| 汤丽莉,陈 涛,高赐威,明 昊,袁 浩.基于深度学习推荐模型的电力市场售电套餐推荐方法[J].电力需求侧管理,2024,26(5):01-08 |
| 基于深度学习推荐模型的电力市场售电套餐推荐方法 |
| Recommendation method of electricity sales package in electricity market based on deep learning recommendation model |
| 投稿时间：2024-07-21 修订日期：2024-08-29 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 05. 001 |
| 中文关键词: 电力市场 售电套餐推荐 深度学习推荐模型 深度兴趣进化网络 门控机制 |
| 英文关键词: electricity market electricity sales package recommendation deep learning recommendation model deep interest evolution network gating mechanism |
| 基金项目:国家自然科学基金资助项目（52107079）；江苏省自然科学基金项目（BK20210243） |
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| 中文摘要: |
| 针对售电公司如何在海量售电套餐中精准地为用户进行产品推荐,以及电力用户如何在众多的产品中挑选出符合自身需求套餐的双难问题,提出了一种基于深度学习的深度兴趣进化网络（deep interest evolution network,DIEN）售电套餐推荐算法。首先,将多种推荐模型进行比较,验证DIEN的表现；其次,对模型中的兴趣进化层结构与超参数进行解析；然后,针对DIEN模型在电力市场应用领域的“长尾效应”,在原始模型的兴趣提取层与用户向量之间引入两种门控机制；最后,进行算例分析,结果表明所提方法可在现有基础上提高电力用户的用电套餐适配率,提升电力公司的市场竞争力与电力公司和用户的双向收益。 |
| 英文摘要: |
| To accurately recommend power sales packages to users and help them select the package that best meets their needs, a deep interest evolution network（DIEN）algorithm based on deep learning is proposed. First, a comparison of several recommendation models is conducted to assess the performance of DIEN. Subsequently, an analysis of the structure of the interest evolution layer and the model’s hyperparameters is performed. Then, aiming at the“long tail effect”observed in the application of DIEN model in electricity market domain,two gating mechanisms are introduced between the interest extraction layer of the original model and the user vector. Finally, the feasibility of the proposed method is verified through a case analysis. Results show that proposed method can improve the electricity package adaptation rate of electricity users, enhance the market competitiveness of the power company and bidirectional benefits for both of the power company and users. |
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