## 3 Experimentation and Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Electrical** & \multicolumn{2}{c}{**State 1**} & \multicolumn{2}{c}{**State 2 3**} & \multicolumn{2}{c}{**State 3**} & \multicolumn{2}{c}{**State 4**} \\
**Appliance** & \multicolumn{2}{c}{**Mean 1**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{2}{c}{**Deviation 2**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{2}{c}{**Deviation**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Standard**} \\
**fridge** & 88.8 & 43.7 & 245.5 & 16.4 & - & - & - & - \\ \hline
**hpc** & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\
**(home theatre PC)** & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\
**washer dryer** & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\
**dishwasher** & 116.0 & 15.1 & 2309.3 & 27.3 & - & - & - & - \\ \hline
**kettle** & 2323.7 & 132.6 & - & - & - & - & - & - \\ \hline
**other submeters** & 17.1 & 22.2 & 67.9 & 152.7 & 457.7 & 72.2 & 280.6 & 37.4 \\ \hline \end{tabular}  
* \({}^{1}\)\(P_{i}\) (\(=\) mean(\(P_{i}\)(\(t\))): statistically computed, for the mean values, from the historical power demand data and stored in the database in Figure 1, where an eventless NIALM approach, the presented methodology, is shown; \(\tau_{i}\): \(c_{i}\)_-std(\(P_{i}\)(\(t\))); \({}^{3}\) In the GA, the multistate transitions from the same types of the concerned electrical appliances are mutually exclusive; illegal offspring are assigned an objective value of 1000 to Equation (2) (an illegal chromosome cannot be decoded to/as a solution; that is, such an illegal chromosome cannot be evaluated).  
\end{table} 
Table 1: Electrical appliances concerned and considered for load disaggregation in this paper. Figure 6: Considered historical power demand on two typical days in House 1 from the UK-DALE dataset parsed in this paper. The thin blue line in (**a**,**c**) shows the total (whole-house) power demand in the mains; the stacked, filled, and colored blocks show the power demand by the concerned individual electrical appliances and all other submeters [32] considered together and treated as a single individual in the power absorptions also concerned in load disaggregation. Base load in (**b**,**d**) exists in the house. Figure 7: _Cont._  
Figure 7: Shown power demand of most of the electrical appliances: (**a**) fridge; (**b**) htpc; (**c**) washer dryer; (**d**) other submeters. We used the UK-DALE dataset [32] as our reference dataset to experimentally validate the performance of the investigated methodology in load recognition; note that, in the dataset, data that were recorded from House 1 in the UK were considered in this experiment. In this experiment, a total of 4096 (=\(2^{N(=12)}\)) load combinations need to be recognized by the parallel computing accelerated GA in this paper (the total number of meters installed and used as ground truth in the house environment is 54 [32]), where a total of 208 composite power consumption (NIALM) data instances are disaggregated according to Equation (2). For each data instance acquired at time \(t\) and disaggregated, the parallel computing accelerated GA indicates the electrical appliances whose operation is active or inactive. In this experiment, the parallel computing accelerated GA is implemented in MATLAB(r) and run on an Acer Predator G3-710 Intel(r) Core(tm) i7-6700 CPU (3.40 GHz) (RAM: 16 GB) personal computer (PC), where for parallel computing the total number of available workers, \(n\), on the machine is four. Note that running the parallel computing accelerated GA requires Global Optimization Toolbox(tm) [33] required with Parallel Computing Toolbox(tm) [34] for parallel computing.