A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

A power control device is contained within a housing and has an electric current sensor configured to measure current passing through an electric outlet during a time period, a proximity sensor configured to detect a distance of an object relative to the electric outlet during the time period, a relay switch that can open or close to stop or conduct current through a circuit in the electric outlet in response to a command, and a wireless network interface in communication with the electric current sensor and the proximity sensor, the wireless network interface configured to transmit and receive data from the current sensor and the proximity sensor, to transmit commands to the relay switch, transmit the data to a computing device, and receive commands from the computing device.

The present disclosure provides a computer-implemented method for ranking one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes analysis of a first set of statistical data, a second set of statistical data, a third set of statistical data, a fourth set of statistical data and a fifth set of statistical data. Further, the computer-implemented method includes identification and execution of the one or more control schemes. In addition, the computer-implemented method includes scoring the one or more control schemes by evaluating a probabilistic score. Further, the computer-implemented method includes ranking the one or more control schemes to determine relevant control schemes for controlling real time peak loading conditions and abrupt changes in energy pricing associated with the one or more built environments.

A control system (300) allows recognized standard premise electrical outlets, for example NEMA, CEE and BS, among others to be remotely monitored and/or controlled, for example, to intelligently execute blackouts or brownouts or to otherwise remotely control electrical devices. The system (300) includes a number of smart receptacles (302) that communicate with a local controller (304), e.g., via power lines using the TCP/IP protocol. The local controller (304), in turn, communicates with a remote controller (308) via the internet.

The present disclosure provides an energy transmission method, an energy router and an operation control device thereof, and a storage medium, and relates to the field of energy network technology. The energy router of the present disclosure includes a plurality of ports configured to perform at least one of receiving or sending energy and PLC data, energy transmission switches, each of the energy transmission switches is configured to control open and closed states of one port of, and a routing processor configured to determine an energy transmission path according to the PLC data and stored routing information, determine a corresponding port associated with the energy transmission path, and turn on an energy transmission switch of the corresponding port, for the energy outputting from the corresponding port.

An energy saving support system according to an embodiment is configured to provide a consumer, who has an electric load to which electric energy is supplied from an electric power supply system within a house, with energy consumption-related information through a photo frame or the like. The information providing apparatus is configured to acquire an acceptability level, which stepwise indicates a degree of interest of the consumer in the energy consumption-related information, and to determine the energy consumption-related information to be newly provided to the consumer based on the acceptability level.

An inductive drawing or feeding device for inductively drawing off or feeding in electrical energy/electricity by electric vehicles is connected to a power grid. The inductive drawing or feeding device includes a measuring device configured to measure at least one physical parameter representing the electrical energy inductively transmitted through the associated drawing or feeding device. The inductive drawing or feeding device also includes an identifier for identifying the owner of the connection of the inductive drawing or feeding device. The inductive drawing of feeding device further includes a comparing device configured to compare the at least one physical parameter measured at a connecting side with at least one physical parameter measured at a drawing off or feeding side.

A method of managing electric energy consumption and/or production dynamics, includes the steps of: sampling an electric energy flow, adapted to be measured by an electricity metering device (1) within a network (15) of an electrical company; calculating a variation ΔE.sub.i=(E.sub.i−E.sub.i−1)/Δt at regular intervals, where E.sub.i and E.sub.i−1 are two integral sum values of electric energy amounts consolidated over a given number of cycles and Δt represents a time interval between the respective times at which the two values are obtained; adding the variation (ΔE.sub.i) to analogous variations calculated at previous times, to obtain a cumulative sum of such variations; determining whether the variation and/or the cumulative sum exceed a predetermined threshold value (δ.sub.DE); transmitting a message over the network (15) from the electricity metering device (1) if the variation and/or cumulative sum exceed the predetermined threshold value (δ.sub.DE).

A plurality of storage battery modules include storage battery control devices that can mutually communicate with each other and obtain a demand for electric power in a predetermined consumer in which the plurality of storage battery modules are provided. The storage battery control devices mutually transmit and receive charging/discharging electric power of the storage batteries and control charging/discharging of the plurality of storage battery modules, respectively, on the basis of the demand for electric power in the predetermined consumer.

A management device includes at least one processor communicatively coupled to at least one energy resource controller controlling at least one energy resource and to at least one deferrable load controller controlling power to at least one deferrable load. The is configured to receive an indication, determined based on a frequency value of an electrical network and a nominal frequency value, that a frequency anomaly event has occurred. Responsive to receiving the indication that the frequency anomaly event has occurred, the processor is also configured to determine, for at least one of the energy resource and the deferrable load, based on the frequency value, the nominal frequency value, and a power value of the electrical network, a respective power command, and cause at least one of the at least one energy resource and the at least one deferrable load to modify operation based on the respective power command.