## 3 Experimentation and Results
Figure 8: Obtained evolutionary trajectory by the parallel computing accelerated GA in this experiment. The performance of the parallel computing accelerated GA in load recognition needs a significant improvement, although the algorithm is capable of recognizing the fridge/freezer, kettle, and dishwasher in the house environment for ADLs. A comparative study is conducted below, where a feed-forward, multilayer ANN as neurocomputing against evolutionary computing is used for the addressed NIALM problem. A network configuration of 1-15-12 of a feed-forward, multilayer ANN in Figure 5a is constructed, specified, and used, in this experiment, to address the same 208 NIALM data instances used before. The feed-forward, multilayer ANN was trained on 135 randomly sampled training data instances (~65% of the whole dataset) and tested on the remaining 73 test data instances. The training trajectory of the constructed, specified and used feed-forward, multilayer ANN is shown in Figure 9. The resulting mean squared error (MSE) is 0.055 (its initial MSE is 0.891). Table 5 tabulates the load recognition results obtained by the feed-forward, multilayer ANN, which has been well trained and validated on the class-imbalanced training data. In Table 5, the \(F_{1}\) score is shown to each load class. Table 6 also tabulates the load recognition results obtained by the well-trained and -tested feed-forward, multilayer ANN applied on the class-imbalanced test data. In Table 6, TPR vs. FPR is also shown to each load class. The authors of [24] used the benchmark implementations, from NILMTK in [36], of the combinatorial optimization (CO) approach, which was developed in [1], as a load recognizer to perform load disaggregation for the UK-DALE dataset. A comparison among the CO, the parallel computing accelerated GA, and the feed-forward, multilayer ANN for load recognition is shown in Table 7. As shown in Tables 3-7, the feed-forward, multilayer ANN outperforms, in terms of load \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **FPR** & **TPR** & **AUC** \\ \hline
1 & 0.56 & 0.81 & 0.63 \\
2 & 0.11 & 0.00 & 0.45 \\
3 & 0.54 & 0.47 & 0.46 \\
4 & 0.11 & 0.04 & 0.46 \\
5 & 0.15 & 0.00 & 0.43 \\
6 & 0.07 & 0.14 & 0.53 \\
7 & 0.15 & 0.36 & 0.61 \\
8 & 0.12 & 1.00 & 0.94 \\
9 & 0.53 & 0.57 & 0.52 \\
10 & 0.24 & 0.29 & 0.53 \\
11 & 0.10 & 0.00 & 0.45 \\
12 & 0.09 & 0.14 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Obtained TPRs vs. FPRs, for ROC curves, by the parallel computing accelerated GA, where for the 12 classes the AUCs obtained are also shown. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & \(\boldsymbol{F_{1}}\)** **Score 1** & **Number of Appliance Instances** \\ \hline
1 & 0.36 & 0.81 & 0.50 & 58 \\
2 & 0.00 & 0.00 & 0.00 & 25 \\
3 & 0.39 & 0.47 & 0.42 & 88 \\
4 & 0.05 & 0.04 & 0.04 & 26 \\
5 & 0.00 & 0.00 & 0.00 & 68 \\
6 & 0.57 & 0.14 & 0.22 & 87 \\
7 & 0.12 & 0.36 & 0.18 & 11 \\
8 & 0.14 & 1.00 & 0.24 & 4 \\
9 & 0.48 & 0.57 & 0.52 & 97 \\
10 & 0.28 & 0.29 & 0.29 & 51 \\
11 & 0.00 & 0.00 & 0.00 & 7 \\
12 & 0.25 & 0.14 & 0.18 & 35 \\ Avg./total & 0.32 & 0.33 & 0.29 & 557 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Load recognition results obtained by the parallel computing accelerated GA.  
recognition, the parallel computing accelerated GA that is slightly superior in load recognition to the CO. \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & \(\boldsymbol{F_{1}}\) **Score** & **Number of Appliance Instances** \\ \hline
1 & 0.87 & 0.77 & 0.82 & 52 \\
2 & 0.38 & 0.75 & 0.50 & 4 \\
3 & 0.80 & 0.74 & 0.77 & 38 \\
4 & 0.75 & 0.67 & 0.71 & 9 \\
5 & 0.88 & 1.00 & 0.93 & 7 \\
6 & 0.25 & 0.33 & 0.29 & 3 \\
7 & 0.69 & 0.92 & 0.79 & 12 \\
8 & 0.92 & 0.79 & 0.85 & 14 \\
9 & 0.78 & 0.94 & 0.85 & 33 \\
10 & 0.96 & 0.85 & 0.90 & 26 \\
11 & 0.75 & 0.67 & 0.71 & 9 \\
12 & 0.50 & 0.25 & 0.33 & 4 \\ Avg./total & 0.81 & 0.79 & 0.79 & 211 \\ \hline \hline \end{tabular}
\end{table}