## 4 Result Discussion  
In this study, relevant electrical signals were obtained through simulated experiments with microwave ovens under three distinct operational scenarios. These signals were then processed through a Bayesian network model developed to calculate the probability values for each experimental outcome. The results indicate that for the same electrical appliance, the model successfully identified the operational states at low, medium, and high settings, with probability values exceeding the decision threshold (low mode 89.1%, medium  
Figure 13: Cluster of mixed appliance operation, excluding microwave oven.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**1 st Harmonic**} & \multicolumn{3}{c}{**3 rd Harmonic**} & \multicolumn{3}{c}{**5 th Harmonic**} & \multicolumn{3}{c}{**7 th Harmonic**} \\ \hline I/A & 2.325 & 8.646 & 17.42 & 0.04393 & 0.4915 & 0.5024 & 0.07581 & 0.08286 & 0.08742 & 0.147 & 0.1771 & 0.1818 \\ U/A & 219.8 & 219 & 217.7 & 220.1 & 218.3 & 219.6 & 219.8 & 219 & 217.7 & 219.8 & 217.7 & 219 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Central cluster positions of harmonics of mixed appliance operation, excluding microwave oven.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1 st Harmonic** & **3 rd Harmonic** & **5 th Harmonic** & **7 th Harmonic** \\ \hline \multirow{3}{*}{Low mode} & 2.256929 & 0.107028 & 0.406909 & 0.425078 \\  & 6.402576 & 1.849012 & 1.039121 & 2.101069 \\  & 12.61787 & 1.2346 & 1.938175 & 0.502686  
mode 94.4%, high mode 92.0%). When the microwave oven operated concurrently with other electrical devices, the model accurately determined its operational state probabilities (low mode 98.4%, medium mode 54.2%, high mode 28.3%). Furthermore, in scenarios excluding microwave usage with other appliances in operation, the inferred probabilities decreased significantly, suggesting non-usage of the microwave (low mode 32.4%, medium mode 30.2%, high mode 37.7%). These experimental outcomes validate the theoretical effectiveness of the proposed method.  
Compared to existing state recognition methodologies using clustering and Bayesian networks [47], the proposed approach demonstrates certain improvements. The criteria for state inference have been expanded and refined. Instead of relying solely on current or power values for determination, the proposed method employs a decision-making process based on the fundamental current and multiple harmonics, coupled with an improved clustering method to identify cluster centroids. This approach more accurately captures the core characteristics of electrical appliance usage, enabling more precise determinations. For validation, we conducted inference processes under the same operational conditions, using only current or power values. While the microwave oven's operational current and power were set as activation conditions for the network nodes, resulting in a high identification probability of 98.9% in solitary operation, the probability fluctuated around 50% in mixed operation modes. This indicates that relying solely on current or power values could lead to misjudgments and an inability to determine the current mode accurately. In contrast to other methodologies that only provide conclusive determinations, our approach precisely calculates probability values based on node activation states, offering a clearer understanding and a more accurate response for the electrical behavior management of devices.  
For future research, there is potential to further expand the categorization of appliance operational states and the establishment of additional network nodes.