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Smart choice or flawed approach? An exploration of connected thermostat data fidelity and use in data-driven modelling in high-rise residential buildings
Journal of Building Performance Simulation ( IF 2.2 ) Pub Date : 2021-05-29 , DOI: 10.1080/19401493.2021.1927189
Helen Stopps 1 , Marianne Touchie 1, 2
Affiliation  

Connected thermostat data provide new opportunities to access heating, ventilation and air conditioning (HVAC) operation and indoor condition data in high-rise residential buildings. However, how well these thermostat data reflect actual conditions and operation is unclear and best-practices to leverage these data for energy use modelling are needed. Connected thermostat data from 54 suites in two high-rise residential buildings are used to investigate the accuracy of thermostat-reported suite condition and HVAC runtime data and the relationship between suite-HVAC runtime and thermal energy demand. Next, data-driven approaches for forecasting suite HVAC runtime are explored. Two key challenges when using these data for energy modelling were identified. First, while a linear relationship between HVAC runtime and thermal energy demand was observed, there was significant variation in this relationship between suites. Second, the simple, data-driven regression methods tested were largely ineffective in accurately predicting suite-level HVAC runtime for hourly intervals (average error: 30%).



中文翻译:

明智的选择还是有缺陷的方法?连接恒温器数据保真度的探索及其在高层住宅建筑数据驱动建模中的应用

联网恒温器数据为访问高层住宅建筑的供暖、通风和空调 (HVAC) 运行和室内条件数据提供了新的机会。然而,这些恒温器数据如何反映实际条件和操作尚不清楚,需要利用这些数据进行能源使用建模的最佳实践。来自两座高层住宅建筑的 54 间套房的连接恒温器数据用于研究恒温器报告的套房状况和 HVAC 运行时间数据的准确性以及套件-HVAC 运行时间与热能需求之间的关系。接下来,将探索用于预测套件 HVAC 运行时间的数据驱动方法。确定了使用这些数据进行能源建模时的两个关键挑战。首先,虽然观察到 HVAC 运行时间和热能需求之间的线性关系,套房之间的这种关系存在显着差异。其次,测试的简单、数据驱动的回归方法在准确预测每小时间隔的套件级 HVAC 运行时间方面基本上是无效的(平均误差:30%)。

更新日期:2021-05-29
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