## 3 Results
### Recommended Analysis of Electricity Sale Packages  
#### 3.2.1 Obtaining the Customer Decision Matrix  
Firstly, the sample BLAP cluster is applied to the customer set in order to identify distinct groups within the data. The optimal clustering results are derived using Formula (2), which is specifically designed to evaluate the quality of the clusters based on certain criteria such as compactness and separation. As illustrated in Figure 4 below, these results provide a clear visualization of the customer segments that have been identified through the BLAP clustering process. The clusters are distinct and well-separated, indicating that the BLAP method has effectively grouped similar customers together while differentiating between dissimilar ones. Category I (depicted in Figure 4a) represents electricity consumption for services, Category II (Figure 4b) for industry, Category III (Figure 4c) for residential use, and Category IV (Figure 4d) for agriculture. Different colors represent clustering results for different individual users. As illustrated in Figure 4, industrial electricity consumption exhibits a distinct pattern: loads are low at night, with only some production lines remaining operational. In the morning, there is a preheating and startup phase, leading to an increase in load. Peak load occurs during full-scale production, which spans from morning to afternoon. Subsequently, as production lines shut down in the evening and into the night, the load decreases. For residential electricity consumption, loads are similarly low at night, primarily due to standby power usage. Upon waking in the morning, the load increases. From morning to afternoon, when residents are typically out for work, the load remains at a relatively stable level. However, as people return home and engage in various activities in the evening and into the night, the load reaches its peak. Subsequently, as people go to sleep, the load decreases. Commercial electricity consumption follows a pattern where loads are low \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Electricity Sale** & **The Unit Price** & **Value-Added Service** & **Incentive Policy** & **Proportion of Renewable Energy/\%** & **Whether It Is a Fixed Package** \\ \hline \multirow{3}{*}{\(C_{1}\)} & 0.59 (\(<\)500 kWh) & Energy efficiency management & An 8\% discount for on-time billing & 15 & be \\  & 0.87 (\(>\)1000 kWh) & & Energy efficiency management & An \% discount for on-time billing & \\ \hline \multirow{3}{*}{\(C_{2}\)} & 0.48 (\(<\)500 kWh) 0.67 & High electricity quality & Reward 10\% of the battery percentage & 5 & be \\  & (\(>\)1000 kWh) & & & & \\ \hline \multirow{3}{*}{\(C_{3}\)} & 0.86 & & & & \\  & (Peak:10:00–12:00, & & & & \\  & 13:00–19:00) 0.60 & & & & \\ \cline{1-1}  & (Flat: 06:00–10:00, & Energy efficiency management & 8\% discount for on-time billing & 15 & clogged \\ \cline{1-1}  & 19:00–22:00) & & & & \\ \cline{1-1}  & 0.30 (Valley: & & & & \\ \cline{1-1}  & 22:00–06:00 next day) & & & & \\ \hline \multirow{3}{*}{\(C_{4}\)} & 0.80 & & & & \\ \cline{1-1}  & (Peak:10:00–12:00, & & & & \\ \cline{1-1}  & 13:00–19:00) 0.63 & & & & \\ \cline{1-1}  & (Flat: 06:00–10:00, & High electricity quality & Reward 10\% of the battery percentage & 5 & clogged \\ \cline{1-1}  & 12:00–13:00, & High electricity quality & Reward 10\% of the battery percentage & 5 & clogged \\ \cline{1-1}  & 19:00–22:00) & & & & \\ \cline{1-1}  & 0.30 (Valley: & & & & \\ \cline{1-1}  & 22:00–06:00 next day) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Set of electricity sale packages offered by the electricity sale companies. at night, despite some venues remaining open. In the morning, as businesses open and become operational, the load increases. Morning activities are frequent, resulting in higher load levels. During lunchtime, there is a slight decrease in load. However, in the afternoon, the load remains high. As evening activities peak, so does the load. Subsequently, as nighttime activities decrease, the load also drops.