Electricity Package Recommendation Integrating Improved Density Peaks Clustering and Fuzzy Group Decision-Making

The recommendation of electricity retail packages is challenged by diversified user demands and the complexity of evaluation information in liberalized electricity markets. Existing approaches are often limited by the subjectivity of user clustering and the difficulty of accurately capturing cognitive fuzziness and dynamic weight variations in the decision-making process. To address these challenges, this paper proposes a novel recommendation framework that integrates Improved Density Peaks Clustering (IDPC) with group decision-making based on trapezoidal fuzzy numbers. First, an IDPC-based model is constructed to objectively identify and partition users into homogeneous groups based on similar electricity consumption characteristics. Subsequently, a dynamic multi-attribute group decision-making model, which synergizes trapezoidal fuzzy numbers and the Multi-Criteria Compromise Ranking Method (MCRM), is designed to aggregate evaluation information from these user groups and to score the retail packages. Furthermore, a full-ranking recommendation strategy is established based on group satisfaction levels. Finally, a case study using a real-world dataset from a region in Eastern China is conducted. The empirical results demonstrate the framework’s superior performance: the IDPC algorithm achieves a stable Davies–Bouldin index of approximately 1.4, and the final recommendation ranking yields a Spearman correlation coefficient of 0.9 against simulated actual choices, significantly outperforming benchmark methods. This study shows that the proposed method can effectively enhance the precision and relevance of package recommendations, providing crucial decision support for electricity retailers in implementing refined marketing strategies.