## 3 Results
### Recommended Analysis of Electricity Sale Packages
#### 3.2.1 Obtaining the Customer Decision Matrix
Agricultural electricity consumption is primarily utilized by mechanized farmers. Farmers generally consume more electricity during the day for farming activities and less at night when they are resting. After the clustering is completed, the new customers are put into the customer set, and the similarity is calculated by using Formula (1), and then \(N\) similar customers are found, here \(N\) is taken as 3. And the probabilistic linguistic term set is used to portray the decision information value. Given the challenges associated with obtaining actual ranking results for various electricity plans, this paper makes several assumptions regarding selection patterns to simulate real-world choices. These assumptions are crucial for developing a comprehensive understanding of consumer behavior in the electricity market. Selection Pattern 1: Customers who exhibit higher electricity consumption during non-working hours are more inclined to choose time-of-use (TOU) electricity plans. This assumption is based on the idea that these customers may be seeking to optimize their electricity usage during off-peak hours to reduce costs. By selecting a TOU plan, they can take advantage of lower rates during non-working hours, which can lead to significant savings on their monthly electricity bills. Selection Pattern 2: Customers with consistently high and relatively stable electricity consumption are more likely to opt for tiered pricing electricity plans. This assumption is based on the observation that customers with predictable and stable electricity usage patterns may find tiered pricing plans more suitable. These plans often offer lower rates for the first tier of usage, which can be beneficial for customers who consistently use a moderate amount of electricity. Selection Pattern 3: When electricity plan prices are comparable, customers tend to prefer plans that offer a higher proportion of renewable energy sources. This assumption reflects the increasing importance of sustainability and environmental concerns in consumer decision-making. Customers who are willing to pay a slight premium for renewable energy  
Figure 4: Customer sample clustering results. sources are likely to prioritize plans that align with their values and contribute to a cleaner energy future. Selection Pattern 4: When electricity plan prices are similar, customers who are sensitive to power quality disturbances tend to prioritize high-quality power value-added services, whereas other customers are more focused on energy management services. This assumption acknowledges the diverse needs and preferences of electricity consumers. While some customers may be willing to pay extra for reliable and high-quality power services, others may prioritize energy management tools and resources that help them monitor and reduce their electricity usage. Let S = {s\({}_{-3}\) = extremely low, s\({}_{-2}\) = low, s\({}_{-1}\) = low, s\({}_{0}\) = average, s\({}_{1}\) = higher, s\({}_{2}\) = higher, and s\({}_{3}\) = extremely high} be a symmetric linguistic term set, and, at the same time, to facilitate the calculation, consider that the probabilistic linguistic term sets in the customer decision matrix are all of standard type; at this time, the customer decision matrix given by 3 similar customers can be shown as follows:  
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