## 4 Comparison with Previous Studies  
In this section, the outcome of the current study is compared with results from literature. It has turned out that a simple one-to-one comparison should not be done, as the initial variables, the applications and the number of varied parameters as well as their variation range may be very different. In addition, [PERSON] et al. [27] and [PERSON] et al. [28] used a hypercube sampling and assumed  
Figure 12: Absolute maxima of the relative variation of the power for charging and discharging when varying the boundary and initial conditions.  
Figure 13: Absolute maxima of the relative variation of the global liquid fraction for charging and discharging when varying the boundary and initial conditions.  
a normal distribution of the varied parameter. In contrast, the analysis of [PERSON] et al. [30] and the study presented here are based on a simple variation with respect to a reference case. Nevertheless, a comparison will still give more insights into the interdependencies and may reveal details for the need for future research.  
Therefore, in Table 5, the most influential parameters are listed together with their assumed variation range for all studies mentioned above. On average, \(T_{\text{m}}\) is the most influential parameter in the studies from literature. Whereas, the heat loss is the most dominant parameter in this study. It is worth mentioning that the effect of heat loss is not investigated in the studies found in literature and might be distinct smaller due to proper insulation. A comparison of all studies that are investigating the influence on the mean power shows that the ranking of the most influential material properties is still different even though the influenced parameter is identical. In the present study, \(\rho\) and \(L\) are the most relevant material parameters and in the literature these are \(T_{\text{m}}\) and \(L\)[27] or \(\frac{\partial h}{\partial T}\) and \(\rho\) as well as \(T_{\text{m}}\)[30] (the ranking for the deviation of the mean power is somewhat different from the ranking based on the deviation of the mean absolute power). [PERSON] et al. [27] also studied, among other parameters, the time until the output temperature reaches a certain temperature threshold. Here, the ranking is again different from the one when looking at the mean power. The ranking given by [PERSON] et al. [28] is different for the heating energy demand than for the cooling demand and the ranking depends on the PCM layer thickness, too. The corresponding ranking in Table 5 is the one given in the conclusion section of the original paper [28].  
Not only the ranking of the most important parameters varies between and within the different studies but also the overall impact of the variations varies widely. [PERSON] et al. [28] report that the yearly energy demand for cooling the studied test cell changes by about \(\pm 10\%\) when all parameters are varied together. Moreover, for a variation of all parameters, [PERSON] et al. [27] have found that this causes differences in the mean power of about \(\pm 3.75\%\). In contrast, [PERSON] et al. [30] state that changing \(T_{\text{in}}\) by \(\pm 1\) K already leads to a shift of the mean absolute power by about \(\pm 11\%\). Similarly, the results of the current study revealed that a variation of \(\rho\) by \(\pm 10\%\) leads to changes in the mean power of about \(\pm 8\%\), and accounting for the heat losses or not leads to a difference in the mean power of \(19.4\%\) and \(13.5\%\) for charging and discharging, respectively.