Machine learning applications for a better demand controlled ventilation system experience in buildings: a review

IF 1.5 4区 经济学 0 ARCHITECTURE
Z. Ismail
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Abstract

PurposeAt the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).Design/methodology/approachThe method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.FindingsThe discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO2) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.Originality/valueFinally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO2 based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.
机器学习应用于建筑物中更好的需求控制通风系统体验:综述
随着2019冠状病毒病(COVID-19)大流行的开始,数字化建筑环境以一种称为需求控制通风(DCV)的现代输送系统的形式出现在供暖、通风和空调(HVAC)系统中。需求控制通风有可能解决建筑通风的最大问题,即管理室内空气质量(IAQ),以控制COVID-19在室内环境中的传播。然而,对密集人群活动感染预防的不当评估和信息管理,如测量误差和挥发性有机化合物(VOC)产生失败率,是碎片化的,因此本研究的目的是整合这一点,并探索机器学习算法(MLAs)的潜力。设计/方法论/方法使用的方法是一种彻底的系统文献综述(SLR)方法。研究结果包括对DCV系统的详细描述及其室内空气质量要素的数字化构建过程。研究结果表明,DCV有进一步整合的潜力,可以将其视为mla,从而从健康风险功能机制(即VOC和CO2)的角度管理室内空气质量水平,以保持公共和私人建筑(PPBs)舒适的热环境和节能。适当的MLA也可以根据季节变化、通风行为、建筑类型和位置以及当前室内空气污染控制策略的不同占用模式来选择。此外,概念框架表明,算法设计/模型预测控制(MPC)集成等MLA应用可以缓解COVID-19在室内环境中的高度传播限制。独创性/价值最后,研究得出结论,在DCV系统和mla中,集成和创新中存在巨大的未开发潜力,可以从整个建筑部门DCV过程系统的健康角度改进以优化室内空气质量水平。从DCV过程的通风控制初始阶段开始,通过进一步的研究和适应和实施经验,应考虑基于CO2的DCV的要求以及VOC浓度监测实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.30
自引率
18.20%
发文量
48
期刊介绍: The journal of an association of institues and individuals concerned with housing, design and development in the built environment. Theories, tools and pratice with special emphasis on the local scale.
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