125 results about "Utility industry" patented technology

Systems and methods for annotating, also known as tagging, visually identifiable objects using natural language (i.e. spoken voice) are provided. More specifically, but not exclusively, this disclosure relates to systems and methods for tagging identified objects related to underground utilities and assets, and communication systems. Once tagged, data representing identified objects can be stored, transmitted, and / or mapped. In an exemplary embodiment, a utility service worker or other personnel may walk or drive around an area of interest using utility locating equipment or systems to collect data. As data is being collected, the user may visually identify utility related or other items using a laser pointer, and then use their voice to tag items of interest. A headset including a microphone may be provided for capturing the user's voice. Hardware and / or software may be provided for processing tagged items, and relating them to a specific location, and / or utility asset.

A method of operating a dryer appliance includes a step of initiating a dryer cycle. The method also includes a step of receiving a demand-side management signal defining a current state of an associated energy supplying utility. The current state of the associated energy supplying utility being indicative of a peak demand period or an off-peak demand period. The method also includes a step of analyzing operating conditions of the dryer appliance following receiving the demand-side management signal. The method further includes a step of directing a suspension sequence following analyzing operating conditions of the dryer appliance.

Live and historical power usage data of one or more loads of a facility may be provided to an AI / ML (Artificial Intelligence / Machine Learning) model. The AI / ML model predicts one or more predicted power usage peaks that are predicted to occur during a future time window based at least in part on the live and historical power usage data of the one or more loads of the facility. One or more loads of the facility are identified that are predicted to contribute to each of the one or more predicted power usage peaks. One or more of the loads of the facility that are predicted to contribute to a selected one of the one or more predicted power usage peaks are controlled to curtail power usage during the selected one of the one or more predicted power usage peaks.

Carbon emission reduction using machine learning is provided. A system receives data for profiles linked with locations of an entity. The system determines, based at least in part on data from a utility service provider system and the data indicative of energy consumption associated with the locations, a first value of a metric indicative of carbon emissions associated with the locations. The system determines, based on a comparison of the first value of the metric with a threshold, to invoke an automated process via the payroll processing system to reduce the metric. The system generates, generate, using one or more models trained with machine learning, an action to execute via the automated process. The system selects a first profile from that is compatible with the action, and commands the payroll processing system to execute the generated action to cause a reduction in the first value of the metric.

The invention discloses a double-layer non-cooperative demand response method based on asynchronous multi-agent reinforcement learning, and the method comprises the steps: S1, constructing a double-layer non-cooperative game framework: constructing a double-layer dynamic game model between a public utility company and a consumer; s2, completing modeling of the partially observable Markov game, and converting the double-layer non-cooperative game into the partially observable Markov game; s3, designing an asynchronous multi-agent reinforcement learning algorithm as a game equilibrium solving engine; and S4, implementing centralized training and priority experience playback to realize asynchronous multi-agent reinforcement learning algorithm efficiency optimization. The method is suitable for a dynamic game scene between a public utility company and a consumer group in an intelligent power grid environment, and can realize efficient consumption and load fluctuation suppression of distributed energy in a power grid.

A building energy system includes equipment configured to consume, store, or discharge one or more energy resources purchased from a utility supplier. At least one of the energy resources is subject to a demand charge. The system further includes a controller configured to determine an optimal allocation of the energy resources across the equipment over a demand charge period. The controller includes a stochastic optimizer configured to obtain representative loads and rates for the building or campus for each of a plurality of scenarios, generate a first objective function comprising a cost of purchasing the energy resources over a portion of the demand charge period, and perform a first optimization to determine a peak demand target for the optimal allocation of the energy resources. The peak demand target minimizes a risk attribute of the first objective function over the plurality of the scenarios.

Examples described herein provide a system for controlling power flow in an energy distribution network. The system includes a plurality of inverters, each having an inverter controller associated therewith. The system further includes a utility controller associated with a utility and configured to communicate with at least a subset of the plurality of inverters. The system further includes a customer controller associated with a customer of the utility and configured to communicate with at least the utility controller. The system further includes a direct current (DC) breaker associated with a battery of the customer. The system further includes a DC meter of the customer, the DC meter and the DC breaker each configured to communicate with the utility controller and the customer controller. The utility controller is configured to determine a current operational scenario of the energy distribution network and to control power routing within the energy distribution network.

