## 4 Experimental Setup and Results
### Experimental Setup  
To conduct this study, a 3.3-square-meter prefabricated test space was used. Within this space, a compact air conditioner (AC 220 V, 2.6 A, 550 W), an electric heater (AC 220 V, 4.5 A, 1000 W), and a circulation fan (5 V, 0.6 A, 3 W) were installed to replicate typical indoor conditions. These devices were controlled by an NVIDIA Jetson Orin Nano 8 GB, which functioned as the central AI controller operating on Ubuntu 22.04 with JetPack 6.1 SDK. This setup enabled real-time processing and integration of various HVAC control functions. Temperature and humidity sensors were installed throughout the experimental space to continuously monitor average environmental conditions. Current and voltage sensors were also implemented to measure power consumption in real time, enabling comprehensive energy tracking during system operation. A total of 8761 data points collected during the testing phase were converted into Excel files and subsequently used for AI model training, providing a robust dataset to refine the control algorithms. Additionally, a hysteresis control mechanism with a \(\pm\)1 \({}^{\circ}\)C tolerance around a 25 \({}^{\circ}\)C setpoint was implemented to minimize unnecessary cycling of equipment. This hysteresis mechanism effectively reduced frequent ON/OFF cycles, decreasing mechanical wear, extending component lifespan, and mitigating potential power surges. The hysteresis control mechanism was integrated into the system to manage real HVAC operations by leveraging prediction results obtained from the LSTM model. Control optimization was conducted in real time by directly incorporating sensor data into the AI decision-making process. The AI algorithm, deployed on the NVIDIA Jetson Orin Nano, processed sensor data in real time to dynamically adjust the control parameters. Throughout this process, real-time inference speed was significantly improved by applying model optimization techniques utilizing TensorRT, thereby ensuring rapid and accurate control decisions. The use of TensorRT was particularly crucial for enhancing the AI model's responsiveness in dynamic indoor environments, where prompt adjustments are essential. Python was chosen for data processing and AI model implementation because it provides a wide range of libraries suitable for machine learning tasks. Low-level driver programming was conducted using C/C++, facilitating real-time data acquisition and precise device control, thus providing the required efficiency and performance. NVIDIA's \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Component** & **Technology/Tool** & **Purpose** \\ \hline Outdoor Dataset (External) & 8761 hourly average temperature measurements (Korea Meteorological Administration) & External dataset used for temperature analysis from January to December 2023. \\ \hline Sensors & Custom Temperature, Humidity, Voltage, and Current Sensors & Sensors with \(\pm\)0.2 \({}^{\circ}\)C for temperature, \(\pm\)3\% RH for humidity, and \(\pm\)1\% for voltage and current error margins. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._CUDA framework was implemented for parallel processing, significantly accelerating computations within neural network models. Additionally, Docker containers were utilized to maintain modular independence and software portability, enabling seamless integration and deployment of various components across different platforms and substantially reducing overall development complexity. In the figures below, Figure 3a,b display the overall setup to carry out this work. Equipped with air conditioning units, monitoring sensors, temperature control devices, heaters, blowers, and a system for real-time monitoring and data analysis, this setup is used for AI-based climate and temperature control.