Large Scale Predictive Analytics based Real-Time Energy Management and Enhance Power Quality in Smart Grid

Energy use has increased dramatically in recent years. As the world’s population grows, so does the need for electricity. The LSTM Algorithm is used to anticipate energy management and manage real-time energy in this paper and ANF control algorithm based smart grid to solve power quality issues is investigated in this paper. To maximize both power generations, back-to-back voltage source converters (VSC) are operated with an intelligent ANN controller. Optimal power converters are adopted this proposed methodology and improved overall performance of the system in a acceptable limit. The simulation results are obtained for different mode of smart grid and non-linear fault condition and proven proposed control algorithm works well. The information is presented as data sets. It has been discovered that a significant quantity of energy will be required in the future to train data, depending on the amount of energy consumed in the future. It has the ability to track training progress in real time. In order to forecast future events, this article used 440 daily energy consumption estimates and 250 iterations. MATLAB/Simulink is used to validate the results.