A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups

A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups

Deep learning-based models are ideally suited for accurately predicting electrical load in a smart grid. However, the computational overhead in training models and identifying optimal training hyperparameters are challenging problems. Two important challenges are addressed in this study. Firstly, a...

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Bibliographic Details
Main Authors: A. Jayanth Balaji, Binoy B. Nair, D. S. Harish Ram, Kuruvachan Kalluvelil George
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Convolutional neural networks
deep learning
gated recurrent units
long-short term memory
mutual information
smart grid
Online Access:https://ieeexplore.ieee.org/document/10835081/
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