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Occupancy data at different spatial resolutions: Building energy performance and model calibration
Applied Energy ( IF 11.2 ) Pub Date : 2021-01-21 , DOI: 10.1016/j.apenergy.2021.116492
Adrian Chong , Godfried Augenbroe , Da Yan

Occupancy is a significant area of interest within the field of building performance simulation. Through Bayesian calibration, the present study investigates the impact of the availability of different spatial resolution of occupancy data on the gap between predicted and measured energy use in buildings. The study also examines the effect of occupancy data on the quality of the constructed prediction intervals (PIs) using the Coverage Width-based Criterion (CWC) metric. CWC evaluates the PIs based on both their coverage (correctness) and width (informativeness). This investigation takes the form of an actual building case study, with nine months of hourly measured building electricity use, WiFi connection counts as a proxy for occupancy, and actual weather data. In general, the building energy model’s accuracy improves with the occupancy and plug-loads schedule derived from WiFi data. Specifically, the Coefficient of Variation Root Mean Square Error (CV[RMSE]) reduced from 37% to 24% with an exponential improvement in the PIs quality compared to the results obtained with ASHRAE 90.1 reference schedules. However, the increase in prediction accuracy shrank to 5% CV(RMSE) and a comparable CWC upon calibrating the base loads of the reference schedules. Increasing the spatial resolution from building aggregated to floor aggregated occupancy data worsened the CV(RMSE) and CWC, suggesting trade-offs between parameter uncertainty and model bias/inadequacy. These results contribute to our understanding of the interactions between model complexity, simulation objectives, and data informativeness, facilitating future discussions on the right level of abstraction when modeling occupancy.



中文翻译:

不同空间分辨率下的占用数据:建筑能效和模型校准

在建筑性能模拟领域中,占用是重要的关注领域。通过贝叶斯校准,本研究调查了占用数据的不同空间分辨率的可用性对建筑物中预计和实测能耗之间的差距的影响。这项研究还使用基于覆盖宽度的标准(CWC)指标,研究了占用数据对构建的预测间隔(PI)的质量的影响。CWC根据PI的覆盖率(正确性)和宽度(信息性)评估PI。这项调查以实际的建筑案例研究的形式进行,每小时测量9个月的建筑用电量,WiFi连接可作为占用量的代理,并提供实际的天气数据。一般来说,根据WiFi数据得出的占用率和插件负荷计划,建筑物能源模型的准确性得以提高。具体而言,与用ASHRAE 90.1参考时间表获得的结果相比,PI的质量呈指数级提高,变异系数的均方根误差(CV [RMSE])从37%降低到24%。但是,在校准参考计划的基本负荷后,预测准确性的提高缩小为5%CV(RMSE)和相当的CWC。将空间分辨率从建筑物汇总占用数据提高到楼层汇总占用数据,会使CV(RMSE)和CWC恶化,这表明在参数不确定性与模型偏差/不足之间进行权衡。这些结果有助于我们理解模型复杂性,模拟目标和数据信息性之间的相互作用,

更新日期:2021-01-22
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