Random Forest Implementation in Prepaid Electric Meter Recognition

Authors

  • Komang Jaya Bhaskara Mahatya Tel-U
  • Fathoni Waseso Jati
  • Budhi Irawan
  • Faisal Candrasyah Hasibuan

DOI:

https://doi.org/10.25124/cepat.v1i02.5228

Keywords:

Random Forest, Image recognition, Seven-segment recognition, Digital electric meter reading, Electricity Monitoring

Abstract

While prepaid electricity services provide better flexibility, it comes with an additional step for the customer. Instead of paying a monthly bill based on electric usage, a prepaid system requires customers to actively predict their electricity usage before they pay for the correct electricity value. This presents a challenge because Underestimating electricity usage may lead to a power outage. Therefore, a system that monitors electricity can be developed to address this issue. There are two approaches to developing an electric monitoring system: designing the electric meter equipped with monitoring features or designing an external capturing device to work with the current electric meter. The first approach is costly and requires a meter disassembly. Thus, in this paper, the second approach is used. By utilizing image processing and a Random Forest machine learning algorithm, a monitoring device can be developed to read the digital meter's display. Although it may affect performance due to the low-power device, Raspberry Pi 3 and Raspberry Camera are used to provide automation. This method yields an accuracy of 97% using 375 images.

Downloads

Download data is not yet available.

References

D. Gunawan, D. Erwanto, and Y. Shalahuddin, “Studi Komparasi Kwh Meter Pascabayar Dengan Kwh Meter Prabayar Tentang Akurasi Pengukuran Terhadap Tarif Listrik Yang Bervariasi,” Setrum Sist. Kendali-Tenaga-elektronika-telekomunikasi-komputer, vol. 7, no. 1, p. 158, 2018, doi: 10.36055/setrum.v7i1.3408.

PT. Perusahaan Listrik Negara, “Keuntungan Listrik Pintar.” https://web.pln.co.id/pelanggan/listrik-pintar/keuntungan-listrik-pintar (accessed Jul. 31, 2022).

T. Kasar, “Recognition of Seven-Segment Displays from Images of Digital Energy Meters,” in Lecture Notes in Networks and Systems, 2019, vol. 43, pp. 1–10. doi: 10.1007/978-981-13-2514-4_1.

S. Popayorm, T. Titijaroonroj, T. Phoka, and W. Massagram, “Seven Segment Display Detection and Recognition using Predefined HSV Color Slicing Technique,” in JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence, 2019, pp. 224–229. doi: 10.1109/JCSSE.2019.8864189.

V. Shenoy and O. Aalami, “Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition,” in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018, vol. 2017, pp. 1564–1570.

Z. Zhang, Z. Hua, Y. Tang, Y. Zhang, W. Lu, and C. Dai, “Recognition method of digital meter readings in substation based on connected domain analysis algorithm,” Actuators, vol. 10, no. 8, pp. 1–14, 2021, doi: 10.3390/act10080170.

U. Suttapakti, T. Titijaroonroj, W. Nunsong, and D. Kakanopas, “Seven Segment Display Detection and Recognition via Deep Learning Technique,” pp. 1–4, 2022, doi: 10.1109/ecti-con54298.2022.9795620.

C. N. Truong, N. Q. H. Ton, H. P. Do, and S. P. Nguyen, “Digit detection from digital devices in multiple environment conditions,” Proc. - 2020 RIVF Int. Conf. Comput. Commun. Technol. RIVF 2020, pp. 5–7, 2020, doi: 10.1109/RIVF48685.2020.9140775.

H. Shuo, Y. Ximing, L. Donghang, L. Shaoli, and P. Yu, “Digital recognition of electric meter with deep learning,” 2019 14th IEEE Int. Conf. Electron. Meas. Instruments, ICEMI 2019, pp. 600–607, 2019, doi: 10.1109/ICEMI46757.2019.9101443.

A. K. Sharma and K. K. Kim, “Lightweight CNN based Meter Digit Recognition,” J. Sens. Sci. Technol., vol. 30, no. 1, pp. 15–19, 2021, doi: 10.46670/jsst.2021.30.1.15.

Downloads

Published

2022-08-30

Most read articles by the same author(s)