Revolutionizing data security in healthcare cloud based on novel hybrid evolutionary approach

The financial benefit of a refined, intelligent power system contain rising demand is known as a smart grid (SG). Energy use has increased significantly due to new technology and population growth, creating significant issues for the environment and energy security. Utilizing the Deep Learning (DL) Method in conjunction with power management is important for issue solving. Data is pre-processed using a normalization method after being gathered via a smart city in a power-hungry smart grid. The research used a Discrete Fourier Transform (DFT) to extract features and estimate smart city power usage within a smart grid setting utilizing large-scale, high-dimensional data. The research introduced a unique DL-based, Hybrid Mountain Gazelle Optimized Graph Neural Networks (HMGO-GNN) for real-time forecast and adaptive management of smart grid stability. The suggested technique can stabilize critical machines; predict transient stability, and the stability limit. It can also be used to detect instability during the first swing and multiple times. Experiments conducted on simulation environments illustrate that the intended method has a high precision to predict system stability in real time. Evaluation of performance with the help of essential metrics of evaluation, i.e., F1-score (96%), precision (94%), accuracy (97%), AUC (98%), and recall (95%), presents a holistic understanding of whether or not the model succeeds in forecasting grid instability instances. The cost benefits, decreased energy consumption, and remarkable efforts in decreasing greenhouse gas emissions eventually contribute towards increased environmental sustainability. Additionally, the adaptability and flexibility of the framework in implementation for use in other urban situations are also exemplified. The study accentuates the ability of the framework to enhance efficiency, facilitate sustainability, and provide sophisticated solutions for the current challenges in energy across the globe.