Secure Power Forecasting and Grid Resilience Enhancement Using a Hybrid SVM-RNN Model

To improve grid resilience and power security with predictive analytics, the study introduces the fusion ability of machine models called, Support Vector Machines (SVM) with Recurrent Neural Networks (RNN). This study addresses the immediate need for exact and effective control of the supply of electricity by focusing on secure data-driven power forecasting for distribution networks and Photovoltaic (PV) facilities. The suggested SVM-RNN hybrid model aims to increase the effectiveness of grid operations by using the combined strengths of SVM and RNN. Deep neural network is used of predicting attacks in the grid network. To help with dimensionality reduction and pattern recognition, the SVM helps to extracts relevant features from the huge amounts of data produced by PV plants and distribution networks. At the same time, the RNN component allows for dynamic forecasting of power output and grid behavior by capturing temporal dependencies and sequential patterns in the data. The effectiveness of the SVM-RNN model has been confirmed by thorough testing and assessment, using performance measures like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The outcomes show better resilience and prediction accuracy when compared with traditional methods, particularly over longer prediction horizons. This innovative approach allows advanced planning and efficient decision-making to reduce possible disturbances and increase resource allocation by providing grid operators with access to advanced forecasting technologies. Overall, combining SVM and RNN in predictive analytics presents a suitable way to improve grid resilience and guarantee a steady supply of electricity over changing energy markets and environmental issues.