基于负荷数据频域特征和LSTM网络的类别不平衡负荷典型用电模式提取方法

doi:10.12204/j.issn.1000-7229.2020.08.003

摘要/Abstract

摘要： 现有用户用电模式提取技术主要基于负荷数据时域特征提取，无法准确分辨时域上欧式距离接近但频域上波动特性差异较大的负荷数据，且对类别不平衡负荷数据的分类准确率较低。为解决上述问题，文章首先通过基于样本支持向量的过采样方法（support vector machines-synthetic minority over-sampling technique, SVM-SMOTE）对存在类别不平衡问题的负荷数据进行处理；然后，通过极大重叠离散小波变换（maximal overlap discrete wavelet transform, MODWT）对负荷数据进行分解，并将分解后的尺度系数和细节系数组成频域的特征矩阵；最后将频域特征矩阵输入深度长短期记忆（long short-term memory, LSTM）神经网络进行负荷分类并通过求取各个类别质心来获取典型用电模式。实验结果表明，该方法具有良好的类别不平衡数据处理能力和负荷分类效果。

关键词: 深度学习, 类别不平衡, 极大重叠离散小波变换（MODWT）, 负荷分类, 长短期记忆神经网络（LSTM）

Abstract: The current user mode extraction technology is mainly based on the time-domain characteristics of load data. It cannot accurately distinguish load data with close Euclidean distance in time domain and different fluctuation characteristics in frequency domain, and the classification accuracy of imbalanced data is low. This paper proposes a user mode extraction model to solve the above problems. The model firstly processes the data set with class imbalance by SVM-SMOTE over-sampling method, secondly obtains the scale and wavelet coefficients which is used to form the characteristic matrix in frequency domain through overlapping discrete wavelet transform, finally inputs the characteristic matrix into the deep LSTM network for classification and obtains the typical user mode by calculating the centroid of each category. The experimental results show that the method can effectively process imbalanced data and increase classification accuracy.

Key words: deep learning, class imbalance, maximum overlapping discrete wavelet transform（MODWT）, load classification, long-term and short-term memory network （LSTM）

中图分类号:

TM 74

唐子卓, 刘洋, 许立雄, 郭久亿. 基于负荷数据频域特征和LSTM网络的类别不平衡负荷典型用电模式提取方法[J]. 电力建设, 2020, 41(8): 17-24.

TANG Zizhuo, LIU Yang, XU Lixiong, GUO Jiuyi. Imbalanced-Load Pattern Extraction Method Based on Frequency Domain Characteristics of Load Data and LSTM Network[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(8): 17-24.

