Development of a Data-driven Energy Monitoring System for Power Consumption and Power Quality Monitoring
DOI:
https://doi.org/10.69650/rast.2025.263660Keywords:
Smart Metering , IoT , Energy Monitoring , Fog-Cloud , Power Quality , Rule-Based System, the UTS Research GrantAbstract
The rapid growth of global electricity demand and the widespread adoption of non-linear loads have intensified power quality (PQ) concerns, revealing critical limitations of existing energy metering solutions. These systems lack real-time monitoring, data granularity, and analytical capabilities necessary for advanced energy and power quality management. This paper presents the design, development, and validation of the Data-Driven Energy Monitoring System (DDEMS), an IoT-based platform integrating smart metering, edge computing, and hybrid analytics for real-time PQ assessment and energy management. The system combines low-cost sensors such as PZEM-004T, ZMPT101B and SCT-013, incorporated with an ESP32 microcontroller to measure key electrical parameters, and classify PQ events using a cloud-based rule-based engine which compliance with IEEE 1159 and IEC 61000-4-30 for power quality standards. Experimental validation was conducted on DDEMS against the calibrated Lovato DMG800 power multimeter and Fluke 437-II demonstrated its accuracy with overall system measurement errors at 1.24% of mean absolute percentage error (MAPE). Furthermore, the system successfully identified and categorized PQ disturbances into four severity levels, enabling timely mitigation the issues such as supply voltage fluctuations, harmonic distortions, and poor power factors. A subsequent 90-day field deployment in residential settings confirmed the system's reliability, and a web-based dashboard facilitating energy optimization with power quality monitoring. DDEMS addresses key limitations of existing solutions by offering a cost-effective, scalable alternative to expensive PQ analyzers while maintaining high accuracy and real-time capabilities. The system's modular architecture and successful real-world implementation highlight its potential for widespread adoption in smart energy management applications.
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