Authors :
Stella Ebere Edeh; Chinagolum Ituma; Maduabuchi Ignatius Edeh; Njoku Chimee Mercy
Volume/Issue :
Volume 11 - 2026, Issue 8 - August
Google Scholar :
https://tinyurl.com/ynfzxjun
Scribd :
https://tinyurl.com/yd9whpk8
DOI :
https://doi.org/10.38124/ijisrt/26aug235
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
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.
Keywords :
Energy Management System, Smart Grid, Renewable Energy Integration, Internet of Thing.
References :
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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.
Keywords :
Energy Management System, Smart Grid, Renewable Energy Integration, Internet of Thing.