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%).

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%）。