Abstract: The disclosed subject matter provides systems and methods for allocating resources within an infrastructure, such as an electrical grid, in response to changes to inputs and output demands on the infrastructure, such as energy sources and sinks.

Abstract: The disclosed subject matter provides systems and methods for allocating resources within an infrastructure, such as an electrical grid, in response to changes to inputs and output demands on the infrastructure, such as energy sources and sinks.

Abstract: Techniques for predicting a failure metric of a physical system using a semiparametric model, including providing raw data representative of the physical system, to identify a set of units at risk in the physical system, a set of times of treatment corresponding to a event of at least one unit in the set of units, and an index-set of the at least one unit for which a event has occurred. A parametric and a nonparametric component of the semiparametric model are estimated and a hazard rate is predicted at a given time with the semiparametric model.

Abstract: The disclosed subject matter provides systems and methods for allocating resources within an infrastructure, such as an electrical grid, in response to changes to inputs and output demands on the infrastructure, such as energy sources and sinks.

Abstract: Techniques for determining forecast information for a resource using learning algorithms are disclosed. The techniques can include an ensemble of machine learning algorithms. The techniques can also use latent states to generate training data. The techniques can identify actions for managing the resource based on the forecast information. The resource can include energy usage in buildings, distribution facilities, and resources such as Electric Delivery Vehicles. The resource can also include forecasting package volume for businesses.

Abstract: Techniques for managing one or more buildings, including collecting historical building data, real-time building data, historical exogenous data, and real-time exogenous data and receiving the collected data at an adaptive stochastic controller. The adaptive stochastic controller can generate at least one predicted condition with a predictive model. The adaptive stochastic controller can generate one or more executable recommendations based on at least the predicted conditions and one or more performance measurements corresponding to the executable recommendations.

Abstract: The disclosed subject matter relates to an integrated decision support “cockpit” or control center for displaying, analyzing, and/or responding to, various events and contingencies that can occur within an electrical grid.

Abstract: Techniques for predicting a failure metric of a physical system using a semiparametric model, including providing raw data representative of the physical system, to identify a set of units at risk in the physical system, a set of times of treatment corresponding to a event of at least one unit in the set of units, and an index-set of the at least one unit for which a event has occurred. A parametric and a nonparametric component of the semiparametric model are estimated and a hazard rate is predicted at a given time with the semiparametric model.

Abstract: The disclosed subject matter relates to an integrated decision support “cockpit” or control center for displaying, analyzing, and/or responding to, various events and contingencies that can occur within an electrical grid.

Abstract: Techniques for managing one or more buildings, including collecting historical building data, real-time building data, historical exogenous data, and real-time exogenous data and receiving the collected data at an adaptive stochastic controller. The adaptive stochastic controller can generate at least one predicted condition with a predictive model. The adaptive stochastic controller can generate one or more executable recommendations based on at least the predicted conditions and one or more performance measurements corresponding to the executable recommendations.

Abstract: A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of

Abstract: Techniques for generating a dynamic treatment control policy for a cyber-physical system having one or more components, including a data collector for collecting data representative of the cyber-physical system, and adaptive stochastic controller including one or more models for generating a predicted value corresponding to available actions based on an objective function, and an approximate dynamic programming element configured to receive actual operation metrics corresponding to the available actions. The approximate dynamic programming element can learn a state-action map and generate a dynamic treatment control policy using the one or more models.

Abstract: A capital asset planning system for selecting assets for improvement within an infrastructure that includes one or more data sources descriptive of the infrastructure, one or more databases, coupled to the one or more data sources, to compile the one or more data sources, one or more processors, each coupled to and having respective communication interfaces to receive data from the one or more databases. The processor includes a predictor to generate a first metric of estimated infrastructure effectiveness based, at least in part, on a current status of the infrastructure, a second metric of estimated infrastructure effectiveness based, at least in part, on a user-selected, proposed changed configuration of the infrastructure, and a net metric of infrastructure effectiveness based, at least in part, on said first metric and said second metric.

Abstract: Techniques for evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure include collecting data, representative of at least one pre-defined metric, from the infrastructure during first and second time periods corresponding to before and after a change has been implemented, respectively. A machine learning system can receive compiled data representative of the first time period and generate corresponding machine learning data. A machine learning results evaluator can empirically analyze the generated machine learning data. An implementer can implement the change to the infrastructure based at least in part on the data from a machine learning data outputer. A system performance improvement evaluator can compare the compiled data representative of the first time period to that of the second time period to determine a difference, if any, and compare the difference, if any, to a prediction based on the generated machine learning data.

Abstract: A system and computer-implemented method of providing contingency analysis information for a utility service network that includes obtaining contingency analysis information from a plurality of external sources, integrally combining the contingency analysis information obtained from each of the plurality of external sources into a single application and prioritizing the contingency analysis information in a predetermined order, dynamically updating, the contingency analysis information obtained from each of the plurality of external sources and the prioritization of the contingency analysis information based on status information, and displaying the contingency analysis information to a user via a graphical user interface.

Abstract: A computer-aided lean management (CALM) controller system recommends actions and manages production in an oil and gas reservoir/field as its properties and conditions change with time. The reservoir/field is characterized and represented as an electronic-field (“e-field”). A plurality of system applications describe dynamic and static e-field properties and conditions. The application workflows are integrated and combined in a feedback loop between actions taken in the field and metrics that score the success or failure of those actions. A controller/optimizer operates on the combination of the application workflows to compute production strategies and actions. The controller/optimizer is configured to generate a best action sequence for production, which is economically “always-in-the-money.

Abstract: A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of

Abstract: A system for managing a battery in communication with an electrical grid that includes (a) a data collector to collect data representative of an electrical grid; (b) an ASC controller operatively coupled to the data collector and adapted to receive collected data therefrom, the ASC controller comprising a financial strategizer to send instructions based on the collected data; and (c) a battery controller operatively coupled to the ASC controller to receive the instructions transmitted by the ASC controller, the battery controller configured to dictate whether the battery receives electricity from, or transmits electricity to the electrical grid.

Abstract: A capital asset planning system for selecting assets for improvement within an infrastructure that includes one or more data sources descriptive of the infrastructure, one or more databases, coupled to the one or more data sources, to compile the one or more data sources, one or more processors, each coupled to and having respective communication interfaces to receive data from the one or more databases. The processor includes a predictor to generate a first metric of estimated infrastructure effectiveness based, at least in part, on a current status of the infrastructure, a second metric of estimated infrastructure effectiveness based, at least in part, on a user-selected, proposed changed configuration of the infrastructure, and a net metric of infrastructure effectiveness based, at least in part, on said first metric and said second metric.

Abstract: Techniques for evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure include collecting data, representative of at least one pre-defined metric, from the infrastructure during first and second time periods corresponding to before and after a change has been implemented, respectively. A machine learning system can receive compiled data representative of the first time period and generate corresponding machine learning data. A machine learning results evaluator can empirically analyze the generated machine learning data. An implementer can implement the change to the infrastructure based at least in part on the data from a machine learning data outputer. A system performance improvement evaluator can compare the compiled data representative of the first time period to that of the second time period to determine a difference, if any, and compare the difference, if any, to a prediction based on the generated machine learning data.