AI for Sustainable Energy Management in Smart Cities in Nigeria

Abstract

This study explores the role of artificial intelligence driven smart energy management system as a tool for addressing rising urban energy demand and promoting sustainable development in developing countries, with specific focus on Nigeria. The study revealed that rapid urbanization has placed significant strain on conventional power infrastructure, resulting in inefficiencies, high operational costs, frequent outages, and increased carbon emissions. To respond to these challenges, the paper examined the design and application of an AI-powered platform that integrates data from IoT sensors, smart meters, and weather stations to support real time energy monitoring and decision making. The study analyzed existing utility operations and technical frameworks to identify key system requirements and data workflows that inform the development of an intelligent energy management solution. Using Objection-Oriented Analysis and Design Methodology (OOADM) and Unified Modeling Language (UML), the study proposed modular and scalable system architecture capable of demand forecasting, grid anomaly detection, predictive maintenance, and optimized integration of renewable energy sources such as solar and wind. The findings indicated that the adoption of AI-driven energy management systems can substantially reduce peak energy load, minimize unplanned outages, lower maintenance costs, and cut carbon emissions, while providing grid operators and policymakers with accurate, real time insights. The paper concludes that intelligent energy management solutions are critical for improving efficiency, strengthening energy security, and supporting sustainable national development in Nigeria and similar developing economies.