| 骆 钊,张 涛,高泽勇,周 磊,梁俊宇,林铭良.基于用户特征聚类的综合能源套餐推荐方法[J].电力需求侧管理,2023,25(6):76-81 |
| 基于用户特征聚类的综合能源套餐推荐方法 |
| Comprehensive energy package recommendation method based on user feature clustering |
| 投稿时间：2023-05-10 修订日期：2023-08-02 |
| DOI：10. 3969 / j. issn. 1009-1831. 2023. 06. 012 |
| 中文关键词: 综合能源服务 推荐算法 知识图谱 谱聚类 随机森林 |
| 英文关键词: integrated energy service recommendation algorithm knowledge graph spectral clustering random forest |
| 基金项目:国家自然科学基金项目（52277104,51907084）；云南省重点研发计划资助项目（202303AC1000003） |
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| 中文摘要: |
| 针对庞大的综合能源用户群体在购买能源服务套餐时难以选择的问题,提出一种基于用户特征聚类的综合能源套餐推荐方法,以提高用户粘性。首先,将收集的综合能源用户信息进行知识图谱构建,对缺失的用户信息进行补充完善,同时分析用户之间的关系。然后,采用谱聚类的方法对构建好的用户知识图谱进行图谱分割聚类,进行用户之间的相似度计算,提取代表综合能源用户用能行为多样性的兴趣特征。最后,通过随机森林模型计算综合能源用户对各能源服务套餐的预测评分,将预测评分进行排序后,取评分最高的部分套餐通过线上平台为用户呈现套餐服务内容,实现对用户的精准推荐。将所提套餐推荐模型与传统推荐模型进行比较分析,结果表明基于用户特征聚类的综合能源套餐推荐方法能够为综合能源服务公司实现有效的用户精准化能源服务推荐,有利于提升能源服务公司的市场竞争力,同时为电力企业向综合能源服务提供商转型提供技术支撑。 |
| 英文摘要: |
| Aiming at the difficulty for a large group of comprehensive energy users to choose when purchasing energy service packages, a comprehensive energy package recommendation method based on user feature clustering is proposed to improve user stickiness. First of all, the comprehensive energy user information collected is constructed by knowledge graph, the missing user information is supplemented and improved, and the relationship between users is analyzed. Then, spectral clustering method is used to cluster the constructed user knowledge map, calculate the similarity among users, and extract the interest features representing the diversity of energy use behavior of comprehensive energy users. Finally, the random forest model is used to calculate the predicted scores of comprehensive energy users for each energy service package. After sorting the predicted scores, the part of the package with the highest score is selected to present the package service content for users through the online platform, so as to achieve accurate recommendation for users. By comparing the package recommendation model proposed in this paper with the traditional recommendation model, the results show that the integrated energy package recommendation method based on user feature clustering can achieve effective user precision energy service recommendation for integrated energy service companies, which is conducive to improving the market competitiveness of energy service companies, and provides technical support for the transformation of power enterprises into integrated energy service providers. |
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