In recent years, the concept of smart buildings has gained significant attention due to its potential for improving energy efficiency and sustainability. Smart buildings use advanced technologies, such as sensors, control systems, and artificial intelligence, to monitor and optimize building performance in real time. However, despite the promise of smart buildings, many buildings still consume excessive amounts of energy, contributing to environmental degradation and increasing operational costs for building owners.
Energy optimization is crucial for sustainable and smart environments such as smart cities and urban buildings. Advanced communication systems and IoT sensor systems play a key role in enhancing energy efficiency by monitoring and controlling such eco-systems. The proposed RL approach reduces power consumption by 12.6\% and HVAC power consumption by 6.7%. Additionally, this study highlights the importance of advanced communication systems in controlling and managing smart buildings' energy consumption.
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions in an environment by receiving feedback in the form of rewards or punishments. The goal of the agent is to learn the optimal policy or decision-making strategy that maximizes the cumulative reward over time, through trial-and-error interactions with the environment. RL is a powerful tool for solving problems where there is no well-defined dataset, and the agent must learn from experience.
RL can be used to optimize energy efficiency in smart buildings by learning optimal temperature, lighting, and occupancy control strategies. The agent takes actions such as adjusting settings, turning on/off equipment, and repositioning sensors to maximize rewards, based on energy consumption and occupancy detection accuracy. RL is a powerful tool for adapting to changing conditions in real-time, leading to significant energy savings while maintaining comfort and safety.