Electricity Misuse With Pzem Anomaly Detection and Notification
DOI:
https://doi.org/10.54536/ajsts.v5i1.7719Keywords:
Anomaly Detection, Electricity Misuse, Notification, PZEM-004TAbstract
The purpose of this study was to create an Electricity Misuse with PZEM Anomaly Detection and Notification System to address the challenges in apartment electricity monitoring, including misuse detection, excessive consumption and failure to track the actual consumption, and eventually enhance efficiency of the landlords and tenants. The scope, development and implementation of a system that can monitor per-room electricity usage, abnormal usage and provide timely alerts with integrated hardware and software, such as sensors, microcontrollers, communication modules and a web-based dashboard were covered. The methods involved identifying existing problem, designing of system architecture, and assembly of the components like the PZEM-004T, Arduino R4, and ESP32 and development of the software to collect data, process it and monitor it remotely and then testing it in a real-world environment at the Mar-Lee Apartment in General Santos City with the involvement of the landlady and tenants. The results demonstrated that the system was able to monitor electricity consumption, identify anomalies correctly and issue timely alerts, garnering good reviews in terms of functionality, usability and reliability, acceptability as well as physical and hardware aspect. In conclusion, the system is a convenient, efficient, and cost-effective solution to the current problem of better electricity management, minimize the misuse, and more responsible use of the energy among tenants.
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References
Ahmed, S., Rahman, T., & Chowdhury, P. (2022). Energy theft detection in smart grids: Taxonomy, comparative analysis, challenges, and future research directions. IEEE/CAA Journal of Automatica Sinica, 9(4), 578–600. https://doi.org/10.1109/JAS.2022.105404
Alcaraz, H. A. (2022). Assessment of the non-technical loss in electric power system of Isabela II Electric Cooperative (ISELCO II). International Journal of Progressive Research in Science and Engineering, 3(9), 8–17.
Alibin, R., Dela Peña, S., & Abad, V. (2021). IoT-based electricity monitoring system with theft detection using threshold analysis [Unpublished undergraduate research].
Collier, J., & Guha, R. (2025). Low-cost LSTM watchdog system for energy theft detection with reduced computational load. Journal of Energy Systems. Advance online publication.
Francisco, J. (2022). Trends and causes of electricity supply interruptions among Philippine electric cooperatives (Technical report). National Electrification Administration.
Gomez, R., Santos, E., & Dizon, L. (2022). Transformer load assessment in Barangay San Nicolas, Arayat, Pampanga. Philippine Engineering Journal.
Guarda, P., Molina, S., & Reyes, K. (2023). A literature survey of non-hardware-based non-technical loss (NTL) detection methods. Energies.
Haq, F., Durrani, S., & Malik, A. (2023). Deep convolutional neural networks for electricity theft identification from large-scale meter data. IEEE Transactions on Smart Grid.
Leynes, C. (2024). Real-time distribution transformer load monitoring using ESP32 and cloud-based visualization [Undergraduate thesis, Philippine State University].
Mababa, J. C. (2023). Development of a real-time electricity meter monitoring with theft detection alert system. International Journal of Research and Innovation in Social Science, 7(9), 886–893. https://doi.org/10.47772/IJRISS.2023.70976
Magnaye, N., Saguid, J. R., Urate, R. M., & Malco, M. D. (2025). eVolta: A cloud-based and real-time electricity usage monitoring system. American Journal of Data Science and Artificial Intelligence, 1(1), 1–7. https://doi.org/10.54536/ajdsai.v1i1.4884
Nebrida, A. P., Amador, C. D., Madiam, C. M., & Ranche, G. J. S. (2023). Development of smart meter to monitor real-time energy consumption for sustainability. International Journal of Sustainable Construction Engineering and Technology, 14(4), 28–39.
Panalangin, M. L., Mantikayan, J. M., Abdulgani, M. A., & Mohamad, H. A. (2023). Integration of IoT-knowledge-based architecture in the development of the daily time records system for the Ministry of Science and Technology, Philippines. American Journal of Innovation in Science and Engineering, 4(1). https://doi.org/10.54536/ajise.v4i1.3947
Stevenson, L., Patel, R., & Gomez, H. (2025). Electricity theft in developing nations: Technology, poverty, and governance factors. Energy Policy Journal. Advance online publication.
Tsao, Y., Liang, F., & Cheng, K. (2024). Machine-learning techniques for improved electricity theft detection and grid reliability. IEEE Transactions on Industrial Electronics.
Zulu, P., & Dzobo, O. (2023). Real-time double-metering electricity theft detection using IoT and GSM technologies. International Journal of Electrical and Computer Engineering.
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Copyright (c) 2026 Ian Glenn B. Adana, Lovennia Khaye P. Zorilla, Yzyl S. Domingo, Lloyd O. Arenas
This work is licensed under a Creative Commons Attribution 4.0 International License.
