Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning

Md Sabbir Hosen; Sidratul Montaha Silmee; Md Shamim; Filbert H. Juwono; Fumiyuki Adachi

doi:10.1109/ICEECIT63698.2024.10859329

Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning

Md Sabbir Hosen^*
, Sidratul Montaha Silmee
, Md Shamim
, Filbert H. Juwono
, Fumiyuki Adachi

^*Corresponding author for this work

Department of Electrical and Electronic Engineering

Beijing University of Posts and Telecommunications
Daffodil International University
Indian Institute of Technology Madras
Tohoku University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

2 Citations (Scopus)

Abstract

Energy efficiency is a critical requirement for nations across the globe. In this connection, Smart Grids (SGs) have become a focal point due to the integration of numerous sensors and modern hardware, including smart devices. This research investigates the optimization of energy efficiency in smart grid systems by enhancing wireless communication through advanced machine learning algorithms. Moreover, this study explores the Home Area Networks (HANs) technologies such as ZigBee, Bluetooth, Wi-Fi, 6LoWPAN, and Z-Wave within the SG context. We propose a model to streamline data transmission, improve reliability, and strengthen security measures. Python-based simulations will be conducted to evaluate the model's efficacy, with results presented through various graphical representations. Through the integration of deep learning model, 99% accuracy, 98% precision and 97% recall was achieved. Preliminary results indicate that the integration of machine learning techniques significantly enhances energy optimization.

Original language	English
Title of host publication	ICEECIT 2024 - Proceedings
Subtitle of host publication	2nd International Conference on Electrical Engineering, Computer and Information Technology 2024
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6-11
Number of pages	6
ISBN (Electronic)	9798331504373
DOIs	https://doi.org/10.1109/ICEECIT63698.2024.10859329
Publication status	Published - 2024
Event	2nd IEEE International Conference on Electrical Engineering, Computer and Information Technology, ICEECIT 2024 - Jember, Indonesia Duration: 22 Nov 2024 → 23 Nov 2024

Publication series

Name	ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024

Conference

Conference	2nd IEEE International Conference on Electrical Engineering, Computer and Information Technology, ICEECIT 2024
Country/Territory	Indonesia
City	Jember
Period	22/11/24 → 23/11/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Data Transmission
Energy Efficiency
Energy Wireless Communication
IEEE 802.11
IoT
Machine Learning
Smart Grids

Access to Document

10.1109/ICEECIT63698.2024.10859329

Cite this

Hosen, M. S., Silmee, S. M., Shamim, M., Juwono, F. H., & Adachi, F. (2024). Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning. In ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024 (pp. 6-11). (ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEECIT63698.2024.10859329

Hosen, Md Sabbir ; Silmee, Sidratul Montaha ; Shamim, Md et al. / Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning. ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 6-11 (ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024).

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title = "Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning",

abstract = "Energy efficiency is a critical requirement for nations across the globe. In this connection, Smart Grids (SGs) have become a focal point due to the integration of numerous sensors and modern hardware, including smart devices. This research investigates the optimization of energy efficiency in smart grid systems by enhancing wireless communication through advanced machine learning algorithms. Moreover, this study explores the Home Area Networks (HANs) technologies such as ZigBee, Bluetooth, Wi-Fi, 6LoWPAN, and Z-Wave within the SG context. We propose a model to streamline data transmission, improve reliability, and strengthen security measures. Python-based simulations will be conducted to evaluate the model's efficacy, with results presented through various graphical representations. Through the integration of deep learning model, 99\% accuracy, 98\% precision and 97\% recall was achieved. Preliminary results indicate that the integration of machine learning techniques significantly enhances energy optimization.",

keywords = "Data Transmission, Energy Efficiency, Energy Wireless Communication, IEEE 802.11, IoT, Machine Learning, Smart Grids",

author = "Hosen, \{Md Sabbir\} and Silmee, \{Sidratul Montaha\} and Md Shamim and Juwono, \{Filbert H.\} and Fumiyuki Adachi",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2nd IEEE International Conference on Electrical Engineering, Computer and Information Technology, ICEECIT 2024 ; Conference date: 22-11-2024 Through 23-11-2024",

year = "2024",

doi = "10.1109/ICEECIT63698.2024.10859329",

language = "English",

series = "ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024",

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Hosen, MS, Silmee, SM, Shamim, M, Juwono, FH & Adachi, F 2024, Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning. in ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024. ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024, Institute of Electrical and Electronics Engineers Inc., pp. 6-11, 2nd IEEE International Conference on Electrical Engineering, Computer and Information Technology, ICEECIT 2024, Jember, Indonesia, 22/11/24. https://doi.org/10.1109/ICEECIT63698.2024.10859329

Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning. / Hosen, Md Sabbir; Silmee, Sidratul Montaha; Shamim, Md et al.
ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 6-11 (ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning

AU - Hosen, Md Sabbir

AU - Silmee, Sidratul Montaha

AU - Shamim, Md

AU - Juwono, Filbert H.

AU - Adachi, Fumiyuki

PY - 2024

Y1 - 2024

N2 - Energy efficiency is a critical requirement for nations across the globe. In this connection, Smart Grids (SGs) have become a focal point due to the integration of numerous sensors and modern hardware, including smart devices. This research investigates the optimization of energy efficiency in smart grid systems by enhancing wireless communication through advanced machine learning algorithms. Moreover, this study explores the Home Area Networks (HANs) technologies such as ZigBee, Bluetooth, Wi-Fi, 6LoWPAN, and Z-Wave within the SG context. We propose a model to streamline data transmission, improve reliability, and strengthen security measures. Python-based simulations will be conducted to evaluate the model's efficacy, with results presented through various graphical representations. Through the integration of deep learning model, 99% accuracy, 98% precision and 97% recall was achieved. Preliminary results indicate that the integration of machine learning techniques significantly enhances energy optimization.

AB - Energy efficiency is a critical requirement for nations across the globe. In this connection, Smart Grids (SGs) have become a focal point due to the integration of numerous sensors and modern hardware, including smart devices. This research investigates the optimization of energy efficiency in smart grid systems by enhancing wireless communication through advanced machine learning algorithms. Moreover, this study explores the Home Area Networks (HANs) technologies such as ZigBee, Bluetooth, Wi-Fi, 6LoWPAN, and Z-Wave within the SG context. We propose a model to streamline data transmission, improve reliability, and strengthen security measures. Python-based simulations will be conducted to evaluate the model's efficacy, with results presented through various graphical representations. Through the integration of deep learning model, 99% accuracy, 98% precision and 97% recall was achieved. Preliminary results indicate that the integration of machine learning techniques significantly enhances energy optimization.

KW - Data Transmission

KW - Energy Efficiency

KW - Energy Wireless Communication

KW - IEEE 802.11

KW - IoT

KW - Machine Learning

KW - Smart Grids

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M3 - Conference Proceeding

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BT - ICEECIT 2024 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Electrical Engineering, Computer and Information Technology, ICEECIT 2024

Y2 - 22 November 2024 through 23 November 2024

ER -

Hosen MS, Silmee SM, Shamim M, Juwono FH, Adachi F. Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning. In ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024. Institute of Electrical and Electronics Engineers Inc. 2024. p. 6-11. (ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024). doi: 10.1109/ICEECIT63698.2024.10859329

Optimizing Energy Efficiency in Smart Grids Through Wireless Communication and Deep Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

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