###### Abstract
The specific steps are as follows: First, a customer load information database is established by constructing a sample customer profiling system and segmenting the sample customer set using the AAP clustering method. This database facilitates the extraction of characteristic profiles of electricity users and supports the identification of sample customers similar to the target customer. Next, quantitative and qualitative attributes are characterized using three-parameter interval gray numbers and triangular fuzzy numbers, respectively, to capture their distinct features. A hybrid target model (external reference point) is constructed by combining gray numbers and fuzzy numbers, while a temporal reference point is defined using average values and mean growth rates to reflect the dynamic evolution of options. A customer weight adjustment model is then developed by examining the similarity between individual and group decision-making among sample customers. Additionally, time weights are determined using the entropy weight method. This process yields comprehensive evaluation scores for all electricity sales packages from the perspectives of different customer categories, enabling a ranked recommendation of packages. Finally, a case study involving customers in a specific region of China is conducted to validate the feasibility and practicality of the proposed recommendation method. The results demonstrate its effectiveness in accurately and scientifically recommending electricity sales packages tailored to customer needs. Based on the above, this paper proposes a dynamic hybrid multi-attribute group decision-making with two reference points for electricity sales package recommendation (DH-MAGDM). The main contributions of this paper are as follows:  
1. A sample customer clustering method, the AAP clustering algorithm, is used to avoid the randomness, subjectivity, and related errors present in existing clustering algorithms, thereby improving the accuracy of identifying customers similar to the target customers. 2. Considering the uncertainty in customers' evaluation information for electricity sales packages, the quantitative attributes and qualitative attributes are characterized using three-parameter interval gray numbers and triangular fuzzy numbers, respectively. A dynamic multi-attribute group decision-making model based on the combination of three-parameter interval gray numbers and triangular fuzzy numbers is proposed. 3. A time reference point is set using the mean and average growth rate to reflect the dynamic development of electricity sales packages, effectively improving the accuracy of group decision-making for electricity sales packages. The remainder of this paper is organized as follows. Section II presents the methodology, specifically proposing a sample customer clustering method based on the customer profile label system and AAP, as well as a dynamic multi-attribute group decision-makingmodel based on two reference points. Section III covers the results and discussion, where a case study is conducted on customers in a certain region of China to test the feasibility of the proposed method, followed by a brief discussion of the results. Finally, Section IV concludes the paper, outlining the limitations of the study and future research directions.