Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a thermostat-based demand response event. In one aspect, a method includes accessing, for sites, historical readings of HVAC activity, indoor temperature, and outdoor temperature and building a model for each of the sites using the historical readings of HVAC activity, indoor temperature, and outdoor temperature. The method also includes using a simulation engine to achieve a target load shed and load reduction shape for a thermostat-based demand response event, and performing the thermostat-based demand response event based on results of the simulation engine.

A solar system installed at a house or building, which may include solar panels and a solar inverter. When the solar system is installed at the house or building, the power load associated with the solar system might overload an electrical panel. This might force the owner of the house or building to spend thousands of dollars on an electrical panel upgrade. To avoid such an expensive upgrade, a smart load manager (SLM) is disclosed that can communicate with the solar inverter and can control it. The SLM can function as a real-time load shedding device, thereby avoiding the cost of a load center/panel upgrade, while enabling a safe and cost-effective solar system installation.

A method for the reduction in the electricity consumption of an electrical load (2) placed along an electric line (3), said method comprising the steps of: —determining a phase shift existing between the current and voltage along the electric line (3); —implementing the following procedure if said phase shift falls within a predetermined range: i) performing an interruption of current to the electrical load (2) of a duration less than a predetermined value; ii) modifying an electrical characteristic of an additional electrical load (6) as a function of the phase shift; iii) putting or maintaining said additional electrical load (6) in parallel with the electrical load (2).

Systems and methods for managing a home electrical network or system, such as managing loads applied to the network by one or more associated electric vehicles, are described. For example, the systems and methods predict or estimate use of a home electrical network (e.g., via a charging station connected to the network) by one or more electric vehicles, and manage use or operation of other devices on the home network accordingly.

In an electronic apparatus, reference information stored on a memory includes standard appliance ownership information describing owned home appliances of at least one standard household and standard monthly power consumption information of the standard household. A processor obtains first monthly power consumption information of a first household which owns a first home appliance of a same type as a standard home appliance described in the standard appliance ownership information. Based on a difference between the standard and first monthly power consumption information being within a threshold range, the processor compares appliance monthly power consumption information of the standard home appliance and of the first home appliance, and, based on the result being within a threshold range, predicts first appliance ownership information on owned home appliances of the first household based on the standard appliance ownership information. The processor then provides guide information associated with the first appliance ownership information.

A site monitoring method uses an electrical socket device to collect current data of an electrical appliance that is electrically connected to the electrical socket device. Based on the current data, a management server can derive a user behavior in connection with using the electrical appliance, and send out a warning message when the user behavior thus derived is abnormal.

Systems and methods for managing power supplied over an electric power grid by an electric utility and/or other market participants to a multiplicity of grid elements and devices for supply and/or load curtailment as supply, each of which having a supply equivalence value associated with its energy consumption and/or reduction in consumption and/or supply, and wherein messaging is managed through a network by a coordinator using IP messaging for communication with the grid elements and devices, with the energy management system (EMS), and with the utilities, market participants, and/or grid operators.

The invention relates to managing electrical power consumption in the home. Claimed is a system which includes the electric power grid in a home, means for measuring power, and means for controlling load. The home grid is fed by an external grid and is configured in the form of individual lines having individual shutoff devices on supply lines for low priority, medium priority, high priority and extra high priority consumers. The medium priority consumers are connected to a supply line such as to allow phase switching, deactivation and deferral of the operation of a consumer to a different designated time. Sensors for monitoring and transmitting instantaneous current and voltage values to a microcontroller are installed on the supply line of said consumers. A group of high priority consumers is connected to a supply line via a phase switcher, allowing both forward and reverse phase switching. The extra high priority consumers are optionally connected to a guaranteed voltage unit. The microcontroller receives monitoring information about instantaneous current and voltage values, and also receives information about the weather, the microclimate, open doors and windows, movement in the home, the status of a home microgeneration source, and air conditioning, heating and ventilation system parameters.

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.

Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.