An electrical distribution grid energy management and router device, or GER device, may be installed in a distribution grid, and route power from power supply to one or more power consumers. The GER devices described herein may provide platforms to add one or more features to a distribution transformer, provide additional features and benefits to both the utility company and end consumer, and may serve as a platform for providing other features, such as multi-layer protection systems for detecting, among other things, one or more fault conditions, and responding to such conditions when detected. A GER device may include sensors to measure electrical properties of incoming and outgoing power, and may include an electrical circuit layer having a central DC power stage.

The utility model relates to converter technology and can be used to efficiently extract the power from solar panels, convert and transmit the obtained energy to the industrial grid, and service the load to consumers. A power supply apparatus based on solar batteries, comprising the following components connected to each other: a solar panel unit, a current and voltage sensor unit of solar panel unit, a DC / DC voltage converter, a DC bus, an inverter, a current and voltage sensor unit at the converter's output, a DC / DC converter control unit, an inverter control unit, a control and switching microcontroller, a current and voltage sensor unit of DC bus, an LC filter and a radio interference filter, a current and voltage sensor unit for the load, wherein the apparatus additionally includes a voltage pulse unit installed between the solar panel and the DC / DC converter. The technical result achieved by using the proposed apparatus is more energy obtained from the solar panel unit, improved reliability of power supply to the consumers.

The present invention provides a system designed to prevent overload and blackout in utility distribution transformers within a local power grid. It includes a utility distribution transformer, users, and a local storage battery, all coordinated through utility load balancing. A temperature-sensing module monitors real-time heat variations, while an electrical output-sensing module tracks and calculates demand. Data from these modules is stored in a database for historical and real-time analysis. An integrated analysis module processes this data to detect decreases in transformer output power. Upon detection, the system discharges energy from the local storage battery to balance the load. Additionally, if a decrease in output power occurs alongside rising temperature, the system alerts the utility service provider to address potential issues and prevent failures. A communication module ensures seamless interaction between the inverter, utility service provider, and end-user via a user-application interface, enhancing coordination and reliability across the grid.

A system receives and automatically transforms utility pipe attribute data and pipe break data. The missing and / or incorrect entries in the pipe attributes and / or break 5 data is automatically identified and correct values for these entries are is automatically imputed to generate improved datasets of the pipe attribute data and break data. The improved data can be used to build a model with machine learning. Predictions of future likelihood of failure for pipe sections in a network of pipes can be made based on the model. A national database can be created that is filled with environmental data that has been transformed, optimized, merged, and imputed. The national database can be used for many customers to save computational costs. The national database can be used to build the failure prediction model for utility companies thereby saving computational costs.

Approaches for managing utility bills and optimizing utility consumption in industrial environments are described. A utility bill resolution system automates extraction, parsing, analysis, and resolution of utility bills from utility providers. The system detects anomalies within bills using machine learning models and verifies bill authenticity using real-time consumption data. A utility consumption optimization system analyzes historical consumption data and utility rate information to generate optimized operation schedules for utility-intensive equipment and processes. The optimization system considers time-of-day pricing structures and implements peak shaving strategies to reduce costs. Both systems leverage advanced technologies including optical character recognition, natural language processing, and Internet of Things (IoT) devices to enhance accuracy and efficiency. The integrated approach enables industrial consumers to ensure billing accuracy, proactively optimize utility consumption patterns, and achieve significant cost savings while improving operational efficiency across multiple utility consumption sites.

The automated system provides automated and remote monitoring, control, communication, and management of recreational vehicle parking facilities, providing guests access to electric, water and sewer services via onsite hookup pedestals. Guests use a computerized reservation system to reserve a parking site. The reservation information is provided to the control system for the hookup pedestals. The control system monitors parking site sensors and activates and deactivates the hookup pedestals via smart switches based on the reservation information and sensor data indicating an arrival and departure, respectively. When a reservation window ends or the site is vacated, the system deactivates the hookup pedestals to end access to the utility services while the sensors remain active. When sites are vacated early, the system determines whether there is sufficient time until the next reservation include the sites' open time to the reservation system's inventory of open sites that are available for new reservations.

Methods and systems for curtailing an electrical load presented by an EV charging station having one or more EV charging ports. The methods and systems may include receiving a curtailment instruction and curtailing energy consumption of an EV charging station by curtailing each of one or more of the EV charging ports of the EV charging station by a corresponding EV charging port curtailment amount. The EV charging port curtailment amount for each of one or more selected EV charging ports may be dependent on a state of charge of the EV that is receiving energy from the corresponding EV charging port. The curtailing of power and / or energy output at an EV charging station may reduce utility tariffs applied by a utility to the facility.

