WattWise: Mobile-Based Energy Monitoring and Forecasting System for Home Appliances

Authors

Euller Grifien A. Cabe

Department of Information Technology, Jesus Reigns Christian College, Manila (Philippines)

Mark Angelo Florendo

Department of Information Technology, Jesus Reigns Christian College, Manila (Philippines)

Jordan Gonzales

Department of Information Technology, Jesus Reigns Christian College, Manila (Philippines)

Ms. Vivien Agustin

La Consolacion University (Philippines)

Dr. Ronald Fernandez

La Consolacion University (Philippines)

Article Information

DOI: 10.51244/IJRSI.2026.1306000030

Subject Category: Information Technology

Volume/Issue: 13/6 | Page No: 538-562

Publication Timeline

Submitted: 2026-05-20

Accepted: 2026-05-26

Published: 2026-06-18

Abstract

The research presents WattWise, a smartphone application that enables households to track and forecast electricity usage for improved management. The study aims to address the growing issue of high electricity costs in the Philippines by providing users with a convenient way to monitor appliance-level energy usage, estimate electricity expenses, and receive practical energy-saving recommendations. It integrates three core functions—live monitoring, appliance recognition, and predictive analysis—into one accessible mobile platform. The study utilized a developmental research approach guided by the Agile methodology to support the continuous design, development, testing, and improvement of the system. WattWise integrates an ESP32 microcontroller and PZEM-004T energy monitoring module to gather real-time electrical measurements such as voltage, current, power, and energy consumption. The system also incorporates a YOLOv8-based appliance identification feature, allowing users to scan household appliances using the mobile application camera. Captured data are processed through a Flask-based API and stored in Firebase Firestore for monitoring and analysis. Past consumption data are processed through ARIMA time-series analysis to generate forecasts of upcoming electricity demand and expenses. The system provides users with a dashboard that displays real-time energy usage, cumulative electricity consumption, estimated costs, and forecasted consumption trends. It also generates personalized recommendations and alerts that encourage more efficient electricity usage. Results indicate that combining AI, predictive modeling, and IoT-based monitoring enhances household energy management and raises user awareness of consumption patterns.

Keywords

Energy Monitoring, Forecasting System

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References

1. Albay, R. L. (2025). High electricity prices, frequent outages underscore need for rooftop solar in the Philippines. Eco-Business. https://www.eco-business.com/news/high-electricity-prices-frequent-outages-underscore-need-for-rooftop-solar-in-the-philippines/ [Google Scholar] [Crossref]

2. Apriandy Angdresey, Lanny Sitanayah, & Zefanya Marieke Philia Rumpesak (2023). An electricity consumption monitoring and prediction system based on the Internet of Things. In Proceedings of the International Conference on Intelligent Computing (ICIC 2022). IEEE. https://doi.org/10.1109/ICIC56845.2022.10007020Kenjale, et al. (2024). Smart electricity tracking system. International Journal of Engineering Research & Technology (IJERT), 13(11). https://doi.org/10.5281/zenodo.18130844 [Google Scholar] [Crossref]

3. U.S. Department of Energy. (n.d.). Reducing electricity use and costs. Energy Saver. https://www.energy.gov/energysaver/reducing-electricity-use-and-costs [Google Scholar] [Crossref]

4. Gopikrishna, P. B., & Mathew, J. A. (2021). Power consumption analysis and prediction of a smart home using ARIMA model. SSRN. https://doi.org/10.2139/ssrn.3819512 [Google Scholar] [Crossref]

5. Pagaduan, L. J. L., Portolazo, J. G., Delfin, J. X. D., Dela Cruz, J. P. O., & Estanda, M. B. O. (2023). The development of real-time energy consumption monitoring using IoT. Advanced Computing: An International Journal, 10(1/2/3). https://doi.org/10.5121/acii.2023.10302 [Google Scholar] [Crossref]

6. Silagpo, G. M., Cabacang, E. J. M., Ilustrisimo, R. L., Inajada, M. R., & Jueco, J. (2024). Monitoring and prediction of household power consumption using Internet of Things and ARIMA. In 2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO). IEEE. https://doi.org/10.1109/ICCMSO61761.2024.00044 [Google Scholar] [Crossref]

7. Zangrando, N., Fraternali, P., Petri, M., Vago, N. O. P., & González, S. L. H. (2022). Anomaly detection in quasi-periodic energy consumption data series: A comparison of algorithms. Energy Informatics, 5. https://doi.org/10.1186/s42162-022-00230-7 [Google Scholar] [Crossref]

8. Motta, L. L., Ferreira, L. C. B. C., Cabral, T. W., Lemes, D. A. M., Cardoso, G. dos S., Borchardt, A., Cardieri, P., Fraidenraich, G., de Lima, E. R., Neto, F. B., & Meloni, L. G. P. (2023). General overview and proof of concept of a smart home energy management system architecture. Electronics, 12(21), 4453. https://doi.org/10.3390/electronics12214453 [Google Scholar] [Crossref]

9. Rahman, A., Hossain, S., Ahmed, S., & Ahmed, M. T. (2025). IoT based smart energy consumption prediction for home appliances. International Journal of Intelligent Engineering and Electronic Business, 17(2). https://doi.org/10.5815/ijieeb.2025.02.06 [Google Scholar] [Crossref]

10. Guo, N., Chen, W., Wang, M., Tian, Z., & Jin, H. (2021). Applying an improved method based on ARIMA model to predict the short-term electricity consumption transmitted by the Internet of Things (IoT). Wireless Communications and Mobile Computing, 2021, Article 6610273. https://doi.org/10.1155/2021/6610273 [Google Scholar] [Crossref]

11. Caw-it, J. G., & Florence, J. B. (2025). Forecasting household electricity consumption using ARIMA: A time series approach. International Journal for Multidisciplinary Research, 3(7), 129–144. https://doi.org/10.5281/zenodo.15836495 [Google Scholar] [Crossref]

12. Sharma, S., Sarkar, D., & Gupta, D. (2012). Agile processes and methodologies: A conceptual study. International Journal on Computer Science and Engineering, 4(5). https://www.researchgate.net/publication/267706023_Agile_Processes_and_Methodologies_A_Conceptual_Study [Google Scholar] [Crossref]

13. Meralco. (2025). Higher rates this July 2025. Manila Electric Company. https://company.meralco.com.ph/news-and-advisories/higher-rates-july-2025 [Google Scholar] [Crossref]

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