Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning.
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优化能源需求响应的价格需要一个灵活的控制器，具有导航复杂环境的能力。我们提出了一种强化学习控制器，令人惊讶的是最小化其架构的修改。我们建议令人惊讶的最小化可用于提高学习速度，以利用人们在人民能源使用中的可预测性。我们的架构在模拟能源需求响应时表现良好。我们提出这种修改，以改善功能，并在大规模的实验中保存。
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