参考文献［29］

［1］	张铁峰,顾明迪. 电力用户负荷模式提取技术及应用综述［J］. 电网技术, 2016, 40（3）: 804-811.
	ZHANG Tiefeng, GU Mingdi. Overview of electricity customer load pattern extraction technology and its application［J］. Power System Technology, 2016, 40（3）: 804-811.
［2］	朱文俊, 王毅, 罗敏, 等. 面向海量用户用电特性感知的分布式聚类算法［J］. 电力系统自动化, 2016, 40（12）: 21-27.
	ZHU Wenjun, WANG Yi, LUO Min, et al. Distributed clustering algorithm for awareness of electricity consumption characteristics of massive consumers［J］. Automation of Electric Power Systems, 2016, 40（12）: 21-27.
［3］	张素香, 刘建明, 赵丙镇, 等. 基于云计算的居民用电行为分析模型研究［J］. 电网技术, 2013, 37（6）: 1542-1546.
	ZHANG Suxiang, LIU Jianming, ZHAO Bingzhen, et al. Cloud computing-based analysis on residential electricity consumption behavior［J］. Power System Technology, 2013, 37（6）: 1542-1546.
［4］	杨德昌, 赵肖余, 何绍文, 等. 面向海量用户用电数据的集成负荷预测［J］. 电网技术, 2018, 42（9）: 2923-2929.
	YANG Dechang, ZHAO Xiaoyu, HE Shaowen, et al. Aggregated load forecasting based on massive household smart meter data［J］. Power System Technology, 2018, 42（9）: 2923-2929.
［5］	Dehalwar V, Kalam A, Kolhe M L, et al. Electricity load forecasting for urban area using weather forecast information［C］// 2016 IEEE International Conference on Power and Renewable Energy （ICPRE）. Shanghai: IEEE, 2016:355-359.
［6］	AHMAD T, CHEN H X, SHAIR J, et al. Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management［J］. International Journal of Refrigeration, 2019, 98: 399-409.
［7］	彭显刚, 赖家文, 陈奕. 基于聚类分析的客户用电模式智能识别方法［J］. 电力系统保护与控制, 2014, 42（19）: 68-73.
	PENG Xiangang, LAI Jiawen, CHEN Yi. Application of clustering analysis in typical power consumption profile analysis［J］. Power System Protection and Control, 2014, 42（19）: 68-73.
［8］	张丽艳, 陈映月, 韩正庆. 基于改进聚类方式的牵引负荷分类方法研究［J］. 西南交通大学学报，2020，55（1）：27-40.
	ZHANG Liyan, CHEN Yingyue，HAN Zhengqing. Traction load classification method based on improved clustering method［J］.Journal of Southwest Jiaotong University，2020，55（1）：27-40.
［9］	顾丹珍, 艾芊, 陈陈, 等. 自适应神经网络在负荷动态建模中的应用［J］. 中国电机工程学报, 2007, 27（16）: 31-36.
	GU Danzhen, AI Qian, CHEN Chen, et al. Application of adaptive neural network in dynamic load modeling［J］. Proceedings of the CSEE, 2007, 27（16）: 31-36.
［10］	黎祚, 周步祥, 林楠. 基于模糊聚类与改进BP算法的日负荷特性曲线分类与短期负荷预测［J］. 电力系统保护与控制, 2012, 40（3）: 56-60.
	LI Zuo, ZHOU Buxiang, LIN Nan. Classification of daily load characteristics curve and forecasting of short-term load based on fuzzy clustering and improved BP algorithm［J］. Power System Protection and Control, 2012, 40（3）: 56-60.
［11］	孙志军, 薛磊, 许阳明, 等. 深度学习研究综述［J］. 计算机应用研究, 2012, 29（8）: 2806-2810.
	SUN Zhijun, XUE Lei, XU Yangming, et al. Overview of deep learning［J］. Application Research of Computers, 2012, 29（8）: 2806-2810.
［12］	GREFF K, SRIVASTAVA R K, KOUTNIK J, et al. LSTM: A search space odyssey［J］. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28（10）: 2222-2232.
［13］	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.
［14］	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.
［15］	冯宏伟, 姚博, 高原, 等. 基于边界混合采样的非均衡数据处理算法［J］. 控制与决策, 2017, 32（10）: 1831-1836.
	FENG Hongwei, YAO Bo, GAO Yuan, et al. Imbalanced data processing algorithm based on boundary mixed sampling［J］. Control and Decision, 2017, 32（10）: 1831-1836.
［16］	PERCIVAL D B, MOFJELD H O. Analysis of subtidal coastal sea level fluctuations using wavelets［J］. Journal of the American Statistical Association, 1997, 92（439）: 868-880.
［17］	NGUYEN H M, COOPER E W, KAMEI K. Borderline over-sampling for imbalanced data classification［J］. International Journal of Knowledge Engineering and Soft Data Paradigms, 2011, 3（1）: 4-21.
［18］	陆俊, 朱炎平, 彭文昊, 等. 智能用电用户行为分析特征优选策略［J］. 电力系统自动化, 2017, 41（5）: 58-63, 83.
	LU Jun, ZHU Yanping, PENG Wenhao, et al. Feature selection strategy for electricity consumption behavior analysis in smart grid［J］. Automation of Electric Power Systems, 2017, 41（5）: 58-63, 83.
［19］	李阳, 刘友波, 刘俊勇, 等. 基于形态距离的日负荷数据自适应稳健聚类算法［J］. 中国电机工程学报, 2019, 39（12）:3409-3419.
	LI Yang, LIU Youbo, LIU Junyong, et al. Self-adaptive and robust clustering algorithm for daily load profiles based on morphological distance［J］. Proceedings of the CSEE, 2019, 39（12）:3409-3419.
［20］	HOWE D A, PERCIVAL D B. Wavelet variance, allan variance, and leakage［J］. IEEE Transactions on Instrumentation and Measurement, 1995, 44（2）: 94-97.
［21］	ADANKON M M, Cheriet M. Support vector machine［J］. Computer Science, 2002, 1（4）: 1-28.
［22］	TOMEK I. A Generalization of the k-NN rule［J］. IEEE Transactions on Systems, Man and Cybernetics, 1976, SMC-6（2）: 121-126.
［23］	HAFEZ A G, RABIE M, KOHDA T. Seismic noise study for accurate P-wave arrival detection via MODWT［J］. Computers & Geosciences, 2013, 54: 148-159.
［24］	陈倩, 齐林海, 王红. 基于LSTM网络的谐波多标签分类［J］. 电力建设, 2019, 40（7）: 101-106.
	CHEN Qian, QI Linhai, WANG Hong. Harmonic multi-label classification based on LSTM［J］. Electric Power Construction, 2019, 40（7）: 101-106.
［25］	Sustainable Energy Authority of Ireland. CER_Electricity_Data. ［EB/OL］.［2019-10-15］.http://www.ucd.ie/issda.
［26］	李艳霞, 柴毅, 胡友强, 等. 不平衡数据分类方法综述［J］. 控制与决策, 2019, 34（4）: 673-688.
	LI Yanxia, CHAI Yi, HU Youqiang, et al. Review of imbalanced data classification methods［J］. Control and Decision, 2019, 34（4）: 673-688.
［27］	石亮缘, 周任军, 张武军, 等. 采用深度学习和多维模糊C均值聚类的负荷分类方法［J］.电力系统及其自动化学报, 2019, 31（7）: 43-50.
	SHI Liangyuan, ZHOU Renjun, ZHANG Wujun, et al. Load classification method using deep learning and multi-dimensional fuzzy C-means clustering［J］. Proceedings of the CSU-EPSA, 2019, 31（7）: 43-50.
［28］	刘洋, 刘洋, 许立雄, 等. 计及数据类别不平衡的海量用户负荷典型特征高性能提取方法［J］. 中国电机工程学报, 2019, 39（14）: 4093-4104.
	LIU Yang，LIU Yang，XU Lixiong，et al. A high performance extraction method for massive user load typical characteristics considering data class imbalance［J］. Proceedings of the CSEE, 2019, 39（14）: 4093-4104.
［29］	刘洋, 刘洋, 许立雄. 适用于海量负荷数据分类的高性能反向传播神经网络算法［J］. 电力系统自动化, 2018, 42（21）: 96-103.
	LIU Yang, LIU Yang, XU Liixiong. High-performance back propagation neural network algorithm for classification of mass load data［J］. Automation of Electric Power Systems, 2018, 42（21）: 96-103.

