Report on Current Developments in Building Automation and Environmental Sustainability
General Direction of the Field
The field of building automation and environmental sustainability is witnessing a significant shift towards more intelligent, privacy-aware, and cost-effective solutions. Recent advancements are leveraging deep learning (DL) and reinforcement learning (RL) to optimize control systems, particularly in complex environments such as commercial buildings and greenhouses. The focus is increasingly on developing model-free approaches that can learn directly from real-world data, reducing the reliance on predefined logic and proprietary simulations. This shift is driven by the need for systems that can adapt to diverse user preferences and environmental conditions, while also addressing privacy concerns and minimizing operational costs.
One of the key trends is the integration of DL models with real-world sensors and user interactions, enabling systems to learn and predict user preferences without the need for complex logic. This approach is particularly promising in building automation, where the goal is to create more user-friendly and efficient control systems. Additionally, the development of open-source simulation environments and datasets is fostering collaboration and accelerating research in RL-based control methodologies. These environments are designed to be more grounded in real-world conditions, allowing for more accurate and scalable training of RL agents.
Another notable development is the emphasis on privacy-aware control systems, particularly in cloud-based platforms. The challenge of ensuring privacy while optimizing control has led to the creation of fully model-free, event-triggered frameworks that minimize communication and computation overhead. These frameworks are designed to be more cost-effective and easier to implement, making them suitable for widespread adoption in building automation.
Noteworthy Papers
Logic-Free Building Automation: Introduces a novel DL-based approach that learns user preferences directly from wall switches and ceiling camera data, achieving high control accuracy and demonstrating the potential for smarter, user-friendly control systems.
Real-World Data and Calibrated Simulation Suite: Presents an open-source dataset and simulation environment for RL training, significantly advancing the ability to optimize energy and emission in buildings through scalable and real-world grounded RL approaches.
GreenLight-Gym: Offers an open-source RL benchmark for greenhouse control, addressing key challenges in RL-based greenhouse management and demonstrating improved generalisation to unseen conditions.
Privacy-aware Fully Model-Free Event-triggered Cloud-based HVAC Control: Proposes a cost-effective, privacy-preserving HVAC control framework that reduces communication and computation overhead, making it a practical solution for cloud-based building automation.
