In this paper, sensor calibration method using data-driven prediction models are developed to eliminate sensor error in the variable air volume (VAV) system. Indoor sensible loads and indoor carbon dioxide (CO₂) concentrations, which are the main factors necessary for sensor operation in a VAV system, were predicted using a data-driven model (artificial neural network). Using this prediction model, we developed a method to calibrate the sensor error by using it to derive a system model of the sensor that we want to calibrate. As a result of the performance evaluation of the indoor sensible load prediction model, MBE was −1.8% and Cv(RMSE) was 3.4%. The performance evaluation of CO₂ prediction models showed that MBE was −3.2% and Cv(RMSE) was 5.4%. In verification of sensor calibration method, it was confirmed that the error occurring in the sensor can be corrected through the application and verification of the method and procedure for calibrating the VAV terminal unit sensor using the prediction model. In addition, it was confirmed that the error can be corrected both in the case of a single error (CASE1 ∼ CASE3) in which an error occurs in one sensor and in the case of two or more multiple errors (CASE4 ∼ CASE7). In addition, the calibration of the sensor data was able to solve practical difficulties such as sensor replacement.

在本文中，开发了使用数据驱动预测模型的传感器校准方法，以消除可变风量 (VAV) 系统中的传感器误差。使用数据驱动模型（人工神经网络）预测室内显负荷和室内二氧化碳 (CO ₂ ) 浓度是 VAV 系统中传感器运行所必需的主要因素。使用这个预测模型，我们开发了一种校准传感器误差的方法，使用它来导出我们想要校准的传感器的系统模型。作为室内显负荷预测模型性能评估的结果，MBE为-1.8%，Cv(RMSE)为3.4%。_{CO 2}的性能评价预测模型显示 MBE 为 -3.2%，Cv(RMSE) 为 5.4%。在传感器校准方法的验证中，通过使用预测模型校准变风量终端单元传感器的方法和程序的应用和验证，确认了传感器中出现的误差可以得到纠正。此外，确认了在一个传感器中发生错误的单个错误（CASE1～CASE3）和两个或多个多个错误（CASE4～CASE7）的情况下都可以纠正错误。此外，传感器数据的校准能够解决传感器更换等实际困难。