Energy-Grid-Optimizer Model Utilizing EfficientNet-Inception Framework for Smart Energy Systems and Grid Optimization

Indeed, deeply advanced learning for data scientists has been applied to previously energy optimized smart grids through Deep learning and Deep Reinforcement learning techniques. In particular, emerging and gaining ground for smart grids is the use of deep neural network based reinforcement learning approaches. For discrete and continuous action space we use the deep reinforcement learning algorithms. We were able to construct these algorithms into a robust real physical smart grid optimizer with the use of the MATLAB environment. It was observed that the agent could correctly identify the features in the training data related to energy supply and demand and ‘learn’ to make actions that will get it the most rewards. In the scope of our multilevel, systems based method of smart grid modelling, EGO-ENIF model and traditional methods are deployed in tandem for the sake of improvement at different levels. Besides improving the predictive accuracy of the energy consumption patterns, this approach enables energy distribution decision making to be made in real time. With the help of advanced machine learning concept and the established model, we can achieve a more stable and efficient smart grid based on resource optimization and conditions adjustment.