A power generation system is provided. The power generation system includes a power generating asset configured to supply power to a power grid. The power generating asset includes at least one power generating device and at least one energy storage device coupled to the at least one power generating device. The power generation system further includes a controller coupled in communication with the power generating asset. The controller is configured to measure a current power output of the at least one power generating device. The controller is further configured to predict a future power output of the at least one power generating device at a future timepoint. The controller is further configured to determine a target power setpoint based on the current power output and the predicted future power output.

A method of adaptive persistence forecasting includes receiving historical load values for a site with at least one component that consumes energy; receiving historical temperature values corresponding to dates of the historical load values; evaluating the historical load values and the historical temperature values to determine a correlation coefficient; determining that there exists at least a threshold correlation between a load activity and temperature for the historical load values and the historical temperature values based on the correlation coefficient; in response to determining that there exists at least the threshold correlation, normalizing the historical load values based on a set temperature; and applying an adaptive seasonal persistence model to the normalized historical load values to output a forecast for use in controlling energy resources at the site.

Systems and methods for coordinating distributed energy storage in accordance with embodiments of the invention are illustrated. One embodiment includes a power distribution network, including a set of nodes, wherein a node includes a controllable load, an uncontrollable load, and a local controller, a substation connected to each node in the set of nodes by a set of distribution lines, and a global controller including a processor, a memory, and a communications device, wherein the global controller obtains load parameters from at least one node, calculates coordination parameters for each node based on the obtained load parameters, and asynchronously provides the coordination parameters to each of the nodes in the set of nodes, and wherein each node independently obtains coordination parameters, and controls the operation of its controllable load using the local controller based on the coordination parameters and a local load profile describing at least the uncontrollable load.

Systems and methods for improving load energy forecasting in the presence of distributed energy resources in which a revised load forecast is calculated based on forecasted meteorological conditions data, forecasted wind and solar energy, forecasted load data, time data and time-series variables determined based on an analysis of the historical data. In exemplary embodiments, the revised load forecast is provided to energy management computer systems to enable appropriate levels of generation of conventional and renewable energy generation within the electric power grid.

A method, apparatus and system are provided to coordinate, manage, and optimize the electrical loads across all subsystems behind a given meter. In this approach, which is sometimes referred to herein as intelligent demand optimization, the optimized capacity needs of each subsystem are assessed in real-time, along with the available capacity of the meter and the current billing period peak. Power is then distributed dynamically to each subsystem to reduce the overall peak.

Respective energy consumption data is collected via respective agents running on respective host servers. The respective energy consumption data represents energy consumed by the respective host servers over a time period. The respective agents communicate with hardware on each of the respective host servers using a unified application programming interface (API). Respective energy costs are determined over the time period for the respective host servers based on the respective energy consumption data. A subset of the respective host servers that are being underutilized is identified based on the respective energy consumption data and the respective energy costs. An action to take with respect to the subset of the respective host servers that are being underutilized is determined to reduce the energy costs.

A building system for training a prediction model with augmented training data. The building system comprising one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to obtain a first training data set comprising data values associated with a data point of the building system and with a plurality of time-steps and energy values associated with consumption of the building system at each of the plurality of time-steps; generate an augmented training data set comprising a second training data set, the second training data set comprising the energy values and the data values of the first training data set but with a data value replaced with a predetermined value at a time-step of the plurality of time-steps; and generate a prediction model by training the prediction model.

Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a machine learning system is trained on a set of energy intelligence data and deployed on an edge device, wherein the machine learning system is configured to receive additional training by the edge device to improve energy management. In some embodiments, the energy management includes management of generation of energy by a set of distributed energy generation resources, management of storage of energy by a set of distributed energy storage resources management of delivery of energy by a set of distributed energy delivery resources, management of delivery of energy by a set of distributed energy delivery resources, and/or management of consumption of energy by a set of distributed energy consumption resources.

Various embodiments are provided for facilitating the operation and control of a fleet of on-site energy assets and optimizing energy dispatch across the fleet, thereby facilitating the use of the on-site energy assets instead of grid-supplied electric consumption. An example system may comprise a central platform and a plurality of on-site gateway devices configured to perform on-site asset control. An example method may comprise receiving a service availability call, performing fleet-level optimization, generating a set of site-level schedules, and causing, as a function of the site-level schedules, real-time on-site asset control. Other embodiments provide for determining a location of each grid-connected energy consumer at which to reduce grid-supplied energy consumption, determining an amount of a reduction of grid-supplied energy consumption, and transmitting a signal to each corresponding gateway device located at the determined location, the signal comprising data indicative of instructions for performing on-site energy dispatch.

Transaction systems and methods are disclosed. A system may include a controller having a transaction detection circuit to interpret a transaction request value, wherein the transaction request value includes a transaction description for one of a proposed or an imminent transaction, and a cryptocurrency type value and a transaction amount value. A transaction locator circuit then determines a transaction location parameter in response to the transaction request value, wherein the transaction location is a geographic value or a jurisdiction value. A transaction execution circuit then provides a transaction implementation command in response to the transaction location parameter.