Predictive Resilience Framework for Energy Management Systems in Next-Generation Smart Grids Using a Hybrid LSTM-ARIMA Model

The Predictive Resilience Framework for Energy Management Systems in Next-Generation Smart Grids integrates advanced forecasting and modeling techniques to enhance grid stability and efficiency. This framework uses Long Short Term Memory (LSTM) network with AutoRegressive Integrated Moving Average (ARIMA) models as the backbone to make the accurate predictions and understanding the dynamics of the energy system. The use of ARIMA befits a number of time series forecasting requirements, which consists of identifying patterns based on past data, and mitigating non-stationary beliefs as load forecasting, renewable integration, fault identification, and demand response. On the other hand, LSTM networks are effective at modelling the long-term dependencies in the sequences hence enhancing the way the model produces future state from previous sequences. The hybrid LSTM-ARIMA approach merges the strengths of both models: ARIMA gives a stable framework on which to build the prediction and LSTM gives an improvement on these predictions due to its ability to deal with complex patterns and temporal attributes. This combination also helps fundamental predictive resilience by allowing for prevision of both energy demand and renewable production, standout of system abnormalities, and direction of peak loads and response programmes. Furthermore, the framework helps in analysing the ability of the grid to operate under various stress conditions such as weather conditions and cyber attacks. By including such superior modelling approaches the framework has been designed to enhance the utility of the grid, balance the energy provision and strengthen the general resource of the system to new threats and instability within the future and more complex smart grid systems.