Abstract: Electric Grid Analytics Learning Machine, EGALM, is a machine learning based, “brutally empirical” analysis system for use in all energy operations. EGALM is applicable to all aspects of the electricity operations from power plants to homes and businesses. EGALM is a data-centric, computational learning and predictive analysis system that uses open source algorithms and unique techniques applicable to all electricity operations in the United States and other foreign countries.

Abstract: Electric Grid Analytics Learning Machine, EGALM, is a machine learning based, “brutally empirical” analysis system for use in all energy operations. EGALM is applicable to all aspects of the electricity operations from power plants to homes and businesses. EGALM is a data-centric, computational learning and predictive analysis system that uses open source algorithms and unique techniques applicable to all electricity operations in the United States and other foreign countries.

Abstract: Energy Analytics Learning Machine (or EALM) system is a machine learning based, “brutally empirical” analysis system for use in optimizing the payout from one or more energy sources. EALM system optimizes exploration, production, distribution and/or consumption of an energy source while minimizing costs to the producer, transporter, refiner and/or consumer. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized energy data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual energy data are classified to correlate with optimal production to capture the dynamics of one or more energy sources of physically real or theoretically calculated systems to provide categorization results from labeled data sets to identify patterns.

Abstract: Energy Analytics Learning Machine (or EALM) system is a machine learning based, “brutally empirical” analysis system for use in optimizing the payout from one or more energy sources. EALM system optimizes exploration, production, distribution and/or consumption of an energy source while minimizing costs to the producer, transporter, refiner and/or consumer. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized energy data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual energy data are classified to correlate with optimal production to capture the dynamics of one or more energy sources of physically real or theoretically calculated systems to provide categorization results from labeled data sets to identify patterns.

Abstract: Petroleum Analytics Learning Machine (or PALM) system is a machine learning based, “brutally empirical” analysis system for use in all upstream and midstream oil and gas operations. PALM system optimizes exploration, production and gathering from at least one well of oil and natural gas fields to maximize production while minimizing costs. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual data are classified to correlate with optimal production to capture the dynamics of at least one or more wells of oil and natural gas fields and to provide categorization results from labeled data sets to identify patterns.

Abstract: Petroleum Analytics Learning Machine (or PALM) system is a machine learning based, “brutally empirical” analysis system for use in all upstream and midstream oil and gas operations. PALM system optimizes exploration, production and gathering from at least one well of oil and natural gas fields to maximize production while minimizing costs. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual data are classified to correlate with optimal production to capture the dynamics of at least one or more wells of oil and natural gas fields and to provide categorization results from labeled data sets to identify patterns.

Abstract: A utility distribution control system and method for performing distribution control of energy within a utility service network including an energy distribution network in communication with a plurality of energy resources. The energy distribution network includes a plurality of energy storage and generation devices which receive energy from at least one of the energy resources of the plurality of resources and distribute energy, and a controller which controls the plurality of energy storage and generation devices, to distribute energy.

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 computer-implemented method and system of providing utility service network information for a utility service network. The method includes obtaining utility service network information from a plurality of external sources, integrally combining the utility service network information obtained from each of the plurality of external sources into and displaying the utility service network information in real-time in a global positioning map to a user via a graphical user interface, selecting, via the user, specific utility service network information of the utility service network information, to be displayed, and automatically reconfiguring the system or manually reconfiguring the utility service network via the user, as needed based on the specific utility service network information selected.

Abstract: A system for heating a facility is provided. The system includes a first inlet from a district heating system. A heating system is arranged having a condensate outlet. A hyper-condensate recycler is provided having a second inlet coupled to the condensate outlet and a third inlet coupled to the first inlet. The hyper-condensate recycler includes a first outlet. A separator having a fourth inlet is coupled to the first outlet, the separator further having a second outlet fluidly coupled to the first inlet.

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 system and method for monitoring status of an electrical grid and one or more building subsystems. The system includes sensors in communication with an electrical grid, buildings that provide data related to the building subsystems, and a digital building operating system that includes a processor that performs instructions to process the data, identify the status of the electrical grid and the building subsystem, predict one or more events based on the data, and provide recommendations to the buildings such as how to prevent the bad event.

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 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: An optical sensor for detecting motion or movement in an area of interest and a method of operation is provided. The system includes a CMOS sensor having an array of pixels that captures images in an area of interest. The system monitors the average pixel value for the array to define a first state. If the average pixel value changes beyond a defined threshold, the system defines a second state. For each change in state, a signal is generated. In one embodiment, the optical sensor is used with a meter having a dial with an indicator. The optical sensor generates a signal each time the indicator passes through the area of interest to allow for the remote monitoring of a consumable commodity.

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 utility distribution control system and method for performing distribution control of energy within a utility service network including an energy distribution network in communication with a plurality of energy resources. The energy distribution network includes a plurality of energy storage and generation devices which receive energy from at least one of the energy resources of the plurality of resources and distribute energy, and a controller which controls the plurality of energy storage and generation devices, to distribute energy.

Abstract: A system for heating a facility is provided. The system includes a first inlet from a district heating system. A heating system is arranged having a condensate outlet. A hyper-condensate recycler is provided having a second inlet coupled to the condensate outlet and a third inlet coupled to the first inlet. The hyper-condensate recycler includes a first outlet. A separator having a fourth inlet is coupled to the first outlet, the separator further having a second outlet fluidly coupled to the first inlet.

Abstract: A metering system for measuring the electrical power used to charge a vehicle is provided. The metering system includes an electrical meter operably coupled to a conductor connected to the vehicle and an electrical outlet. A controller receives signals from the meter to record the measured electrical consumption. The controller includes a plurality of communications devices for communicating with different communications carriers. In one embodiment, the controller selects one of the communications devices based on availability and a desired criterion. The selected communications device then transmits the measured electrical power consumption to a utility provider. In one embodiment, the metering system is mobile.

Abstract: A charger for an electric vehicle is provided. The charge provides for multiple inputs that are combined to provide a single output at higher charging level. The charger includes a toroidal transformer that is electrically coupled between the multiple inputs and the output. The charge may also include a connection to allow an electrical vehicle to communicate with external controllers or servers.