Electricity usage of buildings (including offices, malls, and residential apartments) represents a significant portion of a nation's energy expenditure and carbon footprint. In the United States, the buildings' appliances consume 72% of the total produced electricity approximately. In this regard, cyber-physical system (CPS) researchers have put forth associated research questions to reduce cyber-physical building environment energy consumption by minimizing the energy dissipation while securing occupants' comfort. Some of the questions in CPS building include finding the optimal HVAC control, monitoring appliances' energy usage, detecting insulation problems, estimating the occupants' number and activities, managing thermal comfort, intelligently interacting with the smart grid. Various machine learning (ML) applications have been studied in recent CPS researches to improve building energy efficiency by addressing these questions. In this paper, we comprehensively review and report on the contemporary applications of ML algorithms such as deep learning, transfer learning, active learning, reinforcement learning, and other emerging techniques that propose and envision to address the above challenges in the CPS building environment. Finally, we conclude this article by discussing diverse existing open questions and prospective future directions in the CPS building environment research.

中文翻译：

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网络物理建筑环境中的趋势机器学习模型：一项调查

建筑物（包括办公室、商场和住宅公寓）的用电量占国家能源支出和碳足迹的很大一部分。在美国，建筑物的电器消耗了大约 72% 的总发电量。在这方面，网络物理系统 (CPS) 研究人员提出了相关的研究问题，以通过在确保居住者舒适度的同时最大限度地减少能量消耗来降低网络物理建筑环境的能源消耗。CPS 建筑中的一些问题包括找到最佳的 HVAC 控制、监控电器的能源使用、检测绝缘问题、估计居住者的数量和活动、管理热舒适度、与智能电网智能交互。在最近的 CPS 研究中已经研究了各种机器学习 (ML) 应用程序，以通过解决这些问题来提高建筑能源效率。在本文中，我们全面回顾和报告了 ML 算法的当代应用，如深度学习、迁移学习、主动学习、强化学习和其他提出和设想解决 CPS 建筑环境中上述挑战的新兴技术。最后，我们通过讨论 CPS 建筑环境研究中各种现有的开放性问题和未来的未来方向来总结本文。迁移学习、主动学习、强化学习和其他新兴技术提出并设想解决 CPS 建筑环境中的上述挑战。最后，我们通过讨论 CPS 建筑环境研究中各种现有的开放性问题和未来的未来方向来总结本文。迁移学习、主动学习、强化学习和其他新兴技术提出并设想解决 CPS 建筑环境中的上述挑战。最后，我们通过讨论 CPS 建筑环境研究中各种现有的开放性问题和未来的未来方向来总结本文。