An approach for building occupancy modelling considering the urban context
An approach for building occupancy modelling considering the urban context
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DOI：
10.1016/j.buildenv.2020.107126
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发表时间：
2020-10-01
影响因子：
7.4
通讯作者：
Polak, John
中科院分区：
文献类型：
--
作者：
Hou, Huiqiao;Pawlak, Jacek;Polak, John
建筑占用率反映了建筑空间内的居住者存在、移动和活动，是建筑能源建模和模拟中要考虑的关键因素。描述复杂的居住者行为及其决定因素从传感，建模，解释和预测的角度提出了挑战。过去的研究通常采用时间依赖模型来预测商业建筑的定期占用模式。然而，这种对纯粹的时间效应的普遍依赖通常不足以准确地描述复杂的占用模式，因为它们可能随着建筑物的周围条件（即城市环境）而变化。因此，本文提出了一个概念框架，将城市系统和建筑占用之间的相互作用。在此框架下，我们提出了一种新的建模方法，依靠竞争风险危害制定分析的案例研究建设占用在伦敦，英国。占用简档是根据从现有Wi-Fi基础设施提取的Wi-Fi连接日志推断的。当与传统的离散时间马尔可夫链模型（MCM）相比，基于危险的建模方法能够更好地捕捉的持续时间依赖的性质的转移概率，以及纳入和量化的影响，当地环境占用过渡。这项工作已经证明，这种方法可以方便和灵活地结合城市的依赖性，从而准确的占用预测，同时提供解释城市系统对建筑物占用的影响的能力。
Building occupancy, which reflects occupant presence, movements and activities within the building space, is a key factor to consider in building energy modelling and simulation. Characterising complex occupant behaviours and their determinants poses challenges from the sensing, modelling, interpretation and prediction perspectives. Past studies typically applied time-dependent models to predict regular occupancy patterns for commercial buildings. However, this prevalent reliance on purely time-of-day effects is typically not sufficient to accurately characterise the complex occupancy patterns as they may vary with building's surrounding conditions, i.e. the urban environment. Therefore, this paper proposes a conceptual framework to incorporate the interactions between urban systems and building occupancy. Under the framework, we propose a novel modelling methodology relying on competing risk hazard formulation to analyse the occupancy of a case study building in London, UK. The occupancy profiles were inferred from the Wi-Fi connection logs extracted from the existing Wi-Fi infrastructure. When compared with the conventional discrete-time Markov Chain Model (MCM), the hazard-based modelling approach was able to better capture the duration dependent nature of the transition probabilities as well as incorporate and quantify the influence of the local environment on occupancy transitions. The work has demonstrated that this approach enables a convenient and flexible incorporation of urban dependencies leading to accurate occupancy predictions whilst providing the ability to interpret the impacts of urban systems on building occupancy.