An Integrated Deep Learning and Edge Computing Framework for Intelligent Energy Management in IoT-Based Smart Cities

As smart cities increasingly embrace Internet of Things (IoT) technologies, the demand for effective and intelligent energy management solutions is on the rise. This paper proposes an integrated framework that combines deep learning techniques with edge computing to optimize energy consumption and enhance sustainability in IoT-based smart cities. The framework utilizes the potential of edge devices for local data processing and analysis, reducing latency and improving real-time decision-making. It employs deep learning algorithms to model intricate data relationships and offer precise predictions for energy consumption patterns. The fusion of deep learning and edge computing aims to tackle challenges arising from the vast data volumes generated by IoT devices, while ensuring energy efficiency and responsiveness. This research makes a significant contribution by presenting a holistic IoT-based framework designed to manage energy within smart cities. This framework seamlessly integrates various elements of IoT architecture, crucially facilitating the collection and storage of relevant data for intelligent energy management applications. Furthermore, it functions as a platform for external entities to create their own applications. The study delves into intelligent energy management solutions, incorporating advanced mechanisms to tackle the rising energy demand and depletion of resources, which result in increased energy consumption and building maintenance requirements. The data collected is utilized for monitoring, controlling, and improving the overall efficiency of the system.