Disclosed is a system comprising one or more smart utilities and a personal smart device, the personal smart device being configured to: communicate with the one or more smart utilities via one or more networks; instruct the one or more smart utilities to return self-identification information; and instruct one or more of the one or more smart utilities to activate identification beacons individually or as a sub-group.

According to an embodiment, disclosed is a system comprising an energy management module, electrically coupled to a utility meter, energy consuming devices and energy producing devices, comprising, a communication module configured to receive input energy data from utility meter, and energy information of energy consuming devices and energy producing devices; a processor storing instructions in a non-transitory memory that, when executed, cause the processor to: determine energy consumption data of the devices; identify a first device based on a first set of predefined priority values which requires energy; identify a second device based on a second set of predefined priority values which has excess energy; establish a connection and control energy transfer from the second device to the first device based on the energy consumption data and the input energy data to enable smart scheduling and power optimization among the energy consuming devices and the energy producing devices.

A system for processing utility bills having distinct formats is provided. The system comprises at least one processor. The processor obtains a plurality of utility bills comprising at least two utility bills of distinct formats and generates bill data based on the plurality of utility bills. The bill data includes a plurality of fields and corresponding field values for each bill. The processor classifies the field values into a set of predetermined uniform categories based on a field-category mapping to obtain uniform classified data. The processor computes one or more utility metrics for each utility bill. The processor generates utility reports for the utility bills based on the uniform classified data and the utility metrics, where each is of a predefined standard template and includes the utility metrics and uniform classified data. The processor provides the utility reports in the predefined standard template for display on a user interface.

A utilities box, particularly a box for enclosing a gas meter, comprises a cavity, surrounded by walls 6, 8, 10,12 and a door 14 which may be opened for user access to the interior of the utilities bo

Controlling provision of a set of utilities by a financial institution. The method includes receiving, by a processor of a financial institution computing system associated with a financial institution, utility usage information from a set of utility meters associated with a plurality of customers of the utility company. The method includes determining a utility account balance for a customer of the plurality of customers based at least in part by an aggregate amount of utility usage used by the plurality of customers for a billing cycle by aggregating the received utility usage information for the billing cycle. The method includes determining that the utility account balance exceeds a threshold permitted past due amount. The method includes sending a stop signal to a utility meter associated with the customer, the stop signal instructing the utility meter to cut off provision of the utility to a property serviced by the utility meter.

The disclosure includes methods, systems and apparatus for predictive management of efficient selecting and receiving of retail electric utility service to a facility for a period, by automated selecting of a retail utility service provider corresponding to a selected least cost path of predicted rate plan choices across the period, wherein costs of all possible, viable time-bounded predicted rate plan choices are determined for predicted consumer usage where a predicted market of retail rate formulas for the period are predicted in relation to at least one variable, such as weather.

A Consumer Access Device (CAD) (610) is arranged to obtain utility (608) consumption data from, for example, a smart meter (not shown). The CAD (610) is also programmed to link to further CADs (612). (614) located at other premises and the linking process may be managed by a utility supplier server (618). Once linked, the CADs may communicate via the server or via a peer-to-peer arrangement. Applications executed on the CADs allow consumers to interact with other households over their energy usage—for example to compete over reducing such usage. In a preferred embodiment, the CAD may be programmed to download applications from an online store.

To provide a method and system for improving the reliability of a utility system asset.SOLUTION: The method includes receiving a time series signal collected from a sensor in a utility system asset, using an inference model to generate an estimated value of the time series signal, performing a pairwise difference operation between the actual value and the estimated value of the time series signal to generate a residual, and performing a sequential probability ratio test (SPRT) on the residuals to generate an SPRT alarm, applying an irrelevance filter that removes SPRT alarm for a signal that does not correlate with previous failures of similar utility system assets to the SPRT alarm to generate a filtered SPRT alarm, using a logistic regression model to calculate an RUL-based risk indicator for the utility system asset on the basis of the filtered SPRT alarm, and generating a notification indicating that the utility system asset needs to be replaced when the risk indicator exceeds a threshold value.SELECTED DRAWING: Figure 2

The present disclosure provides a method for predicting coincident peak events in electricity demand. The method includes receiving historical electrical usage data and forecasted demand data for a given timeframe, training a simulation engine based on the historical data, generating a plurality of load scenarios for a target period of time using the trained simulation engine, and computing a probability that the target period of time will include a new record high peak in electricity demand based on the generated scenarios. The method enables accurate prediction of peak demand events without relying on weather or economic data, allowing utilities and consumers to better manage electricity usage and reduce costs associated with peak demand periods.

