Explainable hybrid forecasting model for a 4-node smart grid stability

The 4-Node architecture for smart grids (SGs) distinguishes the operational features to enhance its stability and interruption less power distribution. This architecture contains 1 generator node and 3 consumption nodes to retain the stability of SGs. However, the chances of grid component failure, deficient power distribution, or peak demands result in the 4-node architecture imbalance. To forecast such imbalance towards SG stability, this paper exploits the explainable artificial intelligence (AI) and deep learning models to verify the existence of bias in the 4-node architecture due to the above flaws. For this purpose, in this paper, the component functioning rate and demand-to-distribution ratio are computed and verified to compute the bias. The deep learning model trains the explainable AI model for the potential flaws from the distribution intervals. Using the training using the deep learning model, the 4-node architecture’s performance is evaluated to forecast the grid stability for future distributions. The 4-node architecture stability is verified under different loads, peak demands, and device performances using the integrated hybrid model with EAI. The proposed model improved the forecast accuracy by 14.53 % and reduced the loss in flaw detection by 13.41 % for the varying dissemination hours.