# Optimal Strategy to Select Load Identification Features by Using a Particle Resampling Algorithm
## 3 Experiments and Results
### Laboratory Validation  
The experiments used five devices, including a rice cooker, microwave oven, induction cooker, air conditioner, and kettle. Firstly, the individual device underwent the state of turning on and turning off several times, as seen in Figure 5 for an example. Then, the active power and reactive power were obtained through data statistical analysis, as shown in Table 1. Finally, these load features were recorded in the MySQL database. Meanwhile, the threshold was \(h\) = 350, the minimum change load active power was 100 watts, and the event detection window length was 20 sample points for the load event detection in this example. Figure 6 shows the above load during its state of turning on and/or turning off in the actual test. The order sequence is air conditioner on, induction cooker on, kettle on, microwave oven on, rice cooker on, then kettle off, induction cooker off, microwave oven off, rice cooker off, and air conditioner off, respectively, as shown in Table 2. Table 3 shows the timing of the change point obtained by the original CUSUM load event detection [23]. It can be seen that there was a certain multi-detection phenomenon due to the increase slowly of active power after turning on the air conditioner. Nevertheless, our method could detect this type of load and could get better result accuracy, as shown in Table 4. So the resulting change points is approximated by the real-time change points. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Devices** & **P/W** & **Q/Var** & **Mean of P/W** & **Mean of Q/Var** \\ \hline Induction cooker & 1556.8–1622.1 & 54.5–63.5 & 1586.1 & 54.8 \\ Kettle & 1397.6–1429.4 & \(-\)20.4—2 & 1405.2 & \(-\)8.6 \\ Rice cooker & 584.3–600.3 & \(-\)8.5–1.9 & 594.6 & \(-\)2.8 \\ Air conditioner & 435.9–516.9 & \(-\)192.8—145.1 & 467.3 & \(-\)173.3 \\ Microwave oven & 1170.5–1223.6 & \(-\)272.9—196.2 & 1207.9 & \(-\)221.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Household load power information. P: the active power; Q: the reactive power. Figure 5: Active and reactive power diagrams of different devices: (**a**) induction cooker, (**b**) kettle, (**c**) rice cooker, (**d**) air conditioner, and (**e**) microwave oven. Further, Table 4 shows the difference of P and Q in the case of the time interval T = 1, using the change point detected by our method. It was found that the obtained load identification features had a certain distance from the corresponding actual load characteristic information shown in Table 1. In particular, the active power with slow increases, such as the air conditioner, greatly differed from the actual characteristic values, which may fail to identify the load. In addition, these loads have different load characteristics between on and off, such as electric kettles and induction cookers. In this paper, the particle resampling algorithm was used to extract features. By retaining the particles with larger weights, some off-center particles were discarded and the best load characteristics could then be found. \begin{table}
\begin{tabular}{c c} \hline
**Order Sequence** & **Event Description** \\ \hline A & Air conditioner on \\ B & Induction cooker on \\ C & Kettle on \\ D & Microwave oven on \\ E & Rice cooker on \\ F & Kettle off \\ G & Induction cooker off \\ H & Microwave oven off \\ I & Rice cooker off \\ J & Air conditioner off \\ \hline \end{tabular}
\end{table} 
Table 2: The description of the load event for the test. Figure 6: The active and reactive power during the load turning on or off. Taking the air conditioning load switching on and off as an example, Figure 7 shows the whole process of particle resampling. It can be seen that, in Figure 7a,b, there are a few particles far away from the center point obtained by the whole particles and that the distribution is not uniform. So, these particles can be replaced by the resampling method, as seen in Figure 7c,d. These particles almost converge to a state after resampling.