## 1 Introduction
Considering customers' preferences for various attributes, such as package capacity, electricity prices, and peak-valley electricity prices, Reference [21] proposed a hybrid electricity sale package recommendation method based on electricity customer characteristics and multi-attribute utility. [PERSON] [16] utilized the fuzzy c-means clustering algorithm to classify customers, leveraging the similarity between the target customer and historical customers in the affiliated category, along with historical customers' scores for electricity sale packages, to predict the target customer's score. In Reference [22], household appliance usage time was considered as a residential customer's energy consumption characteristic, and electricity sale packages were recommended based on similar customers' package scores. Building on this, [PERSON] [23] introduced a recommendation method based on Bayesian hybrid collaborative filtering, using the Bayesian probability matrix decomposition algorithm to handle missing usage data for some residential customer appliances, enhancing the practicality of the recommendation algorithm. Lastly, in Reference [24], a customer set matrix was established, employing the similar customer discrimination method of load characteristic labels and double-layer affinity propagation clustering. The study utilized the multi-granularity hesitant fuzzy language set and the differential weight model to quantify customers' evaluation information on multiple package attributes, recommending the electricity sale package with the highest comprehensive satisfaction to the target customer. However, despite considering customers' electricity consumption costs, behavioral characteristics, and preferences, these indirect recommendation methods assume that customers are familiar with all package attributes, which is often unrealistic. Furthermore, they overlook psychological behavioral factors, such as risk-taking and avoidance psychology, which can influence customers' subjective judgments. Additionally, the uncertainty of attribute correlations and attribute weights in the decision-making process remains unaddressed. Therefore, given the limitations of both direct and indirect methods, the multi-attribute group decision-making (MADM) approach is considered for recommending electricity sale packages. Multi-attribute decision-making (MADM) constitutes a crucial component of modern decision science. The Prospect Theory effectively captures the psychological sentiments of experts when evaluating gains and losses in the decision-making process. Recently, the integration of the Prospect Theory with other MADM methods has emerged as a focal point of research in the field. For instance, in Reference [25], aiming to address the hybrid MADM problem with customers' expectations, a decision analysis method based on Cumulative Prospect Theory was proposed, considering customers' psychological behavioral factors. In Reference [26], addressing bounded rational behavior among customers, the Cumulative Prospect Theory and the VIKOR method were incorporated into Pythagorean hesitant fuzzy risk MADM. In Reference [27], the Hesitant Fuzzy Set-Prospect Theory evaluation method was utilized to compare evaluation index values across various scenarios, mitigating the impact of individual decisions on corporate emission reduction and determining the recommended emission reduction plan for energy. In Reference [28], a decision-making method based on the Prospect Theory was introduced for risk MADM problems involving customers' expectations, where both probabilities and attribute values are interval numbers. In Reference [29], a MAGDM method grounded in a novel decision reference point and the Prospect Theory was proposed for scenarios where attribute index values are hesitant fuzzy information and where attribute weights are entirely unknown. A review of the existing literature reveals various MADM methods rooted in the Superiority-Inferiority Ranking (SIR) method within the existing research landscape.