Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Deep Learning-Based Energy Demand Prediction and Energy-Efficient Routing in IoT-Enabled Smart Grids

Authors
A. Hemantha Kumar1, P. Vasuki1, *
1Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India
*Corresponding author. Email: vasakime@gmail.com
Corresponding Author
P. Vasuki
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_35How to use a DOI?
Keywords
Energy Forecasting; LSTM; IoT Smart Grid; Efficient Routing; Deep Learning Introduction
Abstract

The rising necessity for intelligent and sustainable power management in modern electricity networks has led to the evolution of smart grids, supported by sophisticated data analytics and communication frameworks. This research introduces a deep learning-based method designed to anticipate energy requirements within IoT-enabled smart grids and to enhance the routing of data for greater energy conservation. Leveraging Long Short-Term Memory (LSTM) neural networks, the system forecasts energy usage trends using historical records from IoT devices and smart meters. Additionally, a dynamic, energy-aware routing strategy is implemented, which modifies transmission paths in real time based on the predicted energy consumption, thereby reducing energy use during data transfer. The approach is validated using real-world smart grid datasets through simulation, showing a marked reduction in transmission energy costs and overall grid enhancement. The findings emphasize that combining deep learning-based forecasting with adaptive routing can considerably boost efficiency, lower expenses, and increase the operational reliability of IoT-integrated smart grid systems.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_35How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - A. Hemantha Kumar
AU  - P. Vasuki
PY  - 2025
DA  - 2025/10/31
TI  - Deep Learning-Based Energy Demand Prediction and Energy-Efficient Routing in IoT-Enabled Smart Grids
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 420
EP  - 430
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_35
DO  - 10.2991/978-94-6463-866-0_35
ID  - Kumar2025
ER  -