A method and system for operating neighbouring microgrids is disclosed. The method includes creating a first set of energy management policies associated with a predefined area and a second set of energy management policies associated with a microgrid. The method includes measuring power usage data associated with predefined area and current charge level of batteries within the predefined area. The method further includes determining future power demand of the predefined area, based on predefined analytics performed on the measured power usage data and the current charge level of the batteries. The method includes adapting the data models of predefined area with the data models used in microgrid and utility operations for seamless integration of operations and to optimize overall operations while complying with local, edge and overall policies. The method includes applying the first set of energy management policies within the predefined area based on the determined future power demand.

A disclosed example includes: a temperature predictor to determine a combined ambient air temperature of a data center during a future duration based on 1) a first ambient air temperature corresponding to heat generated by hardware resources in physical server racks when executing workloads and 2) a second ambient air temperature corresponding to a building structure of the data center; a power utilization analyzer to determine a predicted total data center power utilization for the future duration based on a computing power utilization and a climate control power utilization, the climate control power utilization based on a power utilization corresponding to adjusting the combined ambient air temperature to satisfy an ambient air temperature threshold; and a power manager to configure a power supply station to deliver an amount of electrical power during the future duration to satisfy the predicted total data center power utilization.

Apparatuses, systems, methods, and computer program products are presented for a building-automation system controller. A building-automation system controller manages and/or controls energy, thermal, and/or functional systems and subsystems thereof utilizing a sensor, a physical model, a simulation engine, one or more predictive control loops, an optimal cost function, and an error band. A control loop is designed to utilize a simulation engine to predict a simulated predicted sensor value of a controlled system under a simulated control regime. A simulated control regime having an optimal cost function is selected for a controlled system until the controlled system diverges from the simulated predicted sensor value beyond an error band indicating uncertainty in a predicted future behavior so that a control loop is formed utilizing a simulation engine to predict a different future behavior in response to the controlled system diverging from the simulated predicted sensor value.

Systems and methods for monitoring an operational system. A data set with output power values and associated environmental data values for an electrical generation system are accumulated. Statistical relationships are determined for output power values and environmental data values. Outlying data is determined based on the statistical relationships and are removed from the data set to create selected data. A regression model is developed from the selected data to map predicted output power values to values of environmental data. Data with present output power values and present associated environmental data for the electrical generation system are later received. Predicted output power values are predicted by the regression model for the present associated environmental data. An output power discrepancy is identified by comparing the predicted output power to the present output power. A notification of an anomaly is provided based on identification of the output power discrepancy.

Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.

The present disclosure provides a robot pose determination method including: collecting laser frames; calculating a current pose of the robot in a map pointed by a first pointer based on the laser frames, and obtaining an amount of the laser frames having been inserted into the map pointed by the first pointer; inserting the laser frames into a map pointed by the first pointer, if less than a first threshold; inserting the laser frames into the map pointed by the first pointer and a map pointed by a second pointer, if greater than or equal to the first threshold and less than a second threshold; and pointing the first pointer to the map pointed by the second pointer, pointing the second pointer to a newly created empty map, and inserting the laser frames into the map pointed by the first pointer, if equal to the second threshold.

Systems and methods are provided for determining a weather forecast corresponding to a location of an air handling unit for a building, generating a foot traffic forecast for a specified time period in the building, and generating a predicted energy consumption curve based on the weather forecast and generated foot traffic forecast for the specified time period. Based on the predicted energy consumption curve, the systems and methods further provide for generating settings for controllable energy devices of the air handling unit to control the air handling unit for the specified time period.

Machine learning techniques provide predictions of computer system load and cooling requirements, allowing cooling systems to anticipate, prepare for, and ameliorate, those requirements. Techniques provide confirmation or denial that the computer and cooling systems are operating as expected, allowing those systems to determine if any parts are failing. Techniques provide information regarding likely effect due to proposed changes. Techniques provide probabilistic predictions, well in advance, of whether parts will fail or degrade. Techniques provide probabilistic predictions whether parts will fail or degrade, responsive to degrees of redundancy, set points for of cooling, or set points when cooling starts or ends.

Systems and methods for monitoring health of systems and components of an oil rig are disclosed. Monitoring usage parameters allows for a probabilistic way to determine when rig equipment will fail. A failure probability curve based on past performance of the equipment can be used. Multiple usage metrics allow for increased accuracy and certainty of a time of failure for the equipment. Using a sufficiently high number of usage metrics allows the failure probability range to be very narrow and therefore the certainty of the prediction is high.

A building system includes one or more storage devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to receive an unstructured user question from a user device of a user and query a graph database based on the unstructured user question to extract context associated with the unstructured user question from contextual information of a building stored by the graph database, wherein the graph database stores the contextual information of the building through nodes and edges between the nodes, wherein the nodes represent equipment, spaces, people, and events associated building and the edges represent relationships between the equipment, spaces, people, and events. The instructions further cause the one or more processors to retrieve data from one or more data sources based on the context and compose a presentation based on the retrieved data.