Smart Energy Management in Nigeria: Implementing IoT and AI for Sustainable Urban Development
This article explores the application of Artificial Intelligence (AI) and the Internet of Things (IoT) in improving renewable energy management within urban development in Nigeria. With the global shift towards sustainable living, many urban areas are upgrading their infrastructure to be more environmentally friendly. This transition involves advancements in waste management, increased renewable energy generation, and integration of modern technologies to optimise energy systems. A key focus of this research is the importance of effective policies in addressing challenges within the energy, transportation, and building sectors. The document emphasises collaboration among municipal authorities and stakeholders to optimise clean energy deployment, making cities smarter and more sustainable. The Smart Green Energy (IoT-SGE) system is central to this discussion, as it employs IoT technology for precise energy usage regulation through continuous monitoring and secure communication. The integration of AI allows the system to learn and adapt over time, significantly enhancing energy management efficiency. The article provides insights into best practices and frameworks for advancing smart cities with improved renewable energy management by examining various studies and practical applications. It also analyses successful case studies from global smart cities, demonstrating how innovative strategies can lead to sustainable urban growth. This research aims to outline a comprehensive strategy for implementing smart energy management systems, fostering a more sustainable future for urban areas in Nigeria.
Authors
- Christian Chukwuemeka Nzeanorue
- Benedict Chibukem Okpala
Institutions
- George Washington University (US)
- Kiryu University (JP)
Publication Details
- Journal
- Path of Science
- Published
- 2024-10-31
- DOI
- https://doi.org/10.22178/pos.110-4
- Citations
- 3
- Primary Topic
- Smart Grid Energy Management
- Type
- article
- Field-Weighted Citation Impact
- 0.63