Effective control of energy consumption in public buildings holds paramount significance for global sustainable development. However, uncertainty in occupant behavior during operational phase often leads to a substantial discrepancy between the designed and actual energy consumption. While there is existing research on occupant behavior in public buildings utilizing stochastic modeling, statistical modeling, data mining, and agent-based modeling methods, the connotations and formation mechanisms have not been systematically revealed, rendering these developed models weak in applicability and reliability in practical scenarios. Reinforcement learning, which can perceive aspects of external environment and learn from stochastic interactions spontaneously, has been distinguished as a promising method for occupant behavior modeling. This research aims to develop a paradigm for reinforcement learning application in occupant behavior modeling in public buildings. Semi-structured interviews and literature review were combined to collect relevant information, while reasons for using reinforcement learning and applicable algorithms were discerned through qualitative analysis. Finally, detailed frameworks were proposed and validated through focus groups, offering a comprehensive guide that will serve as a reference for future research. This includes steps for collecting occupant-related data, selecting appropriate algorithms, and training and deploying reinforcement learning agents effectively. This research innovatively proposed a “panorama” of reinforcement learning for occupant behavior modeling in public buildings, laying a solid foundation for indoor environment and energy efficiency improvement in public building operations.

中文翻译：

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公共建筑中居住者行为建模的强化学习：为什么、什么以及如何？


有效控制公共建筑能源消耗对于全球可持续发展具有重要意义。然而，运行阶段居住者行为的不确定性往往会导致设计能耗与实际能耗之间存在巨大差异。虽然现有的公共建筑居住行为研究采用随机建模、统计建模、数据挖掘和基于主体的建模方法，但其内涵和形成机制尚未系统揭示，导致这些模型在实际中的适用性和可靠性较弱。场景。强化学习可以感知外部环境的各个方面并自发地从随机交互中学习，已被认为是一种有前途的乘员行为建模方法。本研究旨在开发强化学习在公共建筑居住者行为建模中的应用范例。结合半结构化访谈和文献综述收集相关信息，并通过定性分析找出使用强化学习和适用算法的原因。最后，通过焦​​点小组提出并验证了详细的框架，提供了一个全面的指南，为未来的研究提供参考。这包括收集乘员相关数据、选择适当算法以及有效训练和部署强化学习代理的步骤。该研究创新性地提出了强化学习对公共建筑居住者行为建模的“全景图”，为公共建筑运营中的室内环境和能源效率提升奠定了坚实的基础。