Design and Simulation of an IoT-Based Smart Energy Monitoring and Power Management System

This record contains the manuscript and the reproducible simulation files for the design and simulation of an IoT-based smart energy monitoring and power-management system for residential and small commercial use. The proposed architecture combines voltage and current sensing, an ESP32-class microcontroller, Wi-Fi communication, MQTT data transmission and a remote dashboard. It calculates RMS voltage, RMS current, power factor, active power and cumulative energy, and raises alerts when configurable power or daily-energy thresholds are exceeded. A one-minute-resolution Python simulation was developed using an assumed six-load household profile (4.805 kWh/day; 144.15 kWh over 30 days), together with a 30-day stochastic run and a Monte-Carlo study of assumed sensor and ADC errors. All numerical results are simulation outputs based on stated assumptions; they are not laboratory measurements from a physical prototype. The archive includes: simulate.py (simulation script, fixed random seed 42), CSV files with the results, and the figures used in the manuscript.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-29
DOI
https://doi.org/10.5281/zenodo.23030942
Primary Topic
Smart Grid Energy Management
Type
article
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Design and Simulation of an IoT-Based Smart Energy Monitoring and Power Management System

Shahid Imran
Zenodo (CERN European Organization for Nuclear Research)
Smart Grid Energy Management
article

Design and Simulation of an IoT-Based Smart Energy Monitoring and Power Management System

Shahid Imran
article en

Abstract

This record contains the manuscript and the reproducible simulation files for the design and simulation of an IoT-based smart energy monitoring and power-management system for residential and small commercial use. The proposed architecture combines voltage and current sensing, an ESP32-class microcontroller, Wi-Fi communication, MQTT data transmission and a remote dashboard. It calculates RMS voltage, RMS current, power factor, active power and cumulative energy, and raises alerts when configurable power or daily-energy thresholds are exceeded. A one-minute-resolution Python simulation was developed using an assumed six-load household profile (4.805 kWh/day; 144.15 kWh over 30 days), together with a 30-day stochastic run and a Monte-Carlo study of assumed sensor and ADC errors. All numerical results are simulation outputs based on stated assumptions; they are not laboratory measurements from a physical prototype. The archive includes: simulate.py (simulation script, fixed random seed 42), CSV files with the results, and the figures used in the manuscript.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Openalex Percentile: Top 22%
Smart Grid Energy Management
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