System and method for an AI enhanced smart grid framework with optimized incentive capabilities

A system for an AI enhanced smart grid framework with optimized incentive capabilities, comprising:

• a computing device comprising at least a memory and a processor;
• a plurality of programming instructions that, when operating on the processor, cause the computing device to:
• collect a plurality of energy consumption data from a plurality of smart meters and Internet of Things (IoT) devices;
• train an artificial intelligence network using the plurality of energy consumption data, wherein the artificial intelligence network comprises a convolutional neural network (CNN) having:
• input layers that receive the energy consumption data;
• a plurality of convolutional layers that apply learnable filters to extract spatial dependencies in energy consumption patterns;
• pooling layers that downsample feature maps while retaining significant features;
• and
• fully connected layers that learn non-linear combinations of the extracted features;
• generate a plurality of tailored energy plans which are based on particular properties energy consumption using the artificial intelligence network, wherein each tailored energy plan includes specific retrofit recommendations and operational optimizations based on property-specific characteristics identified by the CNN;
• compute an optimized incentive based on the tailored energy plan using a trained interest optimizer, wherein the trained interest optimizer employs machine learning algorithms to calculate personalized interest rate incentives that balance financial risks and rewards between property owners and financing institutions;
• and
• implement the tailored energy plans through a dispatch network, wherein the dispatch network:
• identifies and prioritizes optimal dispatch times based on the tailored energy plans and a plurality of dispatch data;
• communicates with smart switches via a gateway to directly control energy-consuming devices according to the tailored energy plans;
• and
• dynamically adjusts control based on a direct load control variable that determines the authority to control energy loads based on grid conditions.