Abstract: Systems and methods for dynamic management of energy usage in local grids are provided. An example method is performed by a load distribution controller coupled to a power source and an energy storage device configured to accumulate power. The method includes acquiring reference levels of using energy of the power source at time slots within a time period and cost values corresponding to the reference levels, dynamically determining, based on the reference levels and the cost values, a schedule indicating charge levels of the energy storage device at the time slots within the time period, and configuring the energy storage device to charge or discharge the power the power according to the schedule. The reference levels are obtained using the Viterbi algorithm based on costs of the energy of power source at past times slots of a past time period and rates of charging and discharging the energy storage device.

Abstract: Systems and methods for interval energy disaggregation utilizing machine learning are provided. An example method includes obtaining and cleaning weather data and customer data, and based on the customer data, determining whether a structure utilizes solar energy. If the structure utilizes solar energy, the overall aggregate data is disaggregated by using and training a first machine learning model, to ultimately produce a predicted solar production. The predicted solar production is extracted from an overall aggregate data of the structure. Then, the method continues with disaggregating the non-solar aggregate data by utilizing at least a second ML model, to produce disaggregated data relating to at least one of AC energy consumption and EV (electric vehicle) consumption. The disaggregated data is provided to the customer or utility. The disaggregated data comprises at least one of the predicted solar production, the AC energy consumption, and the EV consumption of the structure.

Abstract: Active demand management system and methods are disclosed herein. An example method for active demand management includes, facilitating dynamic and adaptive control of energy consumption within a network of devices. The method involves computing a rule set based on certain conditions, assessing the current operational state of devices, and integrating user inputs and preferences. By evaluating forecasted demand against a predetermined threshold, the method generates a control strategy to sequentially deactivate devices in order of prioritized importance, ensuring demand does not surpass the set threshold. Subsequently, energy-related commands are dispatched to the devices to implement the devised plan, optimizing energy distribution and preventing overload, thereby achieving efficient demand management.

Abstract: Active demand management systems and methods are disclosed herein. An example method includes determining one or more conditions necessary to compute a rule set, determining a current state of one or more devices, receiving user inputs and overrides, if any, via the one or more devices, determining both a forecasted demand and a demand threshold, based on the rule set, the current state of each of the one or more devices, and the user inputs and overrides, when the forecasted demand is greater than the demand threshold, generating a plan to power off the one or more networked devices, one by one, in an order from the least important device to the most important device, until the forecasted demand no longer exceeds the demand threshold; and delivering energy-related device commands for the one or more devices, based on the generated plan.

Abstract: Systems and methods for dynamic management of energy usage in local grids are provided. An example method is performed by a load distribution controller coupled to a power source and an energy storage device configured to accumulate power. The method includes acquiring reference levels of using energy of the power source at time slots within a time period and cost values corresponding to the reference levels, dynamically determining, based on the reference levels and the cost values, a schedule indicating charge levels of the energy storage device at the time slots within the time period, and configuring the energy storage device to charge or discharge the power the power according to the schedule. The reference levels are obtained using the Viterbi algorithm based on costs of the energy of power source at past times slots of a past time period and rates of charging and discharging the energy storage device.