A Novel Reinforced Deep RNN–LSTM Algorithm: Energy Management Forecasting Case Study

In this article, a new hybrid deep learning (DL) algorithm is developed to make a computer-assisted forecasting energy management (EM) system. Applying the Copula function, the Hankel matrix is created for processing gathered automatic metering infrastructure (AMI) load information in the smart network. This processing of the data results in model optimization through the suggested new pooling-based deep neural network (PDNN). Through increased size and variation of AMI data, the suggested PDNN reduces overfitting issues during testing and training. The real-time AMI southern grid data of Tamil Nadu electricity is used as the benchmark. The suggested DL model performs better than the traditional EM forecasting techniques in both mean absolute error and accuracy by 12.7% and 9.5%, respectively.