Deep-Learning-Based Natural Ventilation Rate Prediction with Auxiliary Data in Mismeasurement Sensing Environments

Predicting the amount of natural ventilation by utilizing environmental data such as differential pressure, wind, temperature, and humidity with IoT sensing is an important issue for optimal HVAC control to maintain comfortable air quality. Recently, some research has been conducted using deep learning to provide high accuracy in natural ventilation prediction. Therefore, high reliability of IoT sensing data is required to achieve predictions successfully. However, it is practically difficult to predict the accurate NVR in a mismeasurement sensing environment, since inaccurate IoT sensing data are collected, for example, due to sensor malfunction. Therefore, we need a way to provide high deep-learning-based NVR prediction accuracy in mismeasurement sensing environments. In this study, to overcome the degradation of accuracy due to mismeasurement, we use complementary auxiliary data generated by semi-supervised learning and selected by importance analysis. That is, the NVR prediction model is reliably trained by generating and selecting auxiliary data, and then the natural ventilation is predicted with the integration of mismeasurement and auxiliary by bagging-based ensemble approach. Based on the experimental results, we confirmed that the proposed method improved the natural ventilation rate prediction accuracy by 25% compared with the baseline approach. In the context of deep-learning-based natural ventilation prediction using various IoT sensing data, we address the issue of realistic mismeasurement by generating auxiliary data that utilize the rapidly changing or slowly changing characteristics of the sensing data, which can improve the reliability of observation data.