  In this research, an effort is made to address microgrid systems' operational
challenges, characterized by power oscillations that eventually contribute to
grid instability. An integrated strategy is proposed, leveraging the strengths
of convolutional and Gated Recurrent Unit (GRU) layers. This approach is aimed
at effectively extracting temporal data from energy datasets to improve the
precision of microgrid behavior forecasts. Additionally, an attention layer is
employed to underscore significant features within the time-series data,
optimizing the forecasting process. The framework is anchored by a Multi-Layer
Perceptron (MLP) model, which is tasked with comprehensive load forecasting and
the identification of abnormal grid behaviors. Our methodology underwent
rigorous evaluation using the Micro-grid Tariff Assessment Tool dataset, with
Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient
of determination (r2-score) serving as the primary metrics. The approach
demonstrated exemplary performance, evidenced by a MAE of 0.39, RMSE of 0.28,
and an r2-score of 98.89\% in load forecasting, along with near-perfect zero
state prediction accuracy (approximately 99.9\%). Significantly outperforming
conventional machine learning models such as support vector regression and
random forest regression, our model's streamlined architecture is particularly
suitable for real-time applications, thereby facilitating more effective and
reliable microgrid management.
