Low-Cost Remote Reading of Analog Electric Meters Using Computer Vision and Neural Networks

Juan Fuentes-Maza D.; Sebastián Michael Castro-Apaza H.; Aranda-Utcañe I.H.; Linarez-Wong E.T. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.703

Resumen

This article presents the design and implementation of an automated remote reading system for analog electricity meters, based on computer vision and artificial neural networks. The ESP32-CAM module is used as the capture unit, evaluating three optical configurations of the OV2640 sensor (66°, 120°, and 120° adjustable) to identify the most suitable in terms of sharpness and coverage. The obtained images are processed using binarization, segmentation, and normalization techniques and input into a neural network trained with the MNIST dataset for automatic digit recognition. The results show that the 120° adjustable variant at a distance of 5 cm offers greater capture accuracy. The neural model achieved an accuracy of 96.18% when evaluated with external images, demonstrating its generalization capabilities in real-world conditions. This proposal represents a low-cost, replicable, and technically viable solution for environments where analog meters are still used, contributing to energy automation in contexts with limited infrastructure.

Instituciones: Universidad Tecnológica del Perú