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
N1 - Publisher Copyright:
© 2024 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85218133579&partnerID=8YFLogxK
U2 - 10.1109/ICEECIT63698.2024.10859329
DO - 10.1109/ICEECIT63698.2024.10859329
M3 - Conference Proceeding
AN - SCOPUS:85218133579
T3 - ICEECIT 2024 - Proceedings: 2nd International Conference on Electrical Engineering, Computer and Information Technology 2024
SP - 6
EP - 11
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 -
