## 1 Introduction
Various studies examined control techniques for maintaining constant indoor temperatures and saving energy under changes in a wide range of external environments. [PERSON] et al. [9; 10] suggested an optimal on-off control strategy for improving the  
Figure 1: Comparison between fixed-pressure and cooling-load-estimation controls. AC utilization process in deserts with extremely hot weather conditions. The proposed control solution stated that Elman neural network-based estimators should be proposed to estimate the real value of outdoor temperatures, which allow the adaptive adjustment of indoor temperatures regardless of outdoor temperature changes. [PERSON] et al. [11] presented the relevance of the dynamic analysis of a building HVAC system and potential of the hardware-in-the-loop simulation approach in assessing actual part-load heat pump performance. The authors conducted simulations by collecting continuous real-world climate data from common dwellings in Pisa, Italy, and described methods for optimizing the performance of heat pump systems in buildings under intermittent conditions. [PERSON] et al. [12] presented a novel model predictive control (MPC) method developed for a separate sensible and latent cooling (SSLC) system with a dedicated outdoor air-system (DOAS) support, showing results compared with those of a conventional feedback-control-based building management system (BMS). MPC systems can achieve 18% and 20% energy efficiencies for single-coil AHUs and DOAS-enabled SSLCs, respectively, compared with single-coil air handling units (AHUs) controlled using a BMS. Moreover, MPC systems can improve indoor thermal comfort better than a BMS. [PERSON] et al. [13] conducted an experimental study on control methods by combining setpoint reset techniques with bilinear control techniques. The authors presented a method to efficiently reduce the on/off frequency of a cooler and simultaneously guarantee the robustness of the spatial temperature control in the control role. Moreover, [PERSON] et al. [14] presented HVAC system optimization control strategies related to fan coil unit (FCU) temperature control. The strategies included a dynamic FCU temperature setting, refrigerated tone conversion based on FCU temperature, and pump operating frequency regulation algorithms. The results of the study showed that the proposed strategy can achieve significant energy efficiency compared with HVAC systems operating at full load. [PERSON] et al. [15] reviewed methods investigated in scientific papers in recent years. Most studies focused on one of the following trends: improving \"classical\" control methods, the use of predictable controls based on models, and the use of intelligent control methods. [PERSON] et al. [16] proposed an alternative to the optimization of HVAC control systems. The authors implemented a multicriteria algorithm to integrate occupants' thermal comfort and HVAC systems' electricity consumption into the same function and optimized the operational control of HVAC systems. [PERSON] et al. [17] employed optimal control strategies using a genetic algorithm to maintain thermal comfort and indoor air quality (IAQ). Compared with the base control strategy of the fixed temperature setpoint control method, the method of adjusting the fresh air supply rate and air temperature in each zone demonstrated up to 30.4% energy efficiency during the summer period. [PERSON] et al. [18] presented a real-time optimal control strategy for variable air volume (VAV) air conditioning in HVAC systems using genetic algorithms and simulated large-scale office buildings. The authors set supplied air temperature and duct static pressure as control variables to provide optimal control for the VAV air conditioning system. In the previous study, the system control method was applied by controlling compressor revolutions per minute (RPM) so that evaporative and condensation pressures followed certain targets and controlled the flow rate of refrigerant.