A Deep Learning-Driven Framework for Sustainable and Intelligent Energy Management in Smart Cities

To minimise their negative effects on the environment, cutting-edge technologies are needed to optimise energy consumption in today's fast-growing metropolitan areas. With the integration of Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNS) & Reinforcement Learning (RL), this study offers a deep learning-based energy optimisation framework for Iot-enabled smart cities that will improve sustainability, encourage the use of renewable energy sources & increase efficiency. The suggested hybrid model improves system performance for better use of renewable energy sources, lower carbon emissions & more energy efficiency as compared to conventional methods. By utilisation of cloud-based analytics, the system allows adaptive learning and real-time decision-making by deploying lightweight algorithms on edge devices. The results show that the current methods are shoddy, and they provide a scalable route for environmental city planning. The integration of digital twin technologies with large-scale Iot deployment is an area that needs more investigation for improved predictive abilities in future work.

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