سال انتشار:
2020
عنوان انگلیسی مقاله:
The reinforcement learning method for occupant behavior in building control: a review
ترجمه فارسی عنوان مقاله:
روش یادگیری تقویتی برای رفتار ساکنین در کنترل ساختمان: یک بررسی
منبع:
Sciencedirect - Elsevier - © 2020 Published by Elsevier B.V.
نویسنده:
Mengjie Han , Jing Zhao , Xingxing Zhang , Jingchun Shen , Yu Li
چکیده انگلیسی:
Occupant behavior in buildings has been considered the major source of uncertainty for
assessing energy consumption and building performance. Modeling frameworks are usually
built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to
apply that experience to a similar but distinct environment. For complex and dynamic
environments, the development of smart devices and computing power makes intelligent
control methods for occupant behaviors more viable. It is expected that they will make a
substantial contribution to reducing global energy consumption. Among these control
techniques, the reinforcement learning (RL) method seems distinctive and applicable. The
success of the reinforcement learning method in many artificial intelligence applications has
given an explicit indication of how this method might be used to model and adjust occupant
behavior in building control. Fruitful algorithms complement each other and guarantee the
quality of the optimization. However, the examination of occupant behavior based on
reinforcement learning methodologies is not well established. The way that occupant
interacts with the RL agent is still unclear. This study briefly reviews the empirical
applications using reinforcement learning, how they have contributed to shaping the
modeling paradigms and how they might suggest a future research direction.
Keywords: Reinforcement learning | occupant behavior | energy efficiency | building control | smart building
قیمت: رایگان
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