## 3 Results  
### Introduction to the Arithmetic Example  
Based on the load data of 500 typical customers \(U=\{U_{1},U_{2},\cdots,U_{500}\}\) collected with smart meters from 1 to 31 January 2022, the leave-one-out cross validation method was used [46]. The proposed method is validated, i.e., one customer is extracted as a new customer each time, and the remaining 499 customers are taken as the customer set. As shown in Table 1, to provide a comprehensive understanding of the customer base, a summary table of the key attributes of the 499 surveyed customers is presented. These attributes were chosen as they are crucial in understanding customers' decision-making processes when selecting electricity sale packages. The surveyed customers were specifically chosen because they represent a diverse sample of residential and commercial electricity consumers in the western Zhejiang region, allowing for a comprehensive validation of the proposed recommendation method. Furthermore, to demonstrate how representative the sample of customers was relative to the general population surveyed, a new table has been created as Table 2. The table comprises two primary components. The initial component enumerates the pivotal attributes taken into consideration, encompassing facets like \"Category\", \"Monthly Load (kWh)\", \"Load Factor\", and \"Value-Added Services Preference\". Subsequent to these attributes, the second component delineates two principal divisions. One division presents the mean values of these attributes, derived from our survey sample. The other division exhibits the corresponding mean values for the entire designated area. Referring to the data of the Provincial Trading Center, as well as the actual situation of the customers, referring to the package set provided by the electricity sale company for the customers as \(C=\{C_{1},C_{2},\cdots,C_{4}\}\), the unit price (\(d_{1}\)), value-added services (\(d_{2}\)), incentive policy (\(d_{3}\)), renewable energy proportion (\(d_{4}\)), and whether it is a fixed package (\(d_{5}\)) are selected as the decision-making information, which is detailed in Table 3. Considering that \begin{table}
\begin{tabular}{c c} \hline
**Key Attributes** & **Description** \\ \hline Monthly Load (kWh) & Average monthly electricity consumption of the customer. \\ Load Factor & Reflects the fluctuation of load change for the whole month. \\ Value-Added Services Pref. & Preference for value-added services such as power quality, energy management, etc. \\ Incentive Policy Pref. & Preference for incentive policies offered by electricity sale packages. \\ Renewable Energy Pref. & Preference for packages with a higher proportion of renewable energy sources. \\ Price Sensitivity & The influence of the price of electricity sale packages on the customer’s choice. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of key attributes of surveyed customers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Category** & \begin{tabular}{c} **Monthly** \\ **Load (kWh)** \\ \end{tabular} & **Load Factor** & \begin{tabular}{c} **Value-Added** \\ **Services Pref.** \\ \end{tabular} & \begin{tabular}{c} **Incentive** \\ **Policy Pref.** \\ \end{tabular} & \
\begin{tabular}{c} **Renewable** \\ **Energy Pref.** \\ \end{tabular} &
\begin{tabular}{c} **Price** \\ **Sensitivity** \\ \end{tabular} \\ \hline \multirow{4}{*}{Surveyed} & Services & 2655.93 & 0.62 & 0.57 & 0.72 & 0.67 & 0.62 \\  & Industry & 3,066,650.61 & 0.57 & 0.53 & 0.83 & 0.73 & 0.89 \\  & Residential & 1730.03 & 0.36 & 0.17 & 0.57 & 0.26 & 0.47 \\  & Agriculture & 264.96 & 0.37 & 0.39 & 0.47 & 0.52 & 0.53 \\ \hline \multirow{4}{*}{Average} & Services & 2500 & 0.60 & 0.55 & 0.75 & 0.65 & 0.60 \\  & Industry & 3,000,000 & 0.60 & 0.50 & 0.80 & 0.70 & 0.90 \\  & Residential & 1500 & 0.35 & 0.20 & 0.55 & 0.30 & 0.50 \\  & Agriculture & 300 & 0.45 & 0.40 & 0.50 & 0.50 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of key attributes between survey sample and regional average.