## 3 Results and Discussion
### Survey of Different Infill Strategies  
Different sequential sample infill strategies are compared in order to determine the most effective infill strategies for the suggested strategy. The original LCB [15] and the often used Expected improvement (EI) and extensions of EI (weighted EI, WEI), which have received a lot of attention over the last 10 years [34], are explored and compared with the suggested PLCB. The original LCB, EI, and weighted EI computations as well as the PLCB regulating functions as expressed in Equations (15)-(18) are also carried out in the Matlab environment. For each strategy, the same initial samples are located using the LHD method. PLCB, LCB, EI, and weighted EI are used to implement the sequential sampling technique. For simplicity, EI infill strategy within the proposed adaptive Kriging optimization is abbreviated to EI-AKO. Similar phrases include LCB-AKO for original LCB and WEI-AKO for weighted EI in adaptive Kriging optimization. Each technique was used 30 times separately to remove chance. Table 4 shows the mean and variance of normalized optimization outcomes obtained using various methods, where PLCB-1, PLCB-2, PLCB-3, and PLCB-4, respectively, stand for the controlling formulations in Equations (12)-(15). Figure 8 shows a box plot of the experimental data. The proposed PLCB-1-AKO approach clearly achieved the minimal mean of optimization. Figure 9 depicts the lifetime power generation of different schemes when the cut-off temperature of current generation is 393.15 K. As the result of the adaptive Kriging-based method will not be exactly the same during each run, the average of total power generation obtained via EI-AKO, PLCB-AKO, WEI-AKO are demonstrated in Figure 9, which indicates little difference in total power generation, namely, 4.13 \(\times\) 109 MJ, 4.23 \(\times\) 109 MJ, 4.29 \(\times\) 109 MJ, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **EI-AKO** & **WEI-AKO** & **PLCB-1-AKO** & **PLCB-2-AKO** & **PLCB-3-AKO** & **PLCB-4-AKO** \\ \hline Mean & 0.6296 & 0.6399 & 0.4975 & 0.6144 & 0.6037 & 0.5852 \\ Variance & 0.0366 & 0.0359 & 0.0238 & 0.0326 & 0.0339 & 0.0304 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean and variance via different approaches. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Size** & **Diff. Error Criteria** & **Kriging in KBO** & **Kriging in PLCB-AKO** \\ \hline \multirow{3}{*}{50} & RRMSE & 0.0682 & - \\  & RMAE & 0.2013 & - \\  & RAAE & 0.0398 & - \\ \hline \multirow{3}{*}{29(20+9)} & RRMSE & - & 0.0298 \\  & RMAE & - & 0.1632 \\ \cline{1-1}  & RAAE & - & 0.0354 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction accuracy comparison of Kriging in PLCB-AKO and KBO. The six aforementioned solutions are contrasted with one another in order to discover the optimal sample infill technique. The six sample infill procedures were subjected to the Friedman test [35; 36], a well-known and often-employed non-parametric statistical test suited for multiple comparisons. Table 5 lists the results of the Friedman test's individual rankings and p-value calculations. Table 5 obviously demonstrates that the PLCB-1-AKO technique has a higher optimization statistical outcome among the evaluated options. The PLCB-1-AKO approach that has been suggested is suitable for the problem of optimizing heat generation performance. Figure 8: Box plot of normalized optimization results through different approaches. Figure 9: Lifetime power generation of different schemes. The Wilcoxon signed ranks tests [35; 37], a non-parametric statistical test for pairwise comparisons, were adopted in this work to further investigate which of the five infill strategies is statistically different with respect to the suggested adaptive Kriging-based heat production performance optimization approach. The Wilcoxon signed ranks test findings for a pairwise comparison of the six alternative techniques are presented in Table 6 as a summary.