Mobile Application For Electricity Meter Reading and Billing Using Image Processing and Machine Learning

Traditionally, electric power utilities acquire the energy consumption information of users for billing through manual meter reading. With the advent of smart digital energy meters coupled with IoT solutions, meter reading services have improved considerably where user energy data could be collected remotely through telemetry. In many developing countries where most electric energy meters are still post-paid or non-smart devices, the utilities continue to rely on physical inspection and recording the user energy consumption for billing. This method is tedious and prone to error and delays in customer bill preparations. This paper proposes a mobile application solution that involves taking real-time pictures of energy meter readings using a mobile device and transmitting the data to a central server to process and extract the user consumption information using an artificial intelligence engine. The mobile application allows users to enter details of the meter being read. The optical character recognition technology was used as the intelligence engine at the central server to extract the meter readings from the images. The character recognition engine was trained and tested using the open-source MNIST database, which has 60,000 samples for training and 10,000 samples for testing. The meter reading system was first tested using an existing database of recorded images of energy meters, then tested at selected residences in different communities. Results revealed that the application could extract the different customer energy consumption records from the image data with an accuracy of 99.09%. An average time of 1.52 s was recorded to extract the customer energy consumption data from the images and 0.34 s to transmit the data to the billing server. The transmission time is, however, dependent on the communication service provider used for the data transmission. This software-based solution will provide enormous benefits to electric utilities that use post-paid energy meters and rely on the manual recording of the user data. The utility company will make reading faster, easier, and more accurate by automating the meter reading process.