A deep reinforcement learning control method for multi-zone precooling in commercial buildings

IF 6.1 2区 工程技术 Q2 ENERGY & FUELS
Yuankang Fan , Qiming Fu , Jianping Chen , Yunzhe Wang , You Lu , Ke Liu
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引用次数: 0

Abstract

In commercial buildings, implementing precooling measures before office hours in summer can effectively meet the thermal comfort needs of employees. However, in multi-zone environments, differences in the cooling rates between regions often exacerbate the heat transfer interference between zones, increasing the complexity of the precooling system and leading to energy waste with limited cooling capacity. To overcome these challenges, we have developed a novel multi-zone precooling control method, which integrates deep reinforcement learning (DRL) to optimize the heat transfer process by adjusting the Air Handling Units (AHUs) valve openings, thus achieving uniform precooling across the building. Comparisons with traditional precooling control methods demonstrate the effectiveness of the proposed method. The results show that, under conventional conditions, compared with the rule-based control (RBC) and proportional integral derivative (PID) methods, the precooling time is reduced by 11.4% and 5.8%, respectively, the complexity of heat transfer is reduced by 77.6% and 64.1%, and energy consumption is reduced by 14.5% and 9.3%. In addition, the study analyzes the influence of environmental parameters on precooling optimization. The findings indicate that weather conditions have the most substantial impact on short-term precooling performance, followed by building thermal performance and cooling conditions.
商业建筑多区预冷的深度强化学习控制方法
在商业建筑中,在夏季办公时间之前实施预冷措施可以有效满足员工的热舒适需求。然而,在多区域环境中,区域间制冷速率的差异往往会加剧区域间的传热干扰,增加预冷系统的复杂性,并在制冷能力有限的情况下导致能源浪费。为了克服这些挑战,我们开发了一种新颖的多分区预冷控制方法,该方法集成了深度强化学习(DRL),通过调整空气处理机组(AHU)阀门开度来优化传热过程,从而实现整个楼宇的均匀预冷。与传统预冷控制方法的比较证明了所提方法的有效性。结果表明,在传统条件下,与基于规则的控制(RBC)和比例积分导数(PID)方法相比,预冷时间分别缩短了 11.4% 和 5.8%,传热复杂度分别降低了 77.6% 和 64.1%,能耗分别降低了 14.5% 和 9.3%。此外,研究还分析了环境参数对预冷优化的影响。研究结果表明,天气条件对短期预冷性能的影响最大,其次是建筑热性能和冷却条件。
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来源期刊
Applied Thermal Engineering
Applied Thermal Engineering 工程技术-工程:机械
CiteScore
11.30
自引率
15.60%
发文量
1474
审稿时长
57 days
期刊介绍: Applied Thermal Engineering disseminates novel research related to the design, development and demonstration of components, devices, equipment, technologies and systems involving thermal processes for the production, storage, utilization and conservation of energy, with a focus on engineering application. The journal publishes high-quality and high-impact Original Research Articles, Review Articles, Short Communications and Letters to the Editor on cutting-edge innovations in research, and recent advances or issues of interest to the thermal engineering community.
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