AI-driven innovations in energy efficiency: Transforming smart buildings and urban areas through technology and digital transformation
1 Independent Researcher, Maryland, USA.
2 Independent Researcher, Massachusetts, USA.
3 Independent Researcher, Texas, USA.
4 School of Business, Worcester Polytechnic Institute, Massachusetts, USA.
5 Independent Researcher, Alimosho, Lagos, Nigeria.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(01), 141–152
Article DOI: 10.30574/wjarr.2024.24.1.2921
Publication history:
Received on 15 August 2024; revised on 28 September 2024; accepted on 30 September 2024
Abstract:
This study explores AI's transformative role in enhancing urban energy efficiency. Focusing on AI-driven innovations in smart grids, renewable energy integration, and predictive energy management, it evaluates AI's potential to optimize energy distribution, reduce waste, and improve sustainability. A comprehensive literature review and case studies are employed to analyze the application.
The findings indicate that AI technologies, including machine learning and predictive analytics, are crucial for optimizing energy consumption, managing renewable energy variability, and improving smart grid efficiency. AI enhances sustainability by forecasting energy demand and optimizing storage systems. However, challenges such as data privacy concerns, integration complexities with existing infrastructure, and the need for specialized AI expertise pose barriers to broader adoption of these technologies.
The study recommends that future research focus on advancing AI technologies for real-time optimization and explainability, as well as addressing the skills gap in AI development. Policymakers and energy stakeholders should invest in AI-driven solutions and establish supportive regulations to promote AI adoption in urban energy systems. In the long term, AI will be pivotal in creating more sustainable, resilient, and energy-efficient cities.
Keywords:
Artificial Intelligence; Energy efficiency; Smart buildings; Digital Transformation
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0