## 3 Results and Discussion  
### Data Preprocessing  
Operational data from an E-class gas turbine power plant, spanning June 2022 to May 2023, were collected at a one-minute sampling frequency. The sample data of power are shown in Figure 3.  
As can be seen from Figure 3, the gas turbine starts and stops frequently. Since the steady-state process of gas turbines plays a crucial role in operational monitoring and performance evaluation, this study focuses on power prediction modeling for the steady-state process of gas turbines. Thus, data from dynamic processes like start-up, shutdown, and load changes should be excluded. The filtering method for steady-state data is described below in step (3) \"Steady-state discrimination\". After completing the data preprocessing process, the model training can be carried out. In this study, the preprocessing of data primarily includes sensor measurement data processing, unit conversion, steady-state discrimination, noise reduction, and outlier removal. The data processing is primarily based on Python, utilizing the numpy and pandas libraries to compute the mean, standard deviation, and variance of the data. 1. Sensor measurement data processing: In this process, median values are calculated from multiple sensors, and relative pressure measurements are converted to absolute pressure values according to the control logic of TCS. 2. Unit conversion: This process focuses on unifying units for the physical variables, such as standardizing pressure to Pascal (Pa) and temperature to degree Kelvin (K). 3. Steady-state discrimination: Steady-state processes are essential in assessing turbine performance. The modeling in this study is targeted at steady-state processes. Thus, data from dynamic processes like start-up, shutdown, and load changes should be excluded. Assuming that the task is to determine whether point \(P_{t}\) at time \(t\) is in a steady state, a data window of 10 min after time t would be taken, and the mean value \(\mu\) and standard deviation \(\sigma\) of measurement variables (e.g., IGV angles, compressor inlet temperature, power) during this period would be calculated. If the standard deviation \(\sigma\) exceeds 2% of the mean \(\mu\), point \(P_{t}\) is deemed a non-steady state. Furthermore, if the measurement variables' value of point \(P_{t}\) deviate from the corresponding mean \(\mu\) by more than twice the standard deviation \(\sigma\) (falling outside the 95% confidence interval), \(P_{t}\) is also considered as a non-steady point. 4. Noise reduction: In this process, the mean value \(\mu\) of data points within a 10-min window following a steady-state data point \(P_{t}\) is taken as the filtered value of point \(P_{t}\). Figure 3: Operational data from an E-class gas turbine power plant, before and after preprocessing. Outliers removal: Outliers are identified and removed based on the distribution of power values, excluding those that significantly differ from the main dataset. After data preprocessing, the dataset covers a wide range of steady-state operating conditions with varying parameters, such as \(\text{T}_{1}\in[276.0\,\text{K},316.1\,\text{K}]\), \(p_{1}\in[9.818\times 10^{4}\,\text{Pa},1.028\times 10^{5}\,\text{Pa}]\), \(\text{IGV angles}\in[56.98^{\circ},85.05^{\circ}]\), \(\text{P}_{w}\in[88.75\,\text{MW},132.15\,\text{MW}]\). As can be seen from Figure 3, the preprocessed data have excluded the start-stop process data and reduced the noise fluctuations in the power signal, resulting in a relatively smoother trend. After data preprocessing, a total of 15,848 valid data points were obtained, with 80% designated as the training set and 20% as the test set. These processed data form the foundation for subsequent model development and validation. After data processing, Pearson correlation analysis is conducted according to Equation (1), as depicted in Figure 4. The value of Pearson correlation coefficient \(P\) ranges from \(-1\) to \(1\), with a larger absolute value indicating a stronger correlation between two variables.