A gas line size gauge device is provided. The device is designed to assist first responders, utility workers, and contractors in accurately identifying natural gas pipe sizes during field operations. The device comprises multiple pipe sections corresponding to common industry-standard diameters. A connecting member secures the sections while allowing individual sections to be rotated upwards or downwards for direct size comparison. Each section includes permanent markings, which may be embossed, laser-etched, or phosphorescent for enhanced visibility. Color differentiation between the sections may further aid in quick identification. The device also features attachment points for securing to tool belts or emergency kits. A method of using the device involves extending individual sections to compare with a gas pipe, repeating the process until a match is found, and referencing the corresponding marking for verification.

An energy operation support device includes: an optimization calculation unit; and operation guideline transmission units. The calculation unit sets an operation condition of an energy facility as a determination variable based on information obtained from a result value / prediction value acquisition unit, unit price information acquisition unit, and functioning information acquisition unit, constitutes a balance condition of an energy utility, and adds a shortage amount determination variable of the energy utility to the generation variable side of the balance condition and establishes a constraint condition. Then, the unit sets an objective function containing the total cost for the energy operation of a steel works and the shortage amount determination variable and arithmetically operates the determination variable so the objective function asymptotically approaches the optimum value to satisfy the constraint condition. The transmission units transmit an operation guideline to an operator based on the shortage amount arithmetically operated in the calculation unit.

An optimized, energy-sensitive system and method for fulfilling various data needs of gas utilities. In particular, the disclosure relates to optimized methods for discovering and reacting to an alarm. In one aspect, when an alarm is triggered a telemetry unit automatically alters its operating mode to begin transmitting live pressure reads every minute instead of every hour, as typically performed. This variance in operation eliminates the need for a user to monitor a pipeline system for an incoming alarm and manually request data by initiating a live communication session.

A method may include receiving production data and facility data associated with an enterprise, such that the production data includes operational tasks performed in a hydrocarbon production system, and the facility data includes one or more utility operations within one or more buildings associated with the enterprise. The method may also include receiving sustainability parameter data associated with one or more operations corresponding to the enterprise based on the production data and the facility data, generating a sustainability report representative of sustainability parameters associated with the operations of the enterprise based on the sustainability parameter data, and sending the sustainability report to one or more engineering workflow systems to determine action plans associated with improving the sustainability parameters. After receiving the action plans, the method includes sending commands to cause devices to adjust their respective operations.

Distributed energy resources may be outside the control of a utility. While these distributed energy resources may be managed by distributed energy resource (DER) management systems, the DER management systems do not have knowledge of the overall power network to which the distributed energy resources are connected and do not have the same objective as the utility. Consequently, operation of the distributed energy resources by the DER management systems may result in network violations. Accordingly, disclosed embodiments integrate knowledge from the management system of the utility into the DER management systems using probabilistic optimal power flow to generate operating ranges that may be used in the DER dispatch of each DER management system. These operating ranges may be used to constrain the DER dispatch, to thereby ensure secure and safe operation of the overall power network, while requiring little to no modifications in the DER management systems.

A system for generating a preventative maintenance change request (PMCR) that includes a remote utility controller that receives sensor data of a component of a utility asset generates an alert in response to a trend of the sensor data. The system further includes a data cluster analyzer that receives the alert and identifies a group of components similarly situated to the component by matching clustering data of the component with clustering data of the group of components. The data cluster analyzer generates a preventative maintenance opportunity that recommends changing preventative maintenance schedules for the group of components. The system also includes a data assimilator that assimilates the preventative maintenance schedule for the group of components based on assimilation data including failure history of each component of the group of components. Furthermore, the data assimilator generates the PMCR including the assimilated preventative maintenance schedule for the group of components.

Systems and methods are provided for allocating and providing power to electric loads based on load classification information. A power system, such as a local electrical utility, may receive load demand requests from customers or loads wanting to consume power, and the system may allocate power to the loads based on load classifications of each of the loads. The system may then communicate the power allocations to the loads, and the loads may then consume power based on the power allocated to each of the loads. These improvements may be used to, for example, reduce the likelihood or frequency of demand response events in which power demand exceeds supply since the system may limit the total amount of power that is allocated at any given time.