Authors : Aditya Chinthnalli; Akshay Aspalli

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/2s3np5df

Scribd : https://tinyurl.com/54suvver

DOI : https://doi.org/10.38124/ijisrt/25nov1368

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Ecometer an IoT-based system designed to provide insight for people to understand the amount of electricity used in a campus and how it affects the environment. Many students and staff use electricity every day but do not know how much CO2 is released at power generation side. Measuring energy use in real time, the Ecometer converts this into a clear CO2 footprint and tree-equivalent value at consumer side .This device features an ESP32 microcontroller and a PZEM-004T sensor to accomplish accurate voltage, current, power, and energy measurements. The ESP32 then calculates the total energy in kWh and multiplies it by a carbon factor of 0.82 kg CO2 per kWh to show how much CO2 is produced at the power plant. It makes the environmental impact simple to understand.This system also converts CO2 into the "Number of Trees Needed" to absorb that amount of carbon. The results appear both on a 16×2 LCD and on a web dashboard hosted by the ESP32. Anyone connected to the Wi-Fi can see the live energy use, CO2 output, and tree count. This promotes awareness, reduces wastage, and aids campus sustainability.

Keywords : IoT, ESP32, PZEM004T, CO2 Footprint, Tree Indicator, Sustainability, Energy Monitoring.

References :

1. Hossain et al. is the first source, likely titled "A Real-Time Low-Cost Energy Data Acquisition and Monitoring System for Household Appliances." It is listed as a 2025 publication by Preprints.org with the DOI 10.20944/preprints202510.1691.v1.
2. Gholizadeh et al.'s paper is highly likely titled "Anomaly Detection and Prediction of Energy Consumption for Smart Homes Using Machine Learning." It was published in the ETRI Journal in September 2024, although the specific
3. Shaik et al.'s contribution is likely "A Smart Energy Monitoring System using ESP32 Microcontroller," which was published in e-Prime - Advances in Electrical Engineering and Energy in June 2024. The DOI for this article is 10.1016/j.prime.2024.100666.
4. Alvin Ancy's paper is titled "IoT-Based Smart Energy Monitoring System using Blynk Application." It was published in the International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE) in 2024
5. Gopika b and Dr. George's article is confirmed as "Low-Cost and Scalable Data Acquisition System for Real-Time Wireless Sensor Monitoring." This was published in the IEEE International Conference on Communication, Computing and Internet of Things (ICCCIT) in 2021, and its DOI is 10.1109/ICCCIT53315.2021.9702220.
6. Yadav et al.'s source, concerning gamification, is linked to a highly similar paper published in 2022 titled "Gamified Apps and Customer Engagement: Modeling in Online Shopping Environment." This appeared in the Transnational Marketing Journal, and a ResearchGate DOI is 10.13140/RG.2.2.14810.93122.
7. Arévalo-Guerrero et al. published the article "Integration of Energy and Mobility Data for Personalized Carbon Footprint Calculation and Awareness." This paper was published in IEEE Access in 2024 with the DOI 10.1109/ACCESS.2024.3396791.
8. Sundar et al.'s paper is likely titled "Low-Carbon Green Indicator System for Transportation Logistics and Energy Efficiency Optimization Methods," and is listed for publication in Advances in Engineering Technology Research in September 2025.
9. Pawade and Hate's theoretical foundation is likely the article "Machine Learning Based Smart Energy Metering and Monitoring System." It was published in the International Journal for Research in Applied Science & Engineering Technology (IJRASET) in 2023.
10. The Central Electricity Authority (CEA) is the authoring organization for the source detailing the 0.82 kg CO₂ Per kWh emission factor. The publication title is the CO2 Baseline Database for the Indian Power Sector.¹ The publication name is the CEA Official Report/Database. The publication year varies, often cited as the fiscal year the data covers.