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Title	Smart Green Cities Using Iot-Based Deep Reinforcement Learning Energy Management
ID_Doc	50953
Authors	Tripathy N.; Tripathy S.S.
Year	2024
Published	Internet of Things and Big Data Analytics for a Green Environment
DOI	http://dx.doi.org/10.1201/9781032656830-11
Abstract	Modern energy systems, particularly electricity systems, are becoming more intelligent because of the recent development of sophisticated and intelligent measurement tools. Recent communication and hardware technology developments have made global interconnectedness through the Internet of Things (IoT) possible. The IoT is the foundation for smart grids and smart city green energy management applications. Research on energy management in well-regulated IoT networks still needs to be done, which makes intelligent load forecasting crucial for optimal energy management (EM) in smart cities. Acquiring the best possible energy performance at the building cluster level and reaching energy neutrality by optimizing locally generated renewable energy require accurate short-term (less than a day) energy demand forecasts. However, predictions and analyses of building energy efficiency continue to be centered on the level of the individual structure rather than on building clusters or small neighborhood scales. This chapter presents an IoT-based deep-learning energy management framework. The study results demonstrate that the suggested framework can identify energy use, forecast energy demand in smart cities, and save money. © 2025 selection and editorial matter, Yousef Farhaoui, Bharat Bhushan, Nidhi Sindhwani, Rohit Anand, Agbotiname Lucky Imoize and Anshul Verma; individual chapters, the contributors.
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