结合特征优选与双向长短期记忆网络的用能服务需求预测研究

doi:10.16183/j.cnki.jsjtu.2023.458

上海交通大学学报 ›› 2025, Vol. 59 ›› Issue (7): 1007-1018.doi: 10.16183/j.cnki.jsjtu.2023.458

• 新型电力系统与综合能源 • 上一篇下一篇

结合特征优选与双向长短期记忆网络的用能服务需求预测研究

康峰¹, 谭火超¹, 苏立伟¹, 简冬琳¹, 王帅¹, 覃浩¹(), 张勇军²

1.广东电网有限责任公司客户服务中心,广东佛山 528000
2.华南理工大学电力学院,广州 510000

收稿日期:2023-09-11 修回日期:2023-10-31 接受日期:2023-11-08 出版日期:2025-07-28 发布日期:2025-07-22
通讯作者: 覃浩 E-mail:13914361435@163.com
作者简介:康峰(1984—),硕士,从事电力营销数字化、智能客服等研究.
基金资助:
国家自然科学基金资助项目(52177085);中国南方电网有限责任公司科技项目(036800KK52220003)

Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks

KANG Feng¹, TAN Huochao¹, SU Liwei¹, JIAN Donglin¹, WANG Shuai¹, QIN Hao¹(), ZHANG Yongjun²

1. Customer Service Center of Guangdong Power Grid Co., Ltd., Foshan 528000, Guangdong, China
2. School of Electric Power Engineering, South China University of Technology, Guangzhou 510000, China

Received:2023-09-11 Revised:2023-10-31 Accepted:2023-11-08 Online:2025-07-28 Published:2025-07-22
Contact: QIN Hao E-mail:13914361435@163.com

摘要/Abstract

摘要：

准确且高效的用户用能服务需求预测对于电网客户服务质量管理与客户服务风险管理至关重要.为此,提出一种基于特征优选的用户用能服务需求预测模型.在分析用户用能服务数据的基础上,改进采样算法以解决数据中存在的类不平衡问题;基于自动编码器对数据进行降维处理,以确保K均值算法高效聚类;提出基于轻量级梯度提升机的特征优选算法,筛选有效特征,提高预测模型的训练效率;提出基于注意力机制的双向长短时记忆神经网络多标签分类算法,精细化用户的用能服务需求.对广东电网某地区3年72万条工单数据进行分析,证明该模型能够有效提高预测准确率及速度.

关键词: 用能服务, 需求预测, 类不平衡, 自动编码器, 特征优选, 多标签分类

Abstract:

Accurate and efficient demand forecasting of customer energy services is crucial for quality and risk management in grid customer service. Therefore, this paper proposes a user energy service demand prediction model based on feature selection. The methodology includes introducing a sampling algorithm to solve the class imbalance problem in the data on the basis of analysing the user energy service data, reducing the dimensionality of the data based on an autoencoder to ensure efficient clustering of the K-mean algorithm, constructing a feature selection algorithm based on a lightweight gradient lifting machine to filter the effective features and improve the training efficiency of the prediction model, and establishing a bidirectional long- and short-term memory neural network multi-label predicting model based on an attentional mechanism to refine the user’s energy service demand. Through the analysis of 720 000 work order data from Guangdong Power Grid over three years, showing that the model proposed can effectively improve the prediction accuracy and speed.

Key words: energy services, demand forecasting, class imbalance, automatic encoder, feature optimization, multi label classification

中图分类号:

TM721

康峰, 谭火超, 苏立伟, 简冬琳, 王帅, 覃浩, 张勇军. 结合特征优选与双向长短期记忆网络的用能服务需求预测研究[J]. 上海交通大学学报, 2025, 59(7): 1007-1018.

KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun. Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks[J]. Journal of Shanghai Jiao Tong University, 2025, 59(7): 1007-1018.

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序号	层级	数据来源	类型	内容	采样时间
1	区域	营销管理系统	用户区域	抄表区段	2019-01—2022-01
2	行业	营销管理系统	用户类型	用户电表类型(工/商/居)	2019-01—2022-01
3	用户	营销管理系统	用户信息	行业类别、用户编号、电表数	2019-01—2022-01
4	用户	营销管理系统	用户信息	用户类别、行业类别	2019-01—2022-01
5	用户	营销管理系统	工单信息	业务子类、时间	2019-01—2022-01
6	用户	智慧营业厅	工单信息	业务子类、时间	2019-01—2022-01
5	电表	营销管理系统	电表信息	报装容量	2019-01—2022-01
6	电表	计量系统	电量电费	用户月用电量、电费	2019-01—2022-01

参数	取值
轮数	300
隐藏层	256
学习率	0.02
层数	6

算法	准确率	汉明损失	时间/s
LightGBM(未特征优选)	0.764 9	0.187 8	249.43
LightGBM(特征优选)	0.899 3	0.100 7	126.32
AM-BiLSTM(未特征优选)	0.879 6	0.102 4	227.15
AM-BiLSTM(特征优选)	0.953 2	0.051 1	128.95

算法	准确率	汉明损失	时间/s
LightGBM	0.899 3	0.100 7	126.32
AM-BiLSTM	0.953 2	0.051 1	128.95
CNN-GRU	0.949 5	0.051 7	289.45
BiLSTM	0.833 4	0.157 9	196.85
AM-LSTM	0.871 2	0.193 6	243.15
AM-CNN-LSTM	0.949 8	0.051 2	279.67

结合特征优选与双向长短期记忆网络的用能服务需求预测研究

Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks

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