## 3 Results and Discussion
### Sensor Accuracy Analysis  
Given that field data are acquired through measurement instruments, instrument accuracy inherently influences model accuracy. This section analyzes the model's error distribution in the presence of measurement noise. Based on gas turbine instrumentation  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline
25\% & 0.6042\% & 0.2117\% & 0.1805\% & 0.1382\% & 0.1146\% & 0.09762\% & 0.08707\% & 0.08014\% & 0.07788\% & 0.07481\% \\
50\% & 2.477\% & 1.510\% & 1.150\% & 0.6873\% & 0.3876\% & 0.2310\% & 0.1870\% & 0.1706\% & 0.1646\% & 0.1606\% \\
75\% & 8.427\% & 9.023\% & 9.453\% & 7.603\% & 3.660\% & 1.933\% & 0.6306\% & 0.3787\% & 0.3272\% & 0.2946\% \\ MaxAPE & 21.12\% & 21.95\% & 22.05\% & 21.96\% & 21.60\% & 17.56\% & 4.035\% & 4.026\% & 3.274\% & 2.758\% \\ MAPE & 5.077\% & 4.872\% & 4.867\% & 4.449\% & 3.505\% & 1.544\% & 0.6677\% & 0.5062\% & 0.3341\% & 0.2346\% \\ \hline \end{tabular}
\end{table}
Table 5: Results of prediction errors on different training size.  
Figure 9: The violin plot of absolute percentage error on different training size.  
literature [34], the measurement accuracy specifications for each variable are presented in Table 6.  
Based on Table 6, random errors within their respective maximum allowable ranges were added to the measurement data of the seven input variables required for modeling in the test set. This generated a new set of input variable data containing random noise compared to the original test set input variables. These data were then input into the combined model to generate power prediction values. The error distribution between these predicted power values and actual measured values is shown in Figure 10.  
Figure 10 shows the error distribution of power predictions at different power points. Taking the 95 MW power point as an example, the prediction deviation of the combined model falls within \(\pm\)0.40 MW with 95% probability. For other power points, the prediction deviations are: \(\pm\)0.53 MW at 100 MW, \(\pm\)0.46 MW at 105 MW, \(\pm\)0.53 MW at 110 MW, \(\pm\)1.04 MW at 115 MW, \(\pm\)1.54 MW at 120 MW, \(\pm\)0.53 MW at 125 MW, and \(\pm\)0.28 MW at 130 MW.  
The relatively larger deviations at 115 MW and 120 MW may be attributed to these power levels primarily occurring during spring and autumn seasons and occasional winter conditions, resulting in fewer data points. Additionally, the greater fluctuations in environmental conditions like temperature and humidity during spring and autumn increase prediction difficulty.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Individual Instrument or Parameter** & **Max. Uncertainty** \\ \hline Barometric pressure & +0.05\% \\ Air inlet temperature & +0.2 K \\ Current transformer (IGV) & +0.2\% \\ Relative humidity & +2\% \\ Gas fuel temperature & +0.2 K \\ Gas fuel mass flow & +0.5\% \\ Exhaust pressure loss & +50 Pa \\ \hline \hline \end{tabular}
\end{table}
Table 6: Instrument maximum permissible uncertainties.  
Figure 10: Uncertainty of power prediction on different target power.  
Overall, even with sensor noise introduced, the combined model maintains high prediction accuracy and demonstrates stable performance within the measurement equipment's accuracy limits.