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.

    参考文献
    [1] 裴力耕,张欣,赵明,等.售电公司分时电价盈利策略研究[J].电网与清洁能源,2020,36(11):45-52.PEI Ligeng,ZHANG Xin,ZHAO Ming,et al.A study on profit strategy of time-of-use electricity price of electricity sale companies[J].Power System and Clean Energy,2020,36(11):45-52.
    [2] LIU H F,HU Z,MIAN A,et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge Based Systems,2014,56:156-166.
    [3] GOHARI F S,ALIEE F S,HAGHIGHI H,et al.A significance-based trust-aware recommendation approach[J].Information Systems,2020,87:101421.
    [4] CHEN J Y,ZHANG H W,HE X N,et al.Attentive collaborative filtering:multimedia recommendation with item-and component-level attention[C]//The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval,Tokyo,Shinjuku,Japan,2017.
    [5] 宾晟,孙更新.基于多关系社交网络的协同过滤推荐算法[J].计算机科学,2019,46(12):56-62.BIN Sheng,SUN Gengxin.Collaborative filtering recommendation algorithm based on multi-relational social network[J].Computer Science,2019,46(12):56-62.
    [6] 张祖平,沈晓阳.基于深度学习的用户行为推荐方法研究[J].计算机工程与应用,2019,55(4):142-147+158.ZHANG Zuping,SHEN Xiaoyang.Research on user behavior recommendation method based on deep learning[J].Computer Engineering and Application,2019,55(4):142-147+158.
    [7] 吴云亮,张建新,李豹,等.深度学习辅助约束辨识的电力市场快速出清方法[J].中国电力,2020,53(9):90-97+207.WU Yunliang,ZHANG Jianxin,LI Bao,et al.A fast power market clearing method based on active constraints identification by deep learning[J].Electric Power,2020,53(9):90-97+207.
    [8] 李正浩,李孟凡.基于深度学习的智能型负荷预测方法的研究[J].智慧电力,2020,48(10):78-85+112.LI Zhenghao,LI Mengfan.Smart load forecasting method based on deep learning[J].Smart Power,2020,48(10):78-85+112.
    [9] 李卫国,陈立铭,张师,等.分时电价下考虑储能调度因素的短期负荷预测模型[J].电力系统保护与控制,2020,48(7):133-140.LI Weiguo,CHEN Liming,ZHANG Shi,et al.Short-term load forecasting model considering energy storage scheduling factors under time-sharing price[J].Power System Protection and Control,2020,48(7):133-140.
    [10] 肖桂雨,向健平,凌永志,等.基于小波神经网络的风力发电机故障预测方法[J].电力科学与技术学报,2019,34(2):195-202.XIAO Guiyu,XIANG Jianping,LING Yongzhi,et al.Prediction of wind turbine faults based on wavelet neural networks[J].Journal of Electric Power Science and Technology,2019,34(2):195-202.
    [11] 高强,刘畅,金道杰,等.考虑综合需求响应的园区综合能源系统优化配置[J].高压电器,2021,57(8):159-168.GAO Qiang,LIU Chang,JIN Daojie,et al.Optimal configuration of park-level integrated energy system considering integrated demand response[J].High Voltage Apparatus,2021,57(8):159-168.
    [12] 彭文,王金睿,尹山青.电力市场中基于Attention-LSTM的短期负荷预测模型[J].电网技术,2019,43(5):1745-1751.PENG Wen,WANG Jinrui,YIN Shanqing.Short term load forecasting model based on Attention-LSTM in electricity market[J].Power System Technology,2019,43(5):1745-1751.
    [13] 苑威威,彭敦陆,吴少洪,等.自注意力机制支持下的混合推荐算法[J].小型微型计算机系统,2019,40(7):1437-1441.YUAN Weiwei,PENG Dunlu,WU Shaohong,et al.Hybrid recommendation algorithm supported by self attention mechanism[J].Journal of Chinese Computer Systems,2019,40(7):1437-1441.
    [14] 张宇帆,艾芊,林琳,等.基于深度长短时记忆网络的区域级超短期负荷预测方法[J].电网技术,2019,43(6):1884-1892.ZHANG Yufan,AI Qian,LIN Lin,et al.A very short-term load forecasting method based on deep LSTM RNN at zone level[J].Power System Technology,2019,43(6):1884-1892.
    [15] 杨景刚,邓敏,马勇,等.基于深度学习的PRPD数据特征提取方法[J].电测与仪表,2020,57(3):99-104+115.YANG Jinggang,DENG Min,MA Yong,et al.Feature extraction of PRPD data based on deep learning[J].Electrical Measurement & Instrumentation,2020,57(3):99-104+115.
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引用本文

张帝,王韬,朱吉然,等.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,2022,37(2):213-218.
ZHANG Di, WANG Tao, ZHU Jiran, et al. Active service recommendation method for power customers based on LSTM-Attention fusion[J]. Journal of Electric Power Science and Technology,2022,37(2):213-218.

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  • 在线发布日期: 2022-05-26
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