| Issue |
E3S Web Conf.
Volume 387, 2023
International Conference on Smart Engineering for Renewable Energy Technologies (ICSERET-2023)
|
|
|---|---|---|
| Article Number | 04005 | |
| Number of page(s) | 9 | |
| Section | Sustainable Development | |
| DOI | https://doi.org/10.1051/e3sconf/202338704005 | |
| Published online | 15 May 2023 | |
Design and Implementation of a Smart Home Energy Management System Using IoT and Machine Learning
1 New Prince Shri Bhavani College Of Engineering and Technology, Approved by AICTE, India
2 Sri Sankara Arts and Science College, Enathur, kanchipuram, India
3 Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
4 Assistant Professor, Prince Dr. K. Vasudevan College of Engineering and Technology, Chennai – 127
* Corresponding author: sivakumar.c@vidyanikethan.edu
The paper “Design and Implementation of a Smart Home Energy Management System Using IoT and Machine Learning” proposes a system that aims to optimize energy consumption in a smart home environment. The system uses Internet of Things (IoT) devices to collect real-time data on energy usage and machine learning algorithms to predict future consumption patterns. This paper proposes the use of deep neural networks (DNNs) for the design and implementation of a smart home energy management system using IoT and machine learning techniques. The authors demonstrate the effectiveness of the system through experimental results, showing significant energy savings compared to traditional methods. The DNN is built using Keras or Tensor Flow and is trained on extracted features from energy consumption data collected using IoT sensors. The system is implemented with a real-time monitoring system and a user interface for remote access. The proposed system has the potential to save energy and reduce energy costs for households while providing real-time feedback to the user.
Key words: Internet of Things (IoT) / Machine Learning / Smart Home / Energy Management
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.