Reinforcement Learning for Control of Building HVAC Systems
Reinforcement Learning for Control of Building HVAC Systems
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DOI：
10.23919/acc45564.2020.9147629
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
N. Raman;Adithya M. Devraj;P. Barooah;Sean P. Meyn
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文献类型：
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作者：
N. Raman;Adithya M. Devraj;P. Barooah;Sean P. Meyn
我们提出了一种基于强化学习（RL）的控制器，用于商业建筑的节能气候控制。基于模型的控制技术，如模型预测控制（MPC）的这个问题是具有挑战性的实施，因为他们需要简单而准确的模型，这是很难获得的，由于复杂的建筑物及其HVAC系统的湿热动力学。RL是MPC的一个有吸引力的替代方案，因为一旦学习了策略，真实的时间计算控制涉及解决一个简单的低维优化问题，不涉及建筑物物理模型。然而，训练RL控制器是计算昂贵的，有许多设计选择，影响performance.We比较在模拟中提出的RL控制器，MPC控制器，并在实践中广泛使用的基线基于规则的控制器。RL和MPC控制器都能够保持温度和湿度约束，并且与基线相比，它们都显著减少了能源使用，尽管RL的节省小于MPC。
We propose a reinforcement learning-based (RL) controller for energy efficient climate control of commercial buildings. Model-based control techniques like model predictive control (MPC) for this problem are challenging to implement as they need simple yet accurate models, which are hard to obtain due to the complexity in hygrothermal dynamics of a building and its HVAC system. RL is an attractive alternative to MPC since once the policy is learned, computing the control in real time involves solving a simple low dimensional optimization problem that does not involve a model of building physics. However, training an RL controller is computationally expensive, and there are many design choices that affect performance.We compare in simulations the proposed RL controller, an MPC controller, and a baseline rule-based controller that is widely used in practice. Both the RL and MPC controllers are able to maintain temperature and humidity constraints, and they both reduce energy use significantly compared to the baseline, though the savings by RL is smaller than that by MPC.