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Optimal control of HVAC and window systems for natural ventilation through reinforcement learning

机译：通过强化学习对自然通风的HVAC和窗户系统进行最佳控制

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摘要

Natural ventilation is a green building strategy that improves building energy efficiency, indoor thermal environment, and air quality. However, in practice, it is not always clear when and how to utilize the natural ventilation and coordinate its operation with the HVAC system. This paper introduces a reinforcement learning control strategy, specifically through model-free Q-learning, that makes optimal control decisions for HVAC and window systems to minimize both energy consumption and thermal discomfort. This control system evaluates the outdoor and indoor environments (temperature, humidity, solar radiation, and wind speed) at each time step, and responds with the best control decision that targets both immediate and long-term goals. The reinforcement learning control is evaluated through numerical simulation on a building thermal model and compared with a rule-based heuristic control strategy. Case studies in hot-and-humid Miami and warm-and-mild Los Angeles demonstrated the superior performance of reinforcement learning control, which led to 13% and 23% lower HVAC system energy consumption, 62% and 80% lower discomfort degree hours, and 63% and 77% fewer high humidity hours compared to heuristic control. Unlike heuristic control that requires specific knowledge of individual buildings and the creation of exhaustive decision-making scenarios to improve performance, reinforcement learning control guarantees optimality through self-advancement on given goals and cost functions and is able to adapt to stochastic occupancy and occupant behaviors, which is difficult to accommodate by heuristic control. (C) 2018 Elsevier B.V. All rights reserved.
机译：自然通风是一种绿色建筑策略，可以提高建筑的能效，室内热环境和空气质量。然而，在实践中，并不总是清楚何时以及如何利用自然通风并与HVAC系统协调其运行。本文介绍了一种强化学习控制策略，特别是通过无模型的Q学习，该策略可为HVAC和窗户系统做出最佳控制决策，以最大程度地减少能耗和热不适感。该控制系统在每个时间步评估室外和室内环境（温度，湿度，太阳辐射和风速），并以针对近期和长期目标的最佳控制决策作为响应。通过对建筑物热模型的数值模拟来评估强化学习控制，并将其与基于规则的启发式控制策略进行比较。在热湿的迈阿密和温和的洛杉矶进行的案例研究表明，强化学习控制具有卓越的性能，从而使HVAC系统的能耗降低了13％和23％，不适度小时数降低了62％和80％，与启发式控件相比，高湿度小时减少了63％和77％。与启发式控制不同，启发式控制需要特定的建筑物知识和详尽的决策场景来提高性能，而强化学习控制则通过对给定的目标和成本函数进行自我推进来确保最优性，并且能够适应随机占用和占用行为，这很难通过启发式控制来解决。 （C）2018 Elsevier B.V.保留所有权利。

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  • 来源
    《Energy and Buildings》 |2018年第6期|195-205|共11页
  • 作者

    Chen Yujiao; Norford Leslie K.; Samuelson Holly W.; Malkawi Ali;

  • 作者单位

    Harvard Grad Sch Design, Cambridge, MA 02138 USA;

    MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA;

    Harvard Grad Sch Design, Cambridge, MA 02138 USA;

    Harvard Grad Sch Design, Cambridge, MA 02138 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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