| Issue |
E3S Web Conf.
Volume 387, 2023
International Conference on Smart Engineering for Renewable Energy Technologies (ICSERET-2023)
|
|
|---|---|---|
| Article Number | 02002 | |
| Number of page(s) | 7 | |
| Section | Energy | |
| DOI | https://doi.org/10.1051/e3sconf/202338702002 | |
| Published online | 15 May 2023 | |
Optimizing Energy Consumption in Smart Homes Using Machine Learning Techniques
1 Government Engineering College, Kisangani, Patna, Bihar, India
2 New Prince Shri Bhavani College Of Engineering and Technology, Approved by AICTE, Affiliated To Anna University
3 Associate 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: javaneeraj@gmail.com
The increasing demand for energy utilization in smart homes has led to the exploration of machine learning techniques as a means to optimize energy consumption. This review paper explores the merits and demerits of using machine learning techniques for energy optimization in smart homes. Smart homes are becoming increasingly popular due to the potential benefits they offer, including increased energy efficiency, improved comfort, and enhanced security. However, to achieve these benefits, it is essential to optimize the energy utilization in smart homes. This paper presents machine learning techniques that have been used to optimize energy utilization in smart homes. In this paper proposed the using Stochastic Gradient Descent (SGD) algorithm for optimizing energy utilization in smart homes. However, challenges such as data privacy, accuracy of data collection, and cost may hinder the full adoption of these techniques.
Key words: Smart homes / Energy consumption / Machine learning / Optimization / Literature review
© 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.
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