## 4 Experimental Setup and Results
### Results and Analysis
Output graph of validation process after LSTM training. is integrated into a six-dimensional state vector for input into the DQN model, which incorporates additional real-time environmental variables and a user-specified temperature setpoint. Specifically, the input state vector to the DQN consists of [LSTM-predicted temperature, average humidity, indoor temperature, outdoor temperature, power consumption, and user-defined offset value]. The user offset and immediate setpoint values are configured manually via a JSON file and reflected instantaneously in the state vector. The DQN processes this state vector through Q-learning updates to compute an optimal compensation value, which is combined with the LSTM-predicted temperature using a weighted average to determine the final temperature setpoint (final_setpoint). Finally, the controller regulates the indoor temperature by activating the heater and air conditioner within predefined threshold ranges based on the remotely received final_setpoint. Figure 6a illustrates the terminal status window of the control section where the heater and air conditioner are installed. This terminal output displays the hourly measurements of power consumption, temperature, and humidity values recorded at different heights within the experimental warehouse (approximately a 70 cm vertical difference between channels). Additionally, it shows the operating status of the heater and air conditioner controlled by the hysteresis settings, along with the remote-control status via Modbus TCP. Figure 6b presents the status window reflecting the control actions executed based on the final temperature setpoint generated by the DQN algorithm. This includes temperature and humidity values collected from the experimental warehouse, operational status of the heater and air conditioner, the LSTM model's predicted temperature output from the AI main controller, the user-defined offset value, the selected control method, and the mean squared error (MSE) value. Figure 7a presents the Excel dataset, including hourly measurements of indoor average temperature (avg_temp), control target temperature (final_setpoint), humidity (avg_humidity), power consumption (power_watt), and the predicted temperature from the LSTM model (LSTM_predicted), recorded on 4 March 2025. Figure 7b illustrates the hourly variations of both the indoor average temperature and the target temperature (final_setpoint) for the same date, clearly showing that the controlled target and actual indoor temperatures exhibit highly similar trends. In particular, the two temperature curves closely align throughout most periods, confirming that the control system effectively tracks the desired temperature setpoint. However, during certain intervals with notable temperature fluctuations, the target temperature slightly precedes the actual temperature, which is a natural characteristic of prediction-based temperature control systems. Table 2 presents the AI-calculated final setpoint temperatures (final_setpoint) at approximately 1 h intervals starting from 00:00 on 4 March 2025, alongside the corresponding measured average indoor temperatures (avg_temp). According to the data, the final setpoint temperature gradually increased over time, peaking at 23.51 \({}^{\circ}\)C around 11:00 a.m. The actual measured indoor temperature closely followed this trend, consistently maintaining values within a slight margin of error (approximately \(\pm\)0.1 \({}^{\circ}\)C). This demonstrates the effectiveness of the implemented algorithm, as the DQN efficiently controls indoor temperature by integrating user-defined settings, power consumption data, humidity levels, and temperature predictions from the LSTM model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Timestamp** & **Final\_Setpoint** & **Avg\_Temp** \\ \hline
2025-03-04 00:13:42 & 21.62 & 21.65 \\
2025-03-04 01:13:47 & 21.65 & 21.65 \\
2025-03-04 02:13:49 & 21.66 & 21.71 \\ \hline \hline \end{tabular}
\end{table}