计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 128-133.doi: 10.11896/jsjkx.191200152
李培冠1, 於志勇1,2, 黄昉菀1,2
LI Pei-guan1, YU Zhi-yong1,2, HUANG Fang-wan1,2
摘要: 数据缺失在电力负荷数据采集过程中经常发生,对提高算法的预测精确度带来了不利影响。现有的缺失数据补全算法只适用于缺失数据量较少的情况,而对于缺失数据较多的情况表现不佳。面对严重数据缺失的挑战,文中提出了一种基于稀疏表示的电力负荷缺失数据补全方法。首先以数据随机缺失为前提,将训练数据中假定缺失后的数据与完整的训练数据上下拼接构成训练矩阵;其次,利用离散余弦变换(Discrete Cosine Transform,DCT)生成一个过完备字典,并根据训练矩阵对其进行学习,旨在通过调优得到一个合适的字典,能对训练矩阵中的样本进行最好的稀疏表示。最后,在测试阶段,先利用学习后字典的上半部分获得测试集缺失数据的稀疏表示,然后利用稀疏表示和学习后字典的下半部分重构出无缺失的完整数据。实验结果表明,使用该方法对电力负荷数据缺失值进行补全,可以获得比传统插值方法、基于相关性的KNN算法、时空压缩感知估计算法以及时序压缩感知预测算法更高的精度。即使数据缺失率高达95%,该方法依然可以有效地补全缺失数据。
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[1] FAN W Q,ZHANG W,LI Y G,et al.Ultra short-term loadforecasting for micro-grid based on improved human comfort index[J].Guangdong Electric Power,2017,30(4):137-142. [2] LI H.Residual error GM(1,1) model improved by Markovmethod for long-term and medium-term load forecasting[J].Shaanxi Electric Power,2017,45(5):75-77. [3] YANG H X,DENG Y J,LIU Z B,et al.Study on electric load forecasting with historical bad data[J].Power System Protection and Control,2017,45(15):62-68. [4] CHEN Z H,ZHANG Y,WU Z G.Application of RBF neural network in medium and long-term load forecasting[J].Procee-dings of the CSU-EPSA,2006,18(1):15-19. [5] LAKSHMINARAYAN K.Imputation of missing data in industrial databases[J].Applied Intelligence,1999,11:259-275. [6] WU S F,CHANG C Y,LEE S J.Time series forecasting with missing values[C]//2015 1st International Conference on Industrial Networks and Intelligent Systems (INISCom).2015:151-156. [7] DING Q,LI S.Research on Resampling Application of Intelli-gent Substation and Error Analysis of Linear Interpolation Method[J].Power System Protection and Control,2015,43(23):132-136. [8] ZHU Q W,YE L,ZHAO Y N,et al.Research on Identification and Reconstruction Method of Wind Farm Output Power Abnormal Data[J].Power System Protection and Control,2015,43(3):38-45. [9] RUAN Q Z,CHEN J B,ZHU G,et al.Instantaneous test data analysis of low voltage electrical equipment based on cubic spline interpolation[J].Low Voltage Electrical Appliance,2012(10):27-31. [10] TAO T Y,WANG H.Simplified wind field simulation based on Hermite interpolation[J].Engineering Mechanics,2017,34(3):182-188. [11] GERHARD T,SHAHLA R.Improved methods for the imputation of missing data by nearest neighbor methods[J].Computational Statistics and Data Analysis,2015,90:84-99. [12] NEWSHAM G R,BIRT B J.Building-level occupancy data to improve arima-based electricity use forecasts[C]//Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building,ACM.New York,USA,2010:13-18. [13] SHI W,ZHU Y,ZHANG J,et al.Improving power grid monitoring data quality:An efficient machine learning framework for missing data prediction[C]//2015 IEEE 17th International Conference on High Performance Computing and Communications.IEEE,2015:417-422. [14] KONG L,XIA M,LIU X Y,et al.Data loss and reconstruction in sensor networks[C]//INFOCOM.2013:1654-1662. [15] ZHU Y,LI Z,ZHU H,et al.A Compressive Sensing Approach to Urban Traffic Estimation with Probe Vehicles[J].IEEE Transactions on Mobile Computing,2013,12(11):2289-2302. [16] SONG X X,GUO Y,LI N,et al.Missing Data Prediction Based on Compressive Sensing in Time Series[J].Computer Science,2019,46(6):35-40. [17] ZHANG Y,ROUGHAN M,WILINGER W,et al.Spatio-tem-poral compressive sensing and internet traffic matrices[J].ACM SIGCOMM Computer Communication Review,2009,39(4):267. [18] MEI J L,YOHANN D C,YANNIG G,et al.Nonnegative matrix factorization with side information for time series recovery and prediction[J].IEEE Transactions on Knowledge and Data Engineering,2018:1. [19] WANG Z H,HORNG G J,HSU T H,et al.Heart sound signal recovery based on time series signal prediction using a recurrent neural network in the long short-term memory model[J].The Journal of Supercomputing,2019(1):1-18. [20] STRAUMAN A S,BIANCHI F M,MIKALSEN K Ø.Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks[C]//International Conference on Biomedical & Health Informatics.Las Vegas,USA,2018:307-310. [21] ZHANG Z,XU Y,YANG J,et al.A survey of sparse representation:algorithms and applications[J].Access IEEE,2015,3:490-530. [22] AHARON M,ELAD M,BRUCKSTEINA.K-SVD:an algo-rithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322. |
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