## 3 Experimentation and Results
In this experiment, for simplicity, \(c_{i}\) in Equation (2) is set to 1.5, which can be determined through an exhaustive search for the house environment. Also, \(P_{base}\) assumed and estimated according to Figure 6b,d is 150.0 watts. Parameters for the parallel computing accelerated GA are specified below. The population size, \(pop\_size\), is 250. The initial population is created randomly in bit strings, where the total length of each chromosome is 12. The roulette selection strategy is used, and for this proportional selection procedure raw fitness values based on the rank of evaluated chromosomes, rather than their raw fitness value, are scaled. The single-point crossover operator is conducted; the crossover fraction of the population to be evolved is 0.55. The bit-wise mutation operator is used; the mutation rate is 0.01. An elitist strategy that guarantees a total of top (\(0.05\times pop\_size\)) chromosomes to survive from their current population to the next population is also used. The maximum number of generations is 50. Finally, the fitness function is clarified as --\(E\), where \(E\) is the declared objective function shown in Equation (2). Figure 8 shows the evolutionary trajectory of the parallel computing accelerated GA to a disaggregated NIALM data instance. The resulting objective value obtained is 0.7. To the total 208 NIALM data instances where they are run over for load disaggregation, the parallel computing accelerated GA compared to a standard GA achieves, in terms of computation time, an acceleration of up to 3.49 \(\times\) (=19.57 \begin{table}
\begin{tabular}{c c} \hline
**Class** & **Load** \\ \hline
1 & fridge, state 1 \\ \hline
2 & fridge, state 2 \\ \hline
3 & htpc, state 1 \\ \hline
4 & washer dryer, state 1 \\ \hline
5 & washer dryer, state 2 \\ \hline
6 & dishwasher, state 1 \\ \hline
7 & dishwasher, state 2 \\ \hline
8 & kettle, state 1 \\ \hline
9 & other submeters, state 1 \\ \hline
10 & other submeters, state 2 \\ \hline
11 & other submeters, state 3 \\ \hline
12 & other submeters, state 4 \\ \hline \end{tabular}
\end{table} 
Table 2: Twelve load classes considered from the targeted electrical appliances in Table 1 and recognized. In order to evaluate the performance of the parallel computing accelerated GA in load recognition, we examined the ROC curves, TPR and FPR in Equations (3) and (4), where their Area under ROC (AUC) is shown. Table 3 tabulates the load recognition results obtained by the parallel computing accelerated GA applied on the class-imbalanced data, where the \(F_{1}\) score is shown to each load class. Table 4 also tabulates the load recognition results obtained by the parallel computing accelerated GA, where TPR vs. FPR is also shown to each load class. As shown in Table 4, the presented methodology got good load recognition results (against random guesses with AUCs of 0.5) for the fridge, kettle, and dishwasher, where activities of daily living (ADLs) [35] can be inferred from them for occupants in the house. The experimental results reported in this paper have shown the validity of the parallel computing accelerated GA-based NIALM for load disaggregation. In addition, the NIALM's achieved acceleration of up to 3.49\(\times\) has been shown. The parallel computing accelerated GA can exploit parallel computing and, thus, reduce computation time. Computation time will increase exponentially when a large amount(s) of NIALM data is run over for load disaggregation (Equation (2)) in a large-scale evaluation of NIALM. Parallel computing will be exploited massively and the computation time will, thus, be reduced drastically. As shown in Tables 3 and 4, the performance of the presented methodology in load recognition needs a significant improvement, where the presented methodology suffers from similar \(P\) where the concerned electrical appliances or the load combinations of the concerned electrical appliances are identical. It will be improved. The improvement is shown below.