation, and air-conditioning (HVAC) system. In one example, a method includes obtaining a differential pressure reading from each differential pressure sensor of the plurality of differential pressure sensors; obtaining a differential pressure set point for each differential pressure sensor; applying a shared set point parameter to each differential pressure set point for a differential pressure sensor in order to generate a parameterized set point for the differential pressure sensor; for each differential pressure sensor, generating a demand indicator for the differential pressure sensor based on the differential pressure reading from the differential pressure sensor and the parameterized set point for the differential pressure sensor; and performing the supervisory control based on at least one demand indicator.

Shared parameterization of differential pressure set points in heating, ventilation, and air-conditioning systems

Operating heating, ventilation, and air conditioning (HVAC) systems using occupancy sensing systems is described herein. One device includes instructions which, when executed by a processor, cause the processor to receive a mapping between a space of a plurality of spaces of a building, a fixture of a plurality of occupancy sensing system fixtures in the building, and an HVAC device of a plurality of HVAC devices associated with the building, and modify an operation of the HVAC device based on the mapping and responsive to a determination of occupancy in the space by the fixture.

Operating heating, ventilation, and air conditioning systems using occupancy sensing systems

Operating HVAC systems using occupancy sensing systems is described herein. One device includes instructions to receive information defining a plurality of spaces of a building, occupancy sensing system information describing a location of each of a plurality of occupancy sensing system fixtures with respect to a representation of the building, and HVAC system information describing a relationship between the plurality of spaces and a plurality of HVAC devices in the building, create a mapping between a space, a fixture, and an HVAC device based on the building information, and modify an operation of the HVAC device based on the mapping and responsive to a determination of occupancy in the space by the fixture.

Operating HVAC systems using occupancy sensing systems is described herein. One device includes instructions to receive a mapping describing relationships between a space of a plurality of spaces of a building, a plurality of fixtures of an occupancy sensing system installed in the space, and an upstream HVAC device associated with the building, wherein the upstream HVAC device serves a zone including the space, receive occupancy data determined by the fixture over a time period, filter the occupancy data to determine occupancy information associated with the fixture over the time period, determine an occupancy model associated with the space based on the occupancy information associated with the fixtures, and modify an operation of the upstream HVAC device based on the mapping and the occupancy model.

Devices, methods, and systems for building comfort control are described herein. One system includes computing device for building comfort control, comprising: a memory, and a processor configured to execute instructions to: receive feedback from a plurality of users on a comfort level for an area of a building, generate a comfort model for each of the plurality of users based on the feedback, and adjust a number of settings of a heating, ventilation, and air conditioning (HVAC) unit for the area based on the comfort model for users among the plurality of users who are currently in the area.

Building comfort control devices, methods, and systems

Methods, devices, and systems for aircraft taxi routing are described herein. One device includes instructions stored thereon which, when executed by a processor, cause the processor to receive a record of a plurality of historical routes taken by a plurality of aircraft through taxiways of an airport, receive an indication of a plurality of conditions particular to the airport, receive an indication of a starting point of a proposed route in the airport and an endpoint of the airport, wherein the starting point and the endpoint are associated with a proposed taxiing of an aircraft, determine a route between the starting point and the endpoint based on the plurality of historical routes and the plurality of conditions, and provide the determined route to an air traffic controller.

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Revnice, Czechia

Karel Marik