سال انتشار:
2020
عنوان انگلیسی مقاله:
A novel reinforcement learning method for improving occupant comfort via window opening and closing
ترجمه فارسی عنوان مقاله:
یک روش جدید یادگیری تقویتی برای بهبود راحتی سرنشین از طریق باز و بسته شدن پنجره
منبع:
Sciencedirect - Elsevier - Sustainable Cities and Society, 61 (2020) 102247. doi:10.1016/j.scs.2020.102247
نویسنده:
Mengjie Hana, Ross Maya, Xingxing Zhanga,*, Xinru Wanga, Song Panb, Da Yanc, Yuan Jinc
چکیده انگلیسی:
An occupants window opening and closing behaviour can significantly influence the level of comfort in the
indoor environment. Such behaviour is, however, complex to predict and control conventionally. This paper,
therefore, proposes a novel reinforcement learning (RL) method for the advanced control of window opening and
closing. The RL control aims at optimising the time point for window opening/closing through observing and
learning from the environment. The theory of model-free RL control is developed with the objective of improving
occupant comfort, which is applied to historical field measurement data taken from an office building in Beijing.
Preliminary testing of RL control is conducted by evaluating the control method’s actions. The results show that
the RL control strategy improves thermal and indoor air quality by more than 90% when compared with the
actual historically observed occupant data. This methodology establishes a prototype for optimally controlling
window opening and closing behaviour. It can be further extended by including more environmental parameters
and more objectives such as energy consumption. The model-free characteristic of RL avoids the disadvantage of
implementing inaccurate or complex models for the environment, thereby enabling a great potential in the
application of intelligent control for buildings.
Keywords: Markov decision processes | Reinforcement learning | Window control | Indoor comfort | Occupant
قیمت: رایگان
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