سال انتشار:
2020
عنوان انگلیسی مقاله:
Model-free control method based on reinforcement learning for building cooling water systems: Validation by measured data-based simulation
ترجمه فارسی عنوان مقاله:
روش کنترل بدون مدل مبتنی بر یادگیری تقویتی برای برای ساخت سیستم های آب خنک کننده : اعتبار سنجی با شبیه سازی مبتنی بر داده اندازه گیری شده
منبع:
Sciencedirect - Elsevier - Energy & Buildings, 218 (2020) 110055. doi:10.1016/j.enbuild.2020.110055
نویسنده:
Shunian Qiu a , Zhenhai Li a , Zhengwei Li a , b , ∗, Jiajie Li a , Shengping Long c , Xiaoping Li d
چکیده انگلیسی:
In the domain of optimal control for building HVAC systems, the performance of model-based control
has been widely investigated and validated. However, the performance of model-based control highly depends
on an accurate system performance model and sufficient sensors, which are difficult to obtain for
certain buildings. To tackle this problem, a model-free optimal control method based on reinforcement
learning is proposed to control the building cooling water system. In the proposed method, the wet bulb
temperature and system cooling load are taken as the states, the frequencies of fans and pumps are the
actions, and the reward is the system COP (i.e., the comprehensive COP of chillers, cooling water pumps,
and cooling towers). The proposed method is based on Q-learning. Validated with the measured data
from a real central chilled water system, a three-month measured data-based simulation is conducted
under the supervision of four types of controllers: basic controller, local feedback controller, model-based
controller, and the proposed model-free controller. Compared with the basic controller, the model-free
controller can conserve 11% of the system energy in the first applied cooling season, which is greater than
that of the local feedback controller (7%) but less than that of the model-based controller (14%). Moreover,
the energy saving rate of the model-free controller could reach 12% in the second applied cooling
season, after which the energy saving rate gets stabilized. Although the energy conservation performance
of the model-free controller is inferior to that of the model-based controller, the model-free controller
requires less a priori knowledge and sensors, which makes it promising for application in buildings for
which the lack of accurate system performance models or sensors is an obstacle. Moreover, the results
suggest that for a central chilled water system with a designed peak cooling load close to 20 0 0 kW, three
months of learning during the cooling season is sufficient to develop a good model-free controller with
an acceptable performance.
Keywords: Cooling water system | Cooling tower | Cooling water pump | Optimal control | Reinforcement learning | Model-free control
قیمت: رایگان
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