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Simulating occupants' impact on building energy performance at different spatial scales

机译:模拟居住者在不同空间尺度上对建筑能源绩效的影响

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It is not evident that modeling the stochastic nature of occupants' presence and behavior at the building level is as important as at the room level. Given this line of inquiry, the question is: how should occupants be modeled at different spatial resolutions? How much difference does it make to annual building energy use predictions if: (1) industry assumptions or dynamic models are used, and (2) probabilistic or deterministic models are used? This paper explores the reliability of predicting lighting energy use and the ability to quantify uncertainty at different scales. To this end, the impacts of occupancy and lighting and window shade use behaviors on lighting use predictions at various scales are studied. For occupant modeling, stochastic and rule-based models and custom and standard schedules are used. The simulation results indicated that the whole-building lighting energy use predictions with the stochastic models approached a consistent value of 2.3 kWh/m(2) with office buildings larger than 100 offices. It shows that the impact of individual occupants diminishes as the office building size increases. This research concludes that the required office building size to provide a good approximation of the population is highly dependent on the occupant modeling approaches. The results reveal that the deterministic models (i.e. rule-based models and custom schedules) can reasonably represent occupants' impact on building energy performance at larger scales.
机译:尚不清楚的是,在建筑物级别上模拟居民的存在和行为的随机性与在房间级别上一样重要。考虑到这一点,问题是:应该如何在不同的空间分辨率下模拟居住者?如果满足以下条件,则对年度建筑能耗预测有多少不同:(1)使用行业假设或动态模型,并且(2)使用概率或确定性模型?本文探讨了预测照明能源使用的可靠性以及量化不同规模不确定性的能力。为此,研究了占用和照明以及窗帘使用行为对各种规模的照明使用预测的影响。对于乘员建模,使用随机和基于规则的模型以及自定义和标准时间表。仿真结果表明,对于具有超过100个办公室的办公楼,采用随机模型的整栋建筑的照明能源使用量预测值始终接近2.3 kWh / m(2)。它表明,随着办公楼规模的增加,个人居住者的影响会减小。这项研究得出的结论是,能够很好地逼近人口所需的办公楼规模在很大程度上取决于居住者的建模方法。结果表明,确定性模型(即基于规则的模型和自定义时间表)可以合理地代表住户在更大范围内对建筑能源绩效的影响。

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