Empowering Grid Stability: An Advanced Hybrid Deep Learning Model for Smart Grids

Amid the growing complexity and uncertainties surrounding power systems due to the incorboration of renewable energy sources and smart grid technologies, this study presents a pioneering solution aimed at enhancing power grid stability. The problem statement delineates the critical necessity for heightened reliability in power systems, emphasizing the need for precise short -term electric load forecasting and daily peak load prediction. The proposed system introduces a sophisticated hybrid deep learning model meticulously crafted to address these challenges. This innovative model intricately amalgamates gradient-boosting based multiple kernel learning, dynamic time warping distance, gated RNNs, and Bayesian deep LSTM neural networks, empowering it to prognosticate residential net load probabilistically. The systematic flow of the proposed system navigates through a holistic architecture leveraging an array of advanced techniques for comprehensive data preprocessing, model training, and forecasting. Commencing with data preprocessing techniques like Ensemble Empirical Mode Decomposition (EEMD) and Bisecting K-Means Algorithm for feature selection, the model progresses to train on Gradient-boosting based multiple kernel learning and employs dynamic time warping (DTW) distance for precise daily peak load s' predictions. Gated Recurrent Neural Networks (RNNs) adeptly capture temporal dependencies, while Bayesian deep LSTM neural networks furnish probabilistic forecasts. The results corroborate the model's exceptional performance, demonstrating the training accuracy of 99.94% and the validation accuracy of 99.13%. Comparative analysis with established methodologies firmly establishes the superiority of the proposed hybrid deep learning model. Its proficiency in accurate load forecasting, provision of probabilistic predictions, and surpassing conventional methods establishes it as a potent solution poised to fortify power grid stability within the evolving landscape of modern smart grids.