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Accurate Moving Distance Estimation via Multi-modal Fusion from IMU Sensors and WiFi Signal Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Power Missing Data Filling Based on Improved k-Means Algorithm and RBF Neural Network

Authors : Zhan Shi, Xingnan Li, Zhuo Su

Published in: Cloud Computing and Security

Publisher: Springer International Publishing

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Abstract

Power data mainly comes from power generation, transmission, consumption, scheduling and statistics. However, in the process of power data acquisition, problems such as data missing seriously affect the further analysis. In this paper, we propose a missing data filling method based on improved k-Means clustering and Radial Basis Function neural network (kM-RBF) to solve the problem of missing power data. Firstly, the data samples are clustered by k-Means, and the clustering results are used as the parameters of RBF neural network. The RBF neural network is trained with the complete data samples, and then the missing values are predicted. In order to verify the effectiveness of the algorithm, we have chosen the power consumption and power generation metadata of each province in China for analysis and simulated the absence of data. Simulation results show that the kM-RBF can obtain higher accuracy of missing data filling.

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Literature
1.
Shi, W., Zhu, Y., Zhang, J., et al.: Improving power grid monitoring data quality: an efficient machine learning framework for missing data prediction. In: IEEE International Conference on High Performance Computing and Communications, pp. 417–422. IEEE (2015)
2.
Yang, M., Ma, J.: Data completing of missing wind power data based on adaptive BP neural network. In: International Conference on Probabilistic Methods Applied to Power Systems, pp. 1–6. IEEE (2016)
3.
Leke, C., Twala, B., Marwala, T.: Modeling of missing data prediction: computational intelligence and optimization algorithms. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1400–1404. IEEE (2014)
4.
Zhang, W., Yang, Y., Wang, Q.: Handling missing data in software effort prediction with Naive Bayes and EM algorithm. In: International Conference on Predictive MODELS in Software Engineering, p. 4. ACM (2011)
5.
Yang, Y., Bo, L.C.: A method of filling the missing data based on the limit learning machine. Comput. Appl. Softw. 33(10), 243–246 (2016)
6.
Wu, W., Peng, M.: A data mining approach combining k-Means clustering with bagging neural network for short-term wind power forecasting. IEEE Internet of Things J. 4(4), 979–986 (2017)MathSciNetCrossRef
7.
Han, H.G., Qiao, J.F.: Adaptive computation algorithm for RBF neural network. IEEE Trans. Neural Netw. Learn. Syst. 23(2), 342–347 (2012)CrossRef
8.
Wei, W., Tang, Y.: A generic neural network approach for filling missing data in data mining. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 862–867. IEEE (2003)
9.
He, G., Peng, S., Liu, X., et al.: Missing human motion capture data recovery via fuzzy clustering and projected proximal point algorithm. J. Comput. Aided Des. Comput. Graph. 27(8), 1417–1427 (2015)
10.
Tian, J., Yu, B., Yu, D., et al.: Missing data analyses: a hybrid multiple imputation algorithm using gray system theory and entropy based on clustering. Appl. Intell. 40(2), 376–388 (2014)CrossRef
11.
Chi, J.T., Chi, E.C., Baraniuk, R.G.: k-POD: a method for k-Means clustering of missing data. Am. Stat. 70(1), 91–99 (2014)MathSciNetCrossRef
12.
Leng, Y., Chen, Z., Zhang, Q., et al.: Distributed clustering and filling algorithm of incomplete big data. Comput. Eng. (2015)
13.
Abudu, S., Bawazir, A.S., King, J.P.: Infilling missing daily evapotranspiration data using neural networks. J. Irrig. Drain. Eng. 136(5), 317–325 (2010)CrossRef
14.
Fanyu, B.U., Chen, Z., Zhang, Q.: Missing value imputation algorithm based on clustering and autoencoder. Comput. Eng. Appl. (2015)
15.
Zhang, Y., Zhang, D., Shi, H.: K-means clustering based on self-adaptive weight. In: International Conference on Computer Science and Network Technology, pp. 1540–1544. IEEE (2013)
16.
Lei, G.: A novel sample weighting K-means clustering algorithm based on angles information. In: International Joint Conference on Neural Networks, pp. 3697–3702. IEEE (2016)
17.
van den Berq, R.A., Hoefsloot, H.C., Westerhuis, J.A., et al.: Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genom. 7(1), 142 (2006)CrossRef
18.
Shao, J., Sun, G., Yang, G., et al.: EasyRBF: towards infilling missing soil data. In: International Conference on Big Data Computing and Communications, pp. 376–385. IEEE Computer Society (2017)
19.
Ji, Y., Hong, W., Qi, J.: Missing value prediction using co-clustering and RBF for collaborative filtering. In: International Conference on Cloud Computing and Big Data, pp. 350–353. IEEE Computer Society (2015)
Metadata
Title
Power Missing Data Filling Based on Improved k-Means Algorithm and RBF Neural Network
Authors
Zhan Shi
Xingnan Li
Zhuo Su
Copyright Year
2018
Book
Cloud Computing and Security

Print ISBN: 978-3-030-00017-2

Electronic ISBN: 978-3-030-00018-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00018-9_48

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