Empowering Energy Efficiency: A Real-Time Mobile Analytics Platform For Intelligent Consumption Monitoring

The increasing demand for sustainable energy solutions calls for innovative tools that empower both consumers and providers to optimize energy use. This research presents the development of a novel mobile application for real-time energy consumption monitoring, designed to enhance user awareness and enable data-driven decision-making. By leveraging cutting-edge mobile and cloud technologies, the system integrates seamlessly with smart IoT devices to collect, process, and analyze energy consumption data. The application features an intuitive interface that visualizes usage patterns through dynamic charts, predictive statistics, and anomaly detection algorithms. It provides actionable insights for users to identify inefficiencies, adapt behaviors, and achieve long-term energy savings. Additionally, energy providers gain unprecedented visibility into network usage, unlocking opportunities for optimizing electricity production and distribution. Built upon a robust architecture combining a Spring Boot backend and an Android-based frontend, the system ensures reliable synchronization, secure data handling, and responsive user interactions. The findings demonstrate the potential of integrating real-time analytics with mobile technology to address critical energy challenges while fostering sustainability and efficiency across diverse sectors.

Authors

Publication Details

Journal
International Journal on Science and Technology
Published
2025-05-17
DOI
https://doi.org/10.71097/ijsat.v16.i2.3486
Citations
3
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
2.19
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Empowering Energy Efficiency: A Real-Time Mobile Analytics Platform For Intelligent Consumption Monitoring

Oyeronke Ladapo -
3 citations
International Journal on Science and Technology
Smart Grid Energy Management
2.19
article

Empowering Energy Efficiency: A Real-Time Mobile Analytics Platform For Intelligent Consumption Monitoring

Oyeronke Ladapo -
article en
3 citations

Abstract

The increasing demand for sustainable energy solutions calls for innovative tools that empower both consumers and providers to optimize energy use. This research presents the development of a novel mobile application for real-time energy consumption monitoring, designed to enhance user awareness and enable data-driven decision-making. By leveraging cutting-edge mobile and cloud technologies, the system integrates seamlessly with smart IoT devices to collect, process, and analyze energy consumption data. The application features an intuitive interface that visualizes usage patterns through dynamic charts, predictive statistics, and anomaly detection algorithms. It provides actionable insights for users to identify inefficiencies, adapt behaviors, and achieve long-term energy savings. Additionally, energy providers gain unprecedented visibility into network usage, unlocking opportunities for optimizing electricity production and distribution. Built upon a robust architecture combining a Spring Boot backend and an Android-based frontend, the system ensures reliable synchronization, secure data handling, and responsive user interactions. The findings demonstrate the potential of integrating real-time analytics with mobile technology to address critical energy challenges while fostering sustainability and efficiency across diverse sectors.

International Journal on Science and TechnologyVol. 16(2)
Openalex Percentile: Top 12%
Smart Grid Energy Management
2.19
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.