A disclosed example includes: a resource utilization analyzer to determine 1) first workloads of a first workload type deployed in a first server room in a data center, and 2) second workloads of a second workload type deployed in the first server room; a workload authorizer to determine that first virtual machines executing the first workloads and second virtual machines executing the second workloads cause a first server rack to generate an amount of heat; and a migrator to migrate the first virtual machines from the first server rack of the first server room to a second server rack of a second server room in the data center to reduce a temperature in the first server room based on the amount of heat, the migrator to migrate the first virtual machines to the second server rack without migrating the second virtual machines to the second server rack.

A device may receive a power demand for a load and a weather forecast for a time period. The device may determine a first supply of power available from a photo-voltaic (PV) installation for the time period based on the weather forecast. The device may determine a power deficit for the time period based on the power demand and the first supply of power. The device may determine a first cost associated with utilizing a second supply of power available from a battery and a second cost associated with utilizing a third supply of power available from an engine for the time period. The device may determine a power source to overcome the power deficit based on the first cost and the second cost and may cause the PV installation and the power source to supply power to satisfy the power demand for the load.

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.

This application provides methods and systems for rapid load support for grid frequency transient events. Example electric power systems may include a turbine, a generator coupled to the turbine, where the generator is configured to provide power to an electrical grid, and a controller configured to detect a grid event, determine a rate of change of frequency (rate of change of frequency) value, determine a predicted post-grid event governor set point based on the rate of change of frequency value, and initiate a change to at least one turbine operating parameter based on the predicted post-grid event governor set point.

When two event prediction models produce different numbers of catches, a computer system may be configured to determine which of the two models has the higher net value based on how a “Break-Even Alert Value Ratio” for the models compares to an estimate of the how many false flags are worth trading for one catch. Further, when comparing two event prediction models, a computer system may be configured to determine “catch equivalents” and “false-flag equivalents” numbers for the two different models based on potential-value and impact scores assigned to the models' predictions, and the computing system then use these “catch equivalents” and “false-flag equivalents” numbers in place of “catch” and “false flag” numbers that may be determined using other approaches.

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.

There is provided a method for providing a driving related guidance service by a service server. The method includes receiving advanced driver assistance system (ADAS) data of a vehicle related to a specific driving situation of the vehicle, location data of the vehicle, driving data of the vehicle, and a driving image captured during driving of the vehicle from a vehicle terminal device, generating guidance information related to the specific driving situation of the vehicle by analyzing the received data and the driving image, and providing a driving related guidance service for the vehicle using the generated guidance information.

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.

A system for detection of an anomaly in a discrete manufacturing process (DMP) with human-robot teams executing a task. Receive signals including robot, worker and DMP signals. Predict a sequence of events (SOEs) from DMP signals. Determine whether the predicted SOEs in the DMP signals is inconsistent with a behavior of operation of the DMP described in a DMP model, and if the predicted SOEs from DMP signals is inconsistent with the behavior, then an alarm is to be signaled. Input worker data into a Human Performance (HP) model, to obtain a state of the worker based on previously learned boundaries of human state. The state of the HW is then input into the HRI model and the DMP model to determine a classification of anomaly or no anomaly. Update a Human-Robot Interaction (HRI) model to obtain a control action of a robot or a type of an anomaly alarm.

Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.