AI-Enabled Smart Energy Management Systems Using IoT for Sustainable Urban Development - Fulbert Okouma; Ayim Nguia; Josepsh Gross; David Kamal; Herbert F. Bernard (The Eastasouth Journal of Information System and Computer Science, 2024)
The rapid growth of urban populations and increasing energy demands have intensified the need for efficient and sustainable energy management systems. Traditional energy management approaches are often inefficient, lacking real-time monitoring and adaptive control capabilities. This study proposes an AI-enabled smart energy management system integrated with Internet of Things (IoT) technologies to optimize energy consumption, improve efficiency, and support sustainable urban development. The research analyzes the contribution of various system components, including smart meters (28%), renewable energy sensors (22%), building energy sensors (20%), grid monitoring devices (18%), and demand-response systems (12%). The implementation of intelligent systems results in significant improvements in energy efficiency (34%), predictive demand accuracy (32%), system reliability (30%), cost reduction (28%), and sustainability performance (26%). The proposed framework employs a multi-layered architecture consisting of sensing, communication, processing, and application layers. Machine learning algorithms are used to analyze energy consumption patterns, forecast demand, and enable automated control mechanisms. The integration of edge and cloud computing enhances system performance by enabling real-time processing and scalable data management. The findings demonstrate that IoT and AI-driven energy management systems significantly enhance operational efficiency and sustainability. The proposed system enables proactive energy optimization, reduces waste, and supports the development of smart and sustainable cities.