Authors : Shashvat Sangle; Harsh Pandey; Atharva Khode; Najib Ghatte; Dr. Manisha Bansode

Volume/Issue : Volume 11 - 2026, Issue 5 - May

---

Google Scholar : https://tinyurl.com/3yvtjxn6

Scribd : https://tinyurl.com/4s9k3d8k

DOI : https://doi.org/10.38124/ijisrt/26May1046

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

---

Abstract : The ESP32 samples high-frequency waveforms, computes RMS, real and apparent power, energy, and power factor, then uploads data to Firebase. An AI engine performs anomaly detection, short-term load forecasting, and appliance-type inference using extracted signatures. The paper describes deep internal hardware theory, mathematical modelling, firmware algorithms, cloud architecture, AI pipelines, calibration, test results, error analysis, and deployment guidelines. The approach provides a scalable, safe, and practical smart energy metering solution.

Keywords : Smart Meter, ESP32, ZMPT101B, ACS712, Firebase, IoT, Load Forecasting, RMS, Power Factor, AI Analytics.

References :

1. Qingxian Electronics, “ZMPT101B Voltage Sensor Module Datasheet,” 2018.
2. Allegro Microsystems, “ACS712 Hall-Effect Based Linear Current Sensor IC Datasheet,” 2016.
3. Espressif Systems, “ESP32 Technical Reference Manual,” Version 4.4, 2023.
4. Microchip Technology, “ADE7758/ADE7753 Energy Metering IC Application Notes,” 2020.
5. Texas Instruments, “ADS131M04 – 4-Channel, 24-Bit Delta-Sigma ADC for Energy Measurement,” 2022.
6. V. Oppenheim and R. W. Schafer, *Discrete-Time Signal Processing*, 3rd ed., Pearson, 2013.
7. P. Welford, “Note on a method for calculating corrected sums of squares and products,” *Technometrics*, 1962.
8. Google Developers, “Firebase Realtime Database Documentation,” 2023.
9. L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey,” *Computer Networks*, 2010.
10. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” *Neural Computation*, 1997.
11. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in *Proc. KDD*, 2016.
12. L. Breiman, “Random forests,” *Machine Learning*, 2001.
13. F. T. Liu, K. M. Ting, and Z. Zhou, “Isolation Forest,” *IEEE ICDM*, 2008.
14. G. W. Hart, “Nonintrusive appliance load monitoring,” *Proceedings of the IEEE*, 1992.
15. H. Panapakidis and T. Alexiadis, “Energy consumption forecasting based on machine learning,” *Energy Procedia*, 2016.
16. A. Ipakchi and F. Albuyeh, “Grid of the future: Smart grid technologies,” *IEEE Power and Energy Magazine*, 2009.
17. J. Arrillaga and N. Watson, *Power System Harmonics*, Wiley, 2003.