Dynamic Hybrid Multi-Attribute Group Decision-Making with Two Reference Points for Electricity Sales Package Recommendation

Given that existing electricity sales package recommendation methods mainly consider scenarios where customers are familiar with all the attributes of the packages, they tend to overlook the mixed nature of quantitative and qualitative attributes, psychological factors, and the determination of attribute weights in the decision-making process. To address these limitations, this paper proposes a dynamic hybrid multi-attribute group decision-making with two reference points for electricity sales package recommendation, combining prospect theory. The specific approach is as follows: First, the AAP clustering technique is used to divide the sample customer set and establish a customer load information database, from which customer characteristic profiles are extracted to identify sample customers similar to the target customer. Second, considering the mixed nature of quantitative and qualitative attributes of electricity sales packages and the different characteristics of these attributes, the three-parameter interval gray number and triangular fuzzy number are used to characterize them. A mixed target model is proposed as an external reference point. Next, time reference points are set by using the mean and average development speed to examine the dynamic development of the electricity sales packages. Then, the vector angle method is used to assess the similarity between individual decision-making and group decision-making, establishing an appropriate customer weight adjustment model. The comprehensive prospect value of each package is calculated under the two reference points by comparing the values, which are then used to rank all electricity sales packages. Finally, a case study of customers from a region in China is conducted to verify the accuracy and effectiveness of the proposed recommendation method.