# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 3 Predicting Static Pressure Using Deep Learning
### Input Data Characteristics  
In the HVAC system structure shown in Figure 4, the Air Handling Unit (AHU) has a supply fan and a return fan that blow air into the room and suck air out of it, respectively. Each fan is an inverter type and can adjust its speed. The speed of the supply fan is controlled by the static pressure of the supply duct and the set-point of static pressure, and the return fan operates at a similar speed to that of the supply fan. The amount and temperature of the wind supplied to the room are measured and regulated to maintain a set temperature for the room. Before developing the LSTM model, we identified the environmental factors that affect the static pressure in the duct and analyzed their characteristics. In particular, we determined whether these factors exhibit temporal variations or specific patterns. For instance, Figure 5 shows the set-point values of the static pressure and the other measured values for the 4 th floor data set, one of the three floors for which we accumulated data. In the figure showing data for the 4 th floor, the set-point pressure and the measured pressure are related. They are also related to the fan speed and the air flows into and out of the duct. The supply and return air temperature fluctuations are also related to the set-point  
Figure 4: Air Handling Unit (AHU) and the connected components in the system. Figure 3: Training and testing process used in this paper. temperature. When cooling or heating, the fan speed depends on the set-point temperature and the temperature of the returning air. The speed is higher when the temperature difference is larger. Thus, the direct factors that affect the static pressure in the duct are the set-point for the static pressure, the operating speed of the supply fan, the supply air volume, and the return air volume. The factors that affect the air volume are the set-point of the temperature, the current temperature (supply and return air), and the speed of the supply fan. These values are required inputs to the LSTM. We selected seven input parameters that directly or indirectly affect the system dynamics for the deep learning model. They are  
1. Set-point of the static pressure (Millimeter Aqua; mmAq),
2. Set-point of the temperature (\({}^{\circ}\)C),
3. Volume of the supply air (Cubic Feet per Minute; CFM),
4. Volume of the return air (CFM),
5. Temperature of the supply air (\({}^{\circ}\)C),
6. Temperature of the return air (\({}^{\circ}\)C),
7. Speed of the inverter fan (Hz). Except for the set-points of the temperature and static pressure, all data used are time-series data. To train the LSTM model, we used three historical data sets obtained from our HVAC system. The commonalities of these data sets are as follows:  
* All data sets are for a four-month period, from May through August, and were collected at 1-min intervals from 12:00 a.m. to 11:59 p.m.
* All data sets consist of operational data from spring (from May to June) and summer (from July to August). These two periods have different operational targets because the HVAC system only blows air in spring, but it supplies cold air in summer. Figure 5: The 4 th floor data set. All data sets were subjected to the HVAC system test at varying static pressures from 10 August to the end of August. On the other hand, the data sets come from different floors in the same building: floors 4, 5, and 6. The HVAC system structure of each floor is the same, but the designed capacity is set differently according to the amount of heat generated. The 4 th floor is an office space that generates the least heat, and the 6 th floor is a laboratory that generates the most heat. The 5 th floor is part laboratory and part office space, so the generated heat is between the heat produced on the 4 th and 6 th floors. Consequently, the static pressure set-point is determined differently. The static pressure is zero millimeter Aqua (mmAq) when the system is in the stopped state. Otherwise, the system is in operation.