Systems and methods for energy-related load optimization

An energy device control system executing on a distributed grid subsystem operative to control power demand of a plurality of energy devices, the energy device control system comprising:

• at least one processor operatively connected to a memory;
• a graphical user interface, executed by the at least one processor, configured to accept a user input indicative of a first power demand and a dynamic allocation flexibility for the first power demand associated with a plurality of respective energy devices;
• a communication interface, executed by the at least one processor, configured to aggregate dynamic allocation values establishing a projected cost for respective power demand from a plurality of system nodes, the plurality of systems nodes including the plurality of energy devices and at least a plurality of generator nodes configured to supply electricity, the aggregate dynamic allocation values including at least the user input indicative of the first power demand and the dynamic allocation flexibility for the first power demand, and respective user inputs for power demand and dynamic allocation flexibility for the plurality of energy devices;
• and
• wherein the at least one processor is further configured to:
• use an updateable learning model for evaluating dynamic future allocation of required energy with future energy generation prediction, based on receiving and updating historic energy usage and generation over time, forecasting energy demand and generation based on historic information, updating forecasted energy usage and generation against realized energy usage and production over time, and pricing for predicted energy demand against energy pricing markets, wherein the dynamic future allocation includes operations to refine predicted energy demand based on a categorization of energy usage of the plurality of respective energy devices and optimization of energy pricing incorporating user preference specified for potential energy cost savings;
• trigger energy generation on the energy grid at respective generator nodes according to the forecasted energy demand and energy pricing of the updateable learning model and dynamic projections;
• wherein use of the updateable learning model further comprises operations to:
• identify uncategorized energy devices;
• automatically assign a category of energy demand to an energy device without a category of energy demand, the category of energy demand defining any dynamic allocation flexibility for the device;
• and
• identify incomplete information sets for respective energy devices and locality based pricing, and querying the respective energy devices or inferring at least some incomplete information for the respective energy devices and locality based pricing.