# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 1 Introduction  
Of the global energy consumption, the energy used by buildings worldwide is over 40%, among which heating, ventilation, and air conditioning (HVAC) systems account for nearly 40-70% [1]. For this reason, much research has been carried out to improve the operating efficiency of the HVAC system. One essential component of these efforts is the sensor. For instance, researchers have found that energy savings can be obtained by interlocking the sensor in the building with the control device [2; 3; 4]. Furthermore, HVAC system faults can be diagnosed by analyzing the sensor data so that the energy waste and failure of the device can be prevented [5]. In recent years, studies have been actively attempting to integrate artificial intelligence technology with such systems to improve the intelligence and utilization of sensors. In particular, research is underway to reduce energy by forecasting the energy demand and supply [6], designing automatic fault detection technology using deep learning methods [5; 7], and developing smart sensors with artificial intelligence (AI) [8]. Because the data used to train the AI come from the sensors, they are essential for enhancing the intelligence of buildings. Therefore, an increasing number of sensors are expected to be used in future smart buildings. However, the pursuit of sensor-based intelligence should account for the fact that the benefits of using an increasing number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. For instance, each static pressure sensor used in the HVAC system costs 5500-600, and at least one static pressure sensor is required for each Air Handling Unit (AHU). Second, the sensors need to be physically connected and integrated into the HVAC system. This increases the complexity and operating cost of the system. Third, sensors require maintenance at additional costs. The sensors can be subject to faults or miscalibration, in which case they need to be repaired orreplaced. In some cases, the sensors are located in places that maintenance personnel cannot easily reach. It is not uncommon for an HVAC system to have many components situated near the ceiling or in ducts. Therefore, the holy grail of the sensor-based intelligent HVAC system should be the appropriate technology (AT) in terms of the number of sensors used. In the ideal scenario, we can use the minimum number of sensors required to achieve the intelligence that we need to operate the HVAC system. The target system in this paper is the Air Handling Unit (AHU) in the HVAC system. The AHU regulates and circulates the air, and its components include a blower, heating or cooling elements, filter racks or chambers, sound attenuators, and dampers [9]. It connects to a ductwork system that distributes the conditioned air throughout the building, after which the air returns to the AHU. The AHU needs fan control to blow conditioned air at an appropriate pressure. For this purpose, the AHU system needs to determine the static pressure in the path of the air flow. For this purpose, a static pressure sensor is installed in the ceiling duct that forms the air flow path, as shown in Figure 1. The air flow between the AHU and the room is depicted in Figure 2. The sensor reports the measured pressure inside the duct to the AHU so that the AHU can regulate the air flow using fan control if necessary. Because of its location in the system, however, the integration and the maintenance of the sensor can be burdensome and costly, in addition to the cost of the sensor itself. Therefore, in this paper, we investigate the possibility of removing the static pressure sensor from the target Air Handling Unit (AHU) system while achieving the level of predicted pressure values that are comparable to the measured values. In this paper, we propose a method to predict the operating dynamics of s HVAC system without a static pressure sensor through a deep learning approach.