# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 2 Related Work  
In this section, we briefly survey the previous work on two subjects that are relevant to this paper. The first is the recent trend of applying artificial intelligence (AI) to the operation of HVAC systems, and the second is the development of sensorless HVAC operation methods. First, we examine AI techniques in the context of HVAC system design. For the control and management of the HVAC system, there have been two main threads of research on the application of AI to HVAC system control. The first is performance optimization of the HVAC system, and the second is fault management. For the former approach, various operating techniques using a DNN have been studied as methods for efficient HVAC system control. [PERSON] et al. [6] used an artificial neural network to predict energy use and optimize the energy supply as an optimization method. [PERSON] et al. [10] proposed a thermal load prediction technique that was more efficient than the Multi-Layer Perceptron (MLP) method by applying three hidden layers in a Recurrent Neural Network (RNN) approach. [PERSON] et al. [11] proposed a method of optimal energy supply for control and demand by minimizing energy use through a Deep Reinforcement Learning (DRL) method using two hidden layers. They introduced model-free and self-learning HVAC system control using their proposed method. [PERSON] et al. [12] applied reinforcement learning to a general room detection method to improve its performance. The method allows for the control of illumination through the user's smartphone and adjusts the illumination according to the light sensor value. Using a genetic algorithm, [PERSON] et al. [13] proposed the possibility of a zero-energy building with and without occupancy as a means to optimally control temperature set-points in different climates. [PERSON] et al. [14] introduced a method that achieved 4-5% energy savings and 10% CO\({}_{2}\) savings through HVAC control using deep reinforcement learning. For HVAC system fault management, artificial intelligence has been leveraged to perform fault diagnosis and fault prediction of the equipment. Conventional methods of modeling a facility and detecting faults operate on the basis of historical fault data. However, [PERSON] et al. [5] used an RNN model-based fault diagnosis technique with a time step of 1 to detect faults without such data. [PERSON] et al. [7] used a deep autoencoder [15] and Artificial Neural Network (ANN) to provide various data for fault detection in the industry, and its performance was twice as high as the conventional rule-based method. [PERSON] et al. [16] detected more than 95% of the faults in an Air Handling Unit (AHU) using five hidden layers with 200 neurons. [PERSON] et al. [17] also detected 97% of the faults in a variable refrigerant flow system using a DNN. This fault detection method enables the facility administrator to check the status and cause of the fault and to return the plant operation to its normal state quickly by effectively troubleshooting the problem. The deep learning method used for the operation and fault detection of the HVAC system uses a time-series RNN structure, rather than simply learning using multiple layers. Because the HVAC system is operated to achieve target control using the current state and creates the state of the future, all related data have time-series characteristics. This approach is also required to predict the static pressure in a duct in the study reported here. A few existing works have followed the sensorless operation research thread, although most of them have not been in the HVAC domain. In the field of motor control, sensorless control has been applied for a long time [18; 19; 20]. Recently, intelligent motor control using an RNN has been studied [21; 22]. Further research is underway to replace the difficult and expensive sensors installed in the field of wind and solar energy, which are new and renewable energy sources, by applying a prediction method using DNN. [PERSON] et al.