FlexiGrid AI: A Deep Learning and Stochastic Simulation Framework for Dynamic Energy Pricing, Load Forecasting, and Adaptive Storage Management

The ever-expanding complexity of the energy grids and the increasing energy demand due to evolving technology for improved lifestyle bring about major issues in meeting specific electricity demands, where one could desire to store unused energy for use during peak demand hours. In this research, we introduce FlexiGrid AI: a potential Deep Learning and Stochastic Simulation Framework capable of improving dynamic energy pricing strategies, load forecasting, and energy storage. The framework has eight subsystems that altogether employ weather data, load data, renewable energy generation data, and electricity pricing data as multi-source datasets to provide a one-stop solution to energy/electricity crises, that is crucial in many parts of the world. A Long Short-Term Memory (LSTM) network is used for precise energy demand and generation prediction, and stochastic methods are used to analyze the possibility of using adaptive storage management under uncertainty. The evaluation metrics included load forecast RMSE at around 0.391 and correlation graphs and histograms which reflected the model's high accuracy in prediction. Storage optimization could help to avoid grid overload during high consumer demand by 22%, while dynamic pricing could lead to 15% of load power peak consumption.