Methods, computer software and apparatus for use in an electric power grid are described. Measurement data is received intermittently or continuously from a plurality of measurement units, indicating values of at least one measured characteristic at respective measurement locations. The at least one is characteristic is monitored, and it is determined, based on the monitoring, that there has been a change in electric power flow in the electric power grid. Responsive to the determination, component data is accessed relating to characteristics of components of the electric power grid involved in provision and/or consumption of electric power. Based on received measurement data relating to the change and the accessed component data, a power flow characteristic of the grid in a given time period is determined.

A power-distribution-system management apparatus according to the present invention includes a communication unit that acquires an amount of insolation, which is a measurement value measured by a pyranometer, via a communication network, which is used to collect a measurement value of a smart meter that measures electric energy, and a meter-data management apparatus and a power-generation-amount estimating unit that estimates, on the basis of the amount of insolation, a power generation amount of each of two or more solar power generation facilities connected to a power distribution line of a high voltage system.

Methods for providing provable access to a distributed ledger with a tokenized instruction set are disclosed. A method may include accessing a distributed ledger including an instruction set, tokenizing the instruction set, interpreting an instruction set access request, and in response to the instruction set access request, providing a provable access to the instruction set.

A predictive controller for a building energy system includes one or more processing circuits configured to obtain a constraint that defines a total electric load to be served by the building energy system at each time step of a time period as a summation of multiple source-specific energy components. The source-specific energy components include a first energy component indicating a first amount of energy to obtain from a first energy source during the time step and a second energy component indicating a second amount of energy to obtain from a second energy source during the time step. The one or more processing circuits are configured to perform a predictive control process subject to the constraint to determine values of the source-specific energy components at each time step of the time period and operate equipment of the building energy system using the values of the source-specific energy components.

Systems and methods for machine forward energy transactions optimization are disclosed. A transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task, a forward resource market circuit to access a forward market for energy, and a controller. The controller may include an artificial intelligence (AI) circuit to configure a transaction on the forward market for energy in response to the aggregated resource requirement and a machine resource acquisition circuit to automatically solicit the configured transaction on the forward market for energy. The AI circuit may also iteratively improve the configured transaction to improve a task outcome of the fleet of machines.

Systems and methods for fleet forward energy and energy credits purchase are disclosed. An example transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task; a forward resource market circuit to access a forward market for energy; and a machine resource acquisition circuit to execute a transaction on the forward market for energy in response to the aggregated resource requirement.

Systems and methods for machine forward energy and energy storage transactions are disclosed. An example transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task, wherein the resource requirement comprises an energy storage capacity requirement, a forward resource market circuit to access a forward market for energy, and a machine resource acquisition circuit to execute a transaction on the forward market for energy in response to the aggregated resource requirement.

A battery management system includes: a battery capacity division section that virtually divides a capacity of a battery mounted on a mobile object into a predetermined number of areas; a capacity change reception section that receives a capacity change request for requesting a change in a capacity of a first area allocated to a first user be changed; a capacity change adjustment section that, when the capacity change request is received, determines whether or not, a capacity of a second area allocated to a second user other than the first user can be changed according to the capacity change request; and an area capacity change section that, when the capacity change adjustment section determines that the capacity of the second area can be changed according to the capacity change request, changes the capacities of the first area and the second area according to the capacity change request.

Certain examples described herein relate to systems and methods for estimating energy consumption data for a building. A system (600) for estimating an energy use of a building uses a dilated convolutional neural network architecture (610) to receive time-series data (620) for the building and to predict one or more time-series data points (650) representing an estimated energy consumption for the building. A method for estimating an energy use of a building includes obtaining time-series data for the building, providing the time-series data as input to a dilated convolutional neural network architecture, and predicting one or more time-series data points representing an estimated energy consumption for the building using the dilated convolutional neural network architecture. The systems and methods may be used to help users and building controllers reduce energy use within a building.

A method for calibrating a unit controller with a managing process determining a position of a decoupler mechanism on the pumping unit. The managing process can calibrate the unit controller with a dual pump mode in response to determining the decoupler mechanism is in a coupled position. The managing process can calibrate the unit controller with a single pump mode in response to determining the decoupler mechanism is in a decoupled position with the pumping unit operating with the first fluid end coupled to the power end and the second fluid end decoupled from the power end. The system controller can pump a wellbore treatment fluid in accordance with the pumping unit in i) the dual pump mode or ii) the single pump mode.