Developing a Predictive Framework for Smart Energy Trading in Interconnected Microgrids

The utilization of renewable resources has frequently been insufficient, the microgrid ecosystem has lacked proper equilibrium, and previous solutions faced challenges in adjusting to variations in energy supply and demand. The application of intelligent modeling for energy forecasting is crucial in tackling these issues. Therefore, the present research created a novel Red fox based Reinforcement Learning Model (RbRLM) was introduced in this present research work to predict the needed trading features. In the initial phase, data related to smart microgrid energy trading was collected and the system was trained. In addition, an assessment of demand levels and established priorities; in this context, stocks are ranked according to market complexity, risk assessments and historical patterns. Consequently, a framework for smart contracts and regulatory-adaptive predictive modeling was established, considering elements such as market complexity, information asymmetry, and regulatory constraints. Ultimately, performance metrics including RMSE, MAE, MSE, MAPE, and R2 were assessed and contrasted with other models. The results of the measurements are as follows: MAE 0.09, RMSE 0.16, MSE 0.08, MAPE 0.05 and R2 value 99.5%. The model's error values are significantly lower than those of the existing forecasting methods.