基于LSTM-Attention融合的电力客户主动服务推荐方法
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TM-9

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国家自然科学基金(5127715);国网湖南省电力有限公司科技项目(5216A5180014)


Active service recommendation method for power customers based on LSTM-Attention fusion
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    摘要:

    为提升用电水平,借助人工智能技术进行电力客户主动服务是必然趋势。针对电力行业中在客户主动服务方面的研究不足,提出一种基于LSTM-Attention融合的电力客户主动服务推荐方法。该方法能够有效地解决单一深度学习模型在服务推荐当中出现的梯度弥撒以及梯度爆炸等问题。本文首先建立从电力投诉工单提取客户潜在服务需求的模型;进而获得基于LSTM-Attention融合算法的电力客户主动服务推荐方法;最后采用某市电力客户投诉工单实例随算法和模型进行验证。实验表明本文方法正确有效。

    Abstract:

    In order to improve the level of electricity consumption, it is an inevitable trend to use artificial intelligence technology to provide active service to electricity customers. Under the background, an active customer service recommendation method is proposed based on LSTM-Attention fusion considering the lack of research on active customer service in the power industry. The proposed method can effectively solve the problems of gradient mass and gradient explosion in the service recommendation of a single deep learning model. Firstly, a model is established for extracting potential service demands of customers from electric power complaint work orders. Then, an active service recommendation method is obtained for electric power customers based on the LSTM-Attention fusion algorithm. Finally, an electric power customer complaint work order in one city is included to verify the algorithm and model. It is shown that this method is effective.

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张帝,王韬,朱吉然,唐海国,张志丹,唐小伟,颜宏文.基于LSTM-Attention融合的电力客户主动服务推荐方法[J].电力科学与技术学报,2022,(2):213-218. ZHANG Di, WANG Tao, ZHU Jiran, TANG Haiguo, ZHANG Zhidan, TANG Xiaowei, YAN Hongwen. Active service recommendation method for power customers based on LSTM-Attention fusion[J]. Journal of Electric Power Science and Technology,2022,(2):213-218.

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  • 在线发布日期: 2022-05-26
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