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Title	Energy Optimization In Sustainable Smart Environments With Machine Learning And Advanced Communications
ID_Doc	23349
Authors	Bereketeab L.; Zekeria A.; Aloqaily M.; Guizani M.; Debbah M.
Year	2024
Published	IEEE Sensors Journal, 24, 5
DOI	http://dx.doi.org/10.1109/JSEN.2024.3355229
Abstract	Enhancing energy optimization is crucial for sustainable and smart environments such as smart cities, connected and urban buildings, and cognitive cities. Advanced communication systems and Internet of Things (IoT) sensor systems play a key role in enhancing energy efficiency by monitoring and controlling such ecosystems. In this article, we propose a reinforcement learning (RL) approach for optimizing the energy consumption of multipurpose buildings using the EnergyPlus simulation environment. Our RL algorithm uses the proximal policy optimization with clipping (PPO-Clip) for online training and also includes an offline pretraining model to improve the stability of the proposed algorithm. The observed states in the model include indoor temperature, setpoint temperature, outside temperature, heating coil power, general heating, ventilation, air conditioning (HVAC) power, and occupancy count. Moreover, we have designed and implemented a reward function to guarantee the energy reduction and control consumption while maintaining comfortable indoor temperatures. We have bench-tested the proposed model, and therefore, the collected results demonstrated that the proposed RL approach outperforms the EnergyPlus baseline model, reducing the heating coil power consumption by 12.6% and HVAC power consumption by 6.7%. Additionally, this study highlights the importance of advanced communication systems and IoT sensors in managing and improving smart building's energy consumption. © 2001-2012 IEEE.
Author Keywords	Communication technologies; energy optimization; energy sustainability; Internet of Things (IoT); smart buildings; sustainable environments

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