论文标题
从数据驱动的建筑物控制实验中学到的经验教训：对比基于高斯的MPC，双杆DEEPC和深钢筋学习
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning
论文作者
论文摘要
该手稿提供了实验者对许多现代数据驱动技术的观点：依靠高斯过程，基于行为理论的自适应数据驱动控制以及深入的强化学习的模型预测控制。根据数据要求，易用性，计算负担和鲁棒性在现实世界应用程序的背景下进行比较。我们的言论和观察源于在不同环境中的建筑控制领域进行的许多实验研究，从讲座大厅和公寓空间到医院手术中心。最终目标是支持其他人确定哪种技术最适合解决自己的问题。
This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning. These techniques are compared in terms of data requirements, ease of use, computational burden, and robustness in the context of real-world applications. Our remarks and observations stem from a number of experimental investigations carried out in the field of building control in diverse environments, from lecture halls and apartment spaces to a hospital surgery center. The final goal is to support others in identifying what technique is best suited to tackle their own problems.
