A computing device receives a commitment generated by a distributed resource device, the commitment indicating a type of the distributed resource device and a time interval when the distributed resource device modified usage of a resource at a location; receives an event corresponding to a pattern of usage of the resource at the location during the time interval; identifies an event model that is associated with a pattern of usage of the resource that matches the pattern of usage of the resource at the location during the time interval, the event model being included in a library of event models that associate different patterns of usage of the resource with corresponding types of distributed resource devices; and validates the commitment in response to determining that at least a type of distributed resource device associated with the event model corresponds to the type of distributed resource device indicated by the commitment.

A method of managing energy by use of processing logic that comprises a load processor as a cloud service is provided. The method includes receiving power load information from a data collection system located at a building and using a cloud analysis layer that employs machine-learning and artificial intelligence for optimization control, analyzing the received power load information to disaggregate load waveform signals and identify device-based power loads by use of a neural network to perform historical device demand and performance analysis to generate device-based demand forecasting, generating demand forecasts for the building to mitigate peak demand based on analysis of a power draw signal and the generated device-based demand forecasting, and determining whether the generated demand forecast for the building is to peak in a near future, based on threshold values of at least one of generated device-based demand forecasting, power price or cost information, and user behavior analysis.

An article of manufacture, a machine, process for using the articles and machines, processes for making the articles and machines, and products produced by the process of making, along with necessary intermediates, directed to assessing the energy consumption of networks, typically computer networks, and/or applications of assessments made thereby. Industrial applicability is representatively directed to energy consumption/conservation and efficiency, such as in networks, along with control and implementation therefrom, as well as in networking, control systems communications and related systems, receiver systems, and components used in assessing and carrying out the same.

The present disclosure relates to systems, methods, and devices for controlling charging of vehicles, to avoid charging during charge-adverse time periods or during charge restriction events. This can advantageously reduce cost to vehicles owners, and or provide access to reward incentives. Further, power distribution entities (utility providers) advantageously have increased control over power distribution to avoid over-burdening of power distribution infrastructure. Further, systems and methods for determining or inferring whether a vehicle is connected to a charge station are described, which can be used to inform automatic restriction of vehicle charging.

An exemplary display unit is a display unit of a management system that manages energy, wherein the management system has a plurality of setting patterns, and displays a setting screen that switches between the plurality of the setting patterns for each pattern and is capable of displaying a setting pattern of the setting patterns.

Implementations of the disclosed subject matter may provide a method includes determining, at a server, average historical usage of energy by a user based on received energy usage data. The server may determine at least one available energy usage plan from one or more energy providers based on the determined average historical usage of energy and by determining available energy rate structures. The server may determine an optimized energy usage from the one or more energy providers based on the determined at least one available energy usage plan. The method may include controlling, at the server, one or more setting of an energy usage device based on the determined optimized energy usage and a selected energy usage plan from the determined at least one energy usage plan.

An energy savings selector tool may assists a user in determining electrical devices that, when implemented in a load control system, may reduce an amount of power used by the load control system. The energy savings selector tool may use load control information of the load control system to identify electrical devices that may be added to or replace other electrical devices in the load control system. The load control information may define operations of the load control system and/or include energy usage information of the load control system. The energy savings selector tool may identify savings information associated with implementing an electrical device in the load control system. Once an electrical device is installed in the load control system, the energy savings selector tool may be used to report energy savings information about the electrical device.

An energy control system (1) includes a photovoltaic power generation unit (10) that is connected to the grid and generates power using sunlight, a gas power generation unit (20) that generates power using gas, and a control unit (40) that performs control to supply a load by prioritizing the power generated by the gas power generation unit (20) when sale of the power generated by the photovoltaic power generation unit (10) to the grid is possible and to supply the load by prioritizing the power generated by the photovoltaic power generation unit (10) when the sale is not possible.

A method includes establishing a cost for consumption of a given resource by a given resource consumer for a designated period of time, the cost comprising a fixed cost for consumption of the given resource up to a threshold consumption level for the designated time period and an additional cost associated with consumption of the given resource exceeding the threshold consumption level for the designated period of time, monitoring consumption of the given resource by the given resource consumer, determining whether resource consumption by the given resource consumer is projected to exceed the threshold consumption level over the designated time period, generating an alert responsive to determining that resource consumption by the given resource consumer is projected to exceed the threshold consumption level over the designated time period, and providing the alert for presentation via a user interface of a user device associated with the given resource consumer.

A computer-implemented method for aggregating electric vehicle loads for demand response events includes receiving a demand response (DR) event request from a utility system indicative of a DR event for an area. The DR event request includes at least one event parameter for participation in the DR event. The method includes determining a first original equipment manufacturer (OEM) DR event load for the area based on the DR event request and charging data received from electric vehicles associated with a first OEM. Upon determining the first original OEM DR event load does not meet the at least one event parameter, the method includes aggregating charging data from electric vehicles associated with a second OEM with the first OEM DR event load to determine an aggregated DR load for the area.