Online Electricity Bill Management System: A Web Application for an Energy Supplier Company in Somalia Using Machine Learning
TL;DR
It is demonstrated how the integration of web technologies and machine learning can modernize electricity billing systems and enhance service delivery in the energy sector.
Abstract
This paper presents the design and development of an Online Electricity Bill Management System (OEBMS) for an energy supplier company in Somalia, with the objective of improving the efficiency, accuracy, and reliability of electricity billing operations. The system is developed using the Waterfall model within the Software Development Life Cycle (SDLC), providing a structured framework for analysis, design, implementation, and testing. The proposed OEBMS replaces traditional manual and paper-based billing systems with a web-based application that enables customers to access billing services anytime and from anywhere. It offers key functionalities such as customer account management, electricity consumption monitoring, automated invoice generation, and secure online payment processing. A major contribution of this paper is the integration of machine learning techniques, including regression models and random forest algorithms, to analyze historical electricity consumption data and predict future usage patterns. This predictive capability supports energy providers in demand forecasting and resource planning while also enabling customers to better understand and manage their electricity usage. In addition, the system generates analytical reports that identify consumption trends, potential demand increases, and customer segments with varying usage behaviors. The system utilizes a MySQL database for efficient data storage and management and an Apache web server to support the application interface. By automating billing processes and reducing human intervention, the OEBMS minimizes errors, improves data accuracy, and reduces operational costs. Furthermore, it promotes environmental sustainability by eliminating paper-based processes. Overall, this paper demonstrates how the integration of web technologies and machine learning can modernize electricity billing systems and enhance service delivery in the energy sector.
