## Appendix A
* (\(U_{S}\)) Peak load factor averages  
Peak load ratio average value, reflecting the fluctuation of the customer's load change in the peak period in the whole month (the peak period is defined as 08:00-11:00 and 18:00-21:00), is the average value of the ratio of the average value of the load and the average value of the load of the day in that time period, expressed as follows:  
\[b_{U_{L},\text{plr}}=\frac{1}{G}\sum_{t=1}^{G}\left(\frac{P_{t,(U_{L},\text{av },\text{peak})}}{P_{t,(U_{L},av)}}\right) \tag{11}\]  
In the formula, \(G\) is the total number of days in a month, which satisfies \(G=K_{av}+K_{NW}\), \(P_{t,(U_{L},\text{av},\text{peak})}\) is the average value of load of customer \(U_{i}\) during peak hours on day t, and \(P_{t,(U_{L},\text{av})}\) is the average value of load of customer \(U_{i}\) on day t. The larger the peak load factor average value \(b_{U_{L},\text{plr}}\) is, the more obvious the fluctuation of the load of customer \(U_{i}\) during the peak hour. * (\(U_{6}\)) Mean flat-period load factor  
The average value of load ratio in the weekly period, reflecting the fluctuation in the load in the weekly period of the whole month (the weekly period is defined as 06:00-08:00, 11:00-18:00, and 21:00-22:00), and it is the average value of the ratio between the average value of the load in the time period and the average value of the load in the day, which is expressed as follows:  
\[b_{U_{L},\text{slr}}=\frac{1}{G}\sum_{t=1}^{G}\left(\frac{P_{t,(U_{L},\text{ av},\text{sh})}}{P_{t,(U_{L},\text{av})}}\right) \tag{12}\]  
where \(P_{t,(U_{L},\text{av},\text{sh})}\) is the average value of load of customer \(U_{i}\) in the usual segment on day t. The larger the average value of load factor \(b_{U_{L},\text{slr}}\) in the weekly period, the more obvious the fluctuation of the load of customer \(U_{i}\) in the weekly period. * (\(U_{7}\)) Mean value of valley load factor  
Valley load rate average, reflecting the fluctuation in the customer throughout the month in the valley time load change fluctuations (the valley time is defined as 22:00-24:00 and 00:00-06:00) for the time period of the load average and the day load average ratio of the average value, expressed as follows:  
\[b_{U_{L},\text{vtr}}=\frac{1}{G}\sum_{t=1}^{G}\left(\frac{P_{t,(U_{L},\text{ av},\text{val})}}{P_{t,(U_{L},\text{av})}}\right) \tag{13}\]  
where \(P_{t,(U_{L},\text{av},\text{val})}\) is the average value of load of customer \(U_{i}\) during the valley hour on day t. The larger the average value of load factor \(b_{U_{L},\text{vlr}}\) in the valley period, the more significant the fluctuation of the load of customer \(U_{i}\) in the valley period. * Step 1: Calculate the similarity between each customer portrait in \(U\) using Equation (A8) \[s^{\prime}_{\rho}\big{(}U_{i},U_{j}\big{)}=\frac{\sum\limits_{y=1}^{7}\Big{(}b_{ U_{i,y}}-\overline{b}_{U_{i}}\Big{)}\Big{(}b_{U_{i,y}}-\overline{b}_{U_{i}} \Big{)}}{\sqrt{\sum\limits_{y=1}^{7}\Big{(}b_{U_{i,y}}-\overline{b}_{U_{i}} \Big{)}^{2}}\sqrt{\sum\limits_{y=1}^{7}\Big{(}b_{U_{i,y}}-\overline{b}_{U_{i}} \Big{)}^{2}}}\hskip 28.452756 pti\
eq j\] (A8)
* Step 2: Divide the customer set U into B parts and use the median customer portrait similarity \(\mu_{m,H_{\gamma}}\) as the customer self-similarity within each part, i.e., \(s_{\rho}(U_{j,H_{\gamma}},U_{j,H_{\gamma}})=\mu_{m,H_{\gamma}}\), where \(U_{j,H_{\gamma}}\) is the jth customer in \(H_{\gamma}\). * Step 3: Perform AP clustering for each segment of customers to obtain the set of cluster centers for each segment \(E_{1},E_{2},\cdots,E_{\gamma},\cdots,E_{B}\). * Step 4: Form a new set of customers \(E_{\text{new}}=E_{1}\cup E_{2}\cup\cdots\cup E_{B}\) from the set of clustering centers for each part of the customers. If the number of customers in \(E_{new}\) is greater than 400, return to Step 2; otherwise, go to Step 5.