Abstract

The transition toward net-zero smart cities necessitates advanced energy forecasting methodologies to enhance sustainability, optimize energy distribution, and balance supply-demand dynamics. Artificial Intelligence (AI) has emerged as a transformative tool in this domain, leveraging machine learning (ML) and deep learning (DL) techniques to provide highly accurate and adaptive energy predictions. This paper explores AI-driven energy forecasting frameworks, emphasizing the integration of real-time data from IoT-enabled smart grids, weather prediction models, and historical energy consumption patterns. Key AI methodologies, including time-series forecasting models, reinforcement learning strategies, and hybrid AI techniques, are examined for their efficacy in optimizing energy management. Additionally, this study addresses major challenges such as data integrity, model scalability, and cybersecurity risks associated with AI-driven energy systems. Through case studies and comparative analyses, we highlight the tangible benefits of AI in achieving net-zero energy objectives, offering insights into future advancements for sustainable urban development.

Recommended citation: Hoang, M., Nguyen-Vinh, K., & Vo, M.-T. (2025). AI-Based Energy Forecasting for Net Zero Smart Cities. IEEE ICECIE 2025.
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