Micro-Grid Renewable Energy Integration and Operational Optimization for Smart Grid Applications Using a Deep Learning

The conceptual prediction approaches for solar energy and Photovoltaic energy are thoroughly reviewed in this work. Employing enhanced gated recurrent units (GRUs) and recurrent neural networks (RNNs) for both univariate and multivariate cases, this research proposes a unique technique for the forecasting of electrical load for a smart grid. Initially, many delicate tracking variables or previous power usage information are chosen for the source information following the correlation research. Furthermore, a Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU) is built utilizing an enhanced learning algorithm which is premised on Adaptive Gradient and customizable velocity, employing a condensed GRU. The revised training approach and redesigned RNN-GRU architecture increase the effectiveness and durability of learning. Finally, because of its productive learning mechanisms and self-feedback interconnections, the RNN-GRU is employed to create a precise mapping between both the variables examined and Renewable production or power loads. Experimental investigations are used to verify the presented approach: one predicts power requirements utilizing previous information on electricity usage while the other predicts solar power generation utilizing a variety of meteorological characteristics. The empirical outcomes show that the suggested strategy beats cutting-edge deep learning techniques in generating a precise power forecast for an efficient smart grid