Real-Time Energy Management in Buildings Using Reinforcement Learning Techniques

While simultaneously maintaining the level of comfort and reducing the costs associated with functionality, the primary objective is to reduce the quantity of energy that is spent as much as possible. During the investigation, a framework that is anchored on RL is developed. By learning from literal and real-time data, reacting to varied environmental circumstances, and taking into account the actions of inhabitants, this system can make substantial alterations to the amount of energy that is currently being used. To anticipate and adjust to the requirements for energy, the system that has been shown makes use of complex algorithms. This ultimately leads to an increase in efficiency being achieved by the system. The results of the simulation indicate that there have been significant improvements in terms of both the amount of energy that has been saved and the functional performance that has been achieved in contrast to more conventional control strategies. Furthermore, the findings shed light on the probable role that RL will play in the accomplishment of sustainable energy operations in smart structures. This is in addition to the fact that they contribute to the broader pretensions of energy efficiency and environmental sustainability.