A data driven hybrid cnn lstm framework for accurate prediction and energy optimization in microgrids

This paper presents an intelligent data driven method for microgrid energy management using state of the art Deep Learning (DL) models to predict critical control variables like duty cycle, inverter modulation index, reactive power setpoint and DC link voltage. The framework consists of Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN1D) and a hybrid CNN-LSTM network to capture the nonlinear and spatio-temporal characteristics of renewable energy systems. A comprehensive correlation analysis reveals significant relationships between variables, enabling effective modeling of renewable energy generation and storage dynamics. Training outcomes show fast convergence, low mean squared error and low variance for all models, with the hybrid CNN-LSTM architecture showing better stability and generalisation abilities. The evaluated models achieved comparable prediction accuracy, with average R 2 values close to 0.94. The CNN-LSTM model showed stable multi-output prediction behaviour, while the LSTM model achieved slightly better aggregate numerical metrics. Residual and error distribution tests are also performed to find the stability and accuracy of the models. For the microgrid configuration and energy-flow scenario studied, the prediction-assisted dispatch achieves 86.7% renewable penetration and a 90.2% reduction in grid import relative to a fully grid-supplied baseline, corresponding to an approximately 27% improvement in energy saving over a conventional non-predictive rule-based controller applied to the same system. These indicators characterise the studied configuration and sizing rather than establishing the predictive accuracy of the models. The strong negative correlation between renewable energy generation and grid dependency shows the system’s capacity to improve sustainability and reduce grid dependence. In summary, the framework provides a computationally feasible supervisory prediction layer for microgrid energy management. Establishing operational improvement in a deployed system will require closed-loop and hardware-in-the-loop validation, which is identified as future work.