| 引用本文: | 周王峰,李 勇,郭钇秀,等.基于DAE-LSTM神经网络的配电网日线损率预测[J].电力系统保护与控制,2021,49(17):48-56.[点击复制] |
| ZHOU Wangfeng,LI Yong,GUO Yixiu,et al.Daily line loss rate forecasting of a distribution network based on DAE-LSTM[J].Power System Protection and Control,2021,49(17):48-56[点击复制] |
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| 摘要: |
| 针对配电网线损精益化管理的需求，为准确把握配电线路线损率短期变化趋势，提出一种基于降噪自编码器(DAE)和长短期记忆网络(LSTM)相结合的配电网日线损率预测模型。首先建立灰色综合关联度分析指标，挖掘日线损率影响因素近期量与其去年同期量间的相关性，选择去年同期量作为模型的输入变量辅助预测。然后以无监督的方式构建DAE模型对输入序列进行特征编码与重构，实现输入序列的特征提取与降维。最后将编码后的序列输入LSTM神经网络，经训练拟合得到日线损率预测模型。采用湖南某地市多条配电线路实测数据进行实例分析，结果表明该模型日线损率预测准确性较高，运算速度适中，具有一定的实际工程应用价值。 |
| 关键词: 配电网 线损率 降噪自编码器 长短期记忆网络 预测 |
| DOI：DOI: 10.19783/j.cnki.pspc.201483 |
| 投稿时间：2020-11-30修订日期：2021-03-26 |
| 基金项目:国家重点研发计划政府间国际科技创新合作重点项目资助(2018YFE0125300)；国家自然科学基金项目资助(52061130217)；湖湘高层次人才聚集工程项目资助(2019RS 1016)；长沙市杰出创新青年计划项目资助(KQ1905008) |
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| Daily line loss rate forecasting of a distribution network based on DAE-LSTM |
| ZHOU Wangfeng,LI Yong,GUO Yixiu,QIAO Xuebo,MEI Yujie,DENG Wei |
| (1. School of Electrical Engineering and Information, Hunan University, Changsha 410082, China;
2. State Grid Hunan Electric Power Co., Ltd. Research Institute, Changsha 410007, China) |
| Abstract: |
| In order to determine the short-term change trend of distribution line loss rate and meet the need of line loss lean management, this paper proposes a distribution daily line loss rate forecasting model based on a combination of Denoising Auto-Eencoder (DAE) and Long Short-Term Memory (LSTM) network. First, this paper establishes a grey comprehensive correlation indicator to analyze the correlation between the recent sequences of daily line loss rate influencing factors and the same period in the previous year. The influencing factors of the previous year are added to the model input to assist prediction. Then, to realize the feature extraction and dimension reduction of input sequences, the DAE is constructed to encode and reconstruct the input in an unsupervised way. Finally, the coded sequences are input into the LSTM and the model is obtained by training. A case analysis is carried out using the data of multiple distribution lines in a city of Hunan. The results show that the proposed model has superior accuracy and moderate calculating speed. This has a certain engineering application value.
This work is supported by the International Science and Technology Cooperation Program of China (No. 2018YFE0125300), the National Nature Science Foundation of China (No. 52061130217), the Innovative Construction Program of Hunan Province of China (No. 2019RS1016) and the Excellent Innovation Youth Program of Changsha of China (No. KQ1905008). |
| Key words: distribution network line loss rate denoising autoencoder long short-term memory network prediction |
