## 5 Evaluation
### Experimental Results
#### 5.1.2 Validating Complex Inconsistencies  
We have seen in the previous evaluation that our Automaton has lower energy consumption in an ideal environment compared to previous approaches. This results in lower operating costs as well as an improved climate footprint. In our next experiment, we want to look at the causes of the reduced consumption. To do this, we look at the memory consumption of our FullCC (best full verification approach) for checking complex inconsistencies with that of the automaton. We focus our evaluation on inconsistencies of categories 3 and 4, because for validation of these content requirements, large amounts of data have to be stored and, if necessary, transformed in the respective approaches. We have seen in the previous evaluation that our Automaton has lower energy consumption in an ideal environment compared to previous approaches. This results in lower operating costs as well as an improved climate footprint. In our next experiment, we want to look at the causes of the reduced consumption. To do this, we look at the memory consumption of our FullCC (best full verification approach) for checking complex inconsistencies with that of the automaton. We focus our evaluation on inconsistencies of categories 3 and 4, because for validation of these content requirements, large amounts of data have to be stored and, if necessary, transformed in the respective approaches. This is not required for categories 1 and 2, resulting in a non-significant additional memory usage. Figure 12 depicts our results. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline _Approach_ & _Plant Size_ & \begin{tabular}{l} **Germany** \\ **366** \\ **gCO\_2e/kWh** \\ \end{tabular} & \begin{tabular}{l} **EU** \\ **262** \\ **gCO\_2e/kWh** \\ \end{tabular} & \
\begin{tabular}{l} **USA** \\ **379** \\ **gCO\_2e/kWh** \\ \end{tabular} &
\begin{tabular}{l} **World** \\ **441** \\ **gCO\_2e/kWh** \\ \end{tabular} \\ \hline \multirow{3}{*}{FullCC (all)} & small: & 461 g & 330 g & 478 g & 556 g \\  & medium: & 1730 g & 1239 g & 1792 g & 2085 g \\  & large: & 2884 g & 2065 g & 2987 g & 3475 g \\ \hline \multirow{3}{*}{Automaton} & small: & 353 g & 252 g & 365 g & 425 g \\  & medium: & 1322 g & 946 g & 1369 g & 1593 g \\  & large: & 2203 g & 1577 g & 2282 g & 2655 g \\ \hline \hline \end{tabular} Legend: ge-: Additional CO\(\varepsilon\) in plant of size small (\(\sim\)17.9 g), medium (\(\sim\)67.0 g), large (\(\sim\)111.6 g) in Germany. eu-: Additional CO\(\varepsilon\) in plant of size small (\(\sim\)12.8 g), medium (\(\sim\)47.9 g), large (\(\sim\)79.9 g) in EU. us\({}_{\text{x}}\): Additional CO\(\varepsilon\) in plant of size small (\(\sim\)18.6 g), medium (\(\sim\)69.4 g), large (\(\sim\)115.6 g) in USA. w\({}_{\text{x}}\): Additional CO\(\varepsilon\) in plant of size small (\(\sim\)21.6 g), medium (\(\sim\)80.7 g), large (\(\sim\)134.5 g) worldwide.  
\end{table} 
Table 2: _Cont._  
Figure 12: Memory usage during validation of category 3 and 4 inconsistencies. The table shows the RAM usage in mega-bytes (MB) in three scenarios with increasing data set size: (1) No inconsistencies, (2) Category 3 inconsistency, and (3) Category 4 inconsistency. As can be seen in the table, the memory consumption of the automated runs is very close to each other. The maximum value here is 0.33 MB for no inconsistency and nine messages. This value differs by almost 0.01 MB compared to four messages. The result shows that RAM usage can be kept almost constant by early validation. If a category 3 inconsistency occurs, the run can be terminated early. This is reflected in a peak of 0.31 MB for nine messages. Compared to the semantic approach, the automaton beats it by far. Even with four messages, there is a difference of about 11 MB. In addition, the usage increases with every incoming message. The growth suggests a logarithmic curve which is positive for operation on large data sets in real-world environments (e.g., compared to linear increase).