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

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 8; pp. 5698 - 5704
• Main Authors: Fang, Xia, Zhang, Wei, Guo, Yuhao, Wang, Jie, Wang, Mei, Li, Shunlei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Deep learning (DL); Energy management; energy management (EM); Forecasting; Forecasting techniques; Hankel matrices; Load forecasting; load forecasting (LF); Load modeling; Machine learning; Mathematical models; Optimization; Prediction algorithms; Recurrent neural networks; smart grid (SG); Training; Uncertainty
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3136562

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.