Document Type
Article
Source of Publication
Discover Sustainability
Publication Date
12-1-2024
Abstract
In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application utilizes Linear Regression as a machine learning (ML) algorithm to develop the electricity consumption forecasting model for the next months, based on past utility bills. Linear regression is often considered one of the most computationally lightweight ML algorithms, making it suitable for smartphones. The application also offers users practical tips for optimizing their electricity consumption habits.
DOI Link
ISSN
Publisher
Springer Science and Business Media LLC
Volume
5
Issue
1
Disciplines
Computer Sciences
Keywords
Deep learning, Electricity consumption, Machine learning, Object detection, Smart application, Sustainability
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Al-Rajab, Murad and Loucif, Samia, "Sustainable EnergySense: a predictive machine learning framework for optimizing residential electricity consumption" (2024). All Works. 6470.
https://zuscholars.zu.ac.ae/works/6470
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series