Abstract: 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.

Abstract: Systems and methods for energy optimization of an HVAC system of a building are disclosed. The method includes collecting data from field sensors, the data including damper positions of a plurality of air handling units; calculating an aggregated ventilation rate for the air handling units based on the damper positions; retrieving a predictive model that outputs a predicted state for a plurality of building zones; inputting the damper positions to the predictive model; retrieving a baseline model that outputs an expected energy cost for a reporting period; inputting the aggregated ventilation rate to the baseline model; performing batch data analytics on the predictive model to update a building model; optimizing energy use to minimize actual energy cost based on the building model, energy cost information, and the data collected from the field sensors; generating energy savings data based on the baseline model and the actual energy cost.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments described herein relate to automated asset strategy selection for assets via cloud-based supervisory control. In this regard, one or more baselines for asset strategy data related to an asset is estimated based on a regression analysis process associated with the asset. Additionally, a maximum uncertainty level for the one or more baselines is determined, one or more conditions related to energy optimization for an environment associated with the asset is estimated for a future period of time, and a predicted uncertainty level for the one or more conditions related to the energy optimization is determined. Based on a comparison between the maximum uncertainty level and the predicted uncertainty level, a predetermined asset strategy or an energy optimization asset strategy for the future period of time is then selected.

Abstract: An approach and system incorporating obtaining a semantic model for a device, identifying properties from the semantic model, processing the properties, obtaining a property role, identifying a property role filter that correlates to the property role, and generating parameter values associated with the property role filter.

Abstract: 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.

Abstract: 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.

Abstract: Method, apparatus and computer program product for comfort model extrapolation. For example, the apparatus includes at least one processor and at least one non-transitory memory including program code. The at least one non-transitory memory and the program code are configured to, with the at least one processor, obtain a plurality of known comfort models including: one or more cross-space comfort models each associated with a primary occupant profile and a known spatial element of one or more known spatial elements, one or more cross-profile comfort models each associated with a secondary occupant profile and an unknown spatial element, and one or more cross-context comfort models each associated with a secondary occupant profile and a known spatial element; and generate a unknown comfort model for the primary occupant profile and the unknown spatial element based on the plurality of known comfort models.

Abstract: 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.

Abstract: Method, apparatus and computer program product for comfort model extrapolation. For example, the apparatus includes at least one processor and at least one non-transitory memory including program code. The at least one non-transitory memory and the program code are configured to, with the at least one processor, obtain a plurality of known comfort models including: one or more cross-space comfort models each associated with a primary occupant profile and a known spatial element of one or more known spatial elements, one or more cross-profile comfort models each associated with a secondary occupant profile and an unknown spatial element, and one or more cross-context comfort models each associated with a secondary occupant profile and a known spatial element; and generate a unknown comfort model for the primary occupant profile and the unknown spatial element based on the plurality of known comfort models.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods provide techniques for generating natural language data based on telemetry data. In one embodiments, a method includes at least operations configured to receive telemetry data, wherein the telemetry data includes temporal telemetry vectors; process the temporal telemetry vectors using an encoder model in order to generate a feature vector for the telemetry data; and process the feature vector using a decoder model in order to generate the natural language data.

Abstract: 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.

Abstract: 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.