Reinforcement learning has shown considerable promise in enhancing building energy efficiency, yet a flexible framework for its implementation has been lacking. BuildingGym seeks to address this issue by providing a user-friendly platform that allows building managers and AI specialists to train and implement RL algorithms tailored for energy management scenarios. By utilizing EnergyPlus as its core simulator, BuildingGym facilitates both system-level and room-level control, thus broadening the scope of its application in energy management.
One of the notable features of BuildingGym is its ability to accept external signals as control inputs, enabling use cases in smart grid environments and electric vehicle (EV) communities. This functional flexibility is crucial as it allows for dynamic control strategies that can adapt to varying conditions. Built-in RL algorithms simplify the training of optimal control strategies, allowing users to focus on configuration without deep technical knowledge. Furthermore, the performance of these algorithms in managing cooling loads demonstrates the tool’s efficiency in real-world applications, providing significant implications for energy savings and sustainability in building operations.
👉 Pročitaj original: arXiv AI Papers
