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JCSIS

Neural Networks for Sustainable Energy Optimization in Smart Cities Through IoT Applications

Nader BehdadWeiguo GeeWang Zhang
Volume 4

Abstract

As urbanization increases, the need for sustainable energy solutions in smart cities becomes critical. This study investigates the application of neural networks for optimizing energy systems in smart cities using IoT-based technologies. The research integrates IoT sensors to collect realtime data on energy consumption, renewable energy generation, and environmental factors. Neural network models are then employed to analyze this data and make real-time predictions, enabling dynamic energy management. The proposed approach aims to optimize energy distribution by predicting demand patterns, identifying inefficiencies, and suggesting energy-saving strategies. By leveraging deep learning algorithms, the system can dynamically adjust the energy grid, enhance the integration of renewable energy sources, and reduce carbon footprints. The study highlights the potential of IoT and neural networks to create intelligent energy management systems that contribute to sustainability goals and improve the overall efficiency of smart cities.

Keywords

Neural networks, sustainable energy, smart cities, IoT, energy optimization, renewable energy

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