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Title	Machine Learning Based On Reinforcement Learning For Smart Grids: Predictive Analytics In Renewable Energy Management
ID_Doc	35938
Authors	Li M.; Mour N.; Smith L.
Year	2024
Published	Sustainable Cities and Society, 109
DOI	http://dx.doi.org/10.1016/j.scs.2024.105510
Abstract	This paper introduces a groundbreaking approach to energy management in smart cities, leveraging deep reinforcement learning (DRL) and secured blockchain technology. The escalating demand for energy in urban areas necessitates intelligent solutions that optimize energy usage while ensuring data security and integrity. Our proposed technique integrates DRL algorithms with blockchain technology to achieve optimal energy management while addressing security concerns. The core innovation lies in the development of a DRL framework that dynamically adapts to changing energy demands and environmental factors. By continuously learning and optimizing energy allocation strategies, our approach enhances resource utilization efficiency and reduces wastage, contributing to sustainable smart city initiatives. Additionally, the incorporation of blockchain technology ensures data immutability, transparency, and secure transactions within the energy management system. We validate the effectiveness of our method through extensive simulations and real-world experiments, demonstrating significant improvements in energy efficiency, cost savings, and security compared to traditional approaches. Our research paves the way for future advancements in smart city infrastructure, offering a scalable and robust solution for optimal energy management with heightened security measures. © 2024 Elsevier Ltd
Author Keywords	Deep reinforcement learning; Energy management; Optimization; Secured blockchain technology; Smart cities

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