[1]	陈倩，齐林海，王红. 基于LSTM网络的谐波多标签分类[J]. 电力建设, 2019, 40(7): 101-106.
[2]	肖泽青，华昊辰，曹军威. 人工智能在能源互联网中的应用综述[J]. 电力建设, 2019, 40(5): 63-70.
[3]	杨懿男，齐林海，王红，苏林萍. 基于生成对抗网络的小样本数据生成技术研究[J]. 电力建设, 2019, 40(5): 71-77.
[4]	梁荣，杨波，马润泽，吴健，吴奎华，林振智，文福拴. 利用多源信息和深度置信神经网络的配电系统空间负荷预测[J]. 电力建设, 2018, 39(10): 12-19.
[5]	杨甲甲, 刘国龙, 赵俊华, 文福拴, 董朝阳. 采用长短期记忆深度学习模型的工业负荷短期预测方法[J]. 电力建设, 2018, 39(10): 20-27.
[6]	史佳琪，张建华. 基于深度学习的超短期光伏精细化预测模型研究 [J]. 电力建设, 2017, 38(6): 28-.
[7]	王晓辉，朱永利，郭丰娟. 基于同步监测和深度学习的电容器介损角辨识[J]. 电力建设, 2017, 38(5): 76-.
[8]	徐衍会，张蓝宇，宋歌. 基于核的模糊C均值逐层聚类算法在负荷分类中的应用[J]. 电力建设, 2015, 36(4): 46-51.
[9]	石鑫，朱永利. 深度学习神经网络在电力变压器故障诊断中的应用[J]. 电力建设, 2015, 36(12): 116-122.