# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 4 Experimental Evaluation  
The data sets from our HVAC system used for the proposed static pressure prediction were from three levels of an office building located in Seoul, Korea. Figure 13 shows the HVAC system configuration for the building. Except for the lobby (1 st floor), the internal structures from the 2 nd to the 6 th floor are almost the same. The difference is that the HVAC systems on the 2 nd and 3 rd floors use a constant air volume, whereas the HVAC systems on the 4 th and 6 th floors use a variable air volume. The 1 st, 2 nd, and 3 rd floors were designed as a public space for the lobby, library, and conference room, and all the installed HVAC systems use a constant air volume. This type of HVAC system does not control the speed of the fan according to the static pressure in the duct because it is operated with fixed speed fan speed. However, the 4 th, 5 th, and 6 th floors are designed for use as offices and laboratories, and a variable-airflow HVAC system is installed because the heat load differs depending on the occupants and the conducted experiment. For a variable-airflow HVAC system, the fan speed must be controlled according to the static pressure in the duct, as described in Section 1.  
The target building was built in January 2018, and the calibration of the HVAC facility and sensor values was carried out until April. After the residents moved in, the air volume was adjusted during the cooling/heating operation. Generally, the HVAC operational parameters of new buildings are adjusted for the spring, summer, and winter seasons for one year after the move-in. Therefore, in our experiment, the proposed deep learning model was developed and tested on the basis of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Option** & **Parameter Setting** \\ \hline Batch size & 72 \\ Time step & 10 \\ Training stop strategy & Early stopping \\ Loss function & MAE (Mean Absolute Error) \\ Training method & Adam optimizer \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameter setting detail of the proposed model.  
Figure 13: Heating, Ventilation, and Air Conditioning (HVAC) system structure of target building.  
operational data from May to August. The reason that we used the data from this period is as follows. During spring (from March to May) and autumn (from September to November) in Korea, the HVAC system performs only the wind blowing operation. In summer (from June to August) and winter (from December to February), the cooling or the heating valve is opened for the cooling or heating operation, but, in these two seasons, the operation method for supplying the wind according to the set-temperature is the same. Therefore, in our experiment, the proposed model was developed and tested by using data from spring and summer (May-August) after the correction period ended in April. The model can also be used if the number of residents or usage purposes change between the design time and move-in. These changes can cause the air conditioning system to operate incorrectly. The operation test is performed to identify these changes and to set the optimal operating parameters using data such as user claims and the temperature and humidity inside the building. From 15 August to the end of August, the operation test was performed by the operator. During this time, the operation test was conducted while changing the set-point static pressure and the set-point temperature. In this study, we tested whether the proposed model could predict the static pressure during this period.  
All input data items consist of the seven measured values defined in Section 3.1. As shown in Figure 9, we sequentially inputted data from the 10 previous time steps (from \(t-9\) through \(t\)) to predict the static pressure and thereby estimate the static pressure at time step \(t\). The training, validation, and test data periods used in each experiment are shown in Table 3.