IoT-Integrated Smart Energy Meter for Industrial Energy Monitoring

- Electric energy monitoring in industrial loads like pressure die casting is extremely vital especially due to their energy handling requirements. This work details the conception and implementation of an Internet of Things based Smart Energy Meter designed specifically for Industrial purposes. The system can measure three-phase voltage, current (up to 100A) and furnace temperatures with the aid of Resistance Temperature detectors. Using the ESP32 microcontroller and the Blynk Internet of things platform, the meter enables the collection, storage, and displaying of the data on the Cloud in real time. In order to enhance accuracy, the hardware architecture combines ZMPT101B voltage sensors, SCT-013-000 current sensors and a MAX31865 Resistance Temperature detector module. The software part of the system uses the Open-Energy Monitor library which contains various algorithms for energy calculations and transfers data over Serial peripheral interface/Wi-Fi protocols. The system has been tested in an industrial setting and the results have shown an accuracy of measurement within a tolerance of 1% for electrical and thermal characteristics plus a normal operation for variable conditions. The presented results prove the possibility of intra- system energy optimization and the industrial prospects of its application. Promising research directions include the addition of predictive maintenance features based on machine learning as well as more efficient scaling to support many different industrial applications. Key Words: Smart Energy Meter, IoT, ESP32, RTD Sensors, Energy Monitoring, Industrial Automation

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-02-06
DOI
https://doi.org/10.55041/ijsrem41416
Citations
1
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.73
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article

IoT-Integrated Smart Energy Meter for Industrial Energy Monitoring

Kiruthick Sundakaraipudur Maheshkumar
1 citations
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Smart Grid Energy Management
0.73
article

IoT-Integrated Smart Energy Meter for Industrial Energy Monitoring

Kiruthick Sundakaraipudur Maheshkumar
article en
1 citations

Abstract

- Electric energy monitoring in industrial loads like pressure die casting is extremely vital especially due to their energy handling requirements. This work details the conception and implementation of an Internet of Things based Smart Energy Meter designed specifically for Industrial purposes. The system can measure three-phase voltage, current (up to 100A) and furnace temperatures with the aid of Resistance Temperature detectors. Using the ESP32 microcontroller and the Blynk Internet of things platform, the meter enables the collection, storage, and displaying of the data on the Cloud in real time. In order to enhance accuracy, the hardware architecture combines ZMPT101B voltage sensors, SCT-013-000 current sensors and a MAX31865 Resistance Temperature detector module. The software part of the system uses the Open-Energy Monitor library which contains various algorithms for energy calculations and transfers data over Serial peripheral interface/Wi-Fi protocols. The system has been tested in an industrial setting and the results have shown an accuracy of measurement within a tolerance of 1% for electrical and thermal characteristics plus a normal operation for variable conditions. The presented results prove the possibility of intra- system energy optimization and the industrial prospects of its application. Promising research directions include the addition of predictive maintenance features based on machine learning as well as more efficient scaling to support many different industrial applications. Key Words: Smart Energy Meter, IoT, ESP32, RTD Sensors, Energy Monitoring, Industrial Automation

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(02)
Openalex Percentile: Top 32%
Smart Grid Energy Management
0.73
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