Research on Performance Comprehensive Evaluation of Thermal Power Plant under Low-Carbon Economy

  • Xing Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)


A performance evaluation index system of thermal power plant is established under low carbon economy, and a comprehensive evaluation model based on principal component analysis (PCA), support vector machine (SVM) and quick sort algorithm is presented. Then experiments are made by using the real data from 17 thermal power plants, and the sequence of them is obtained ultimately. The results show that the model proposed has high accuracy, and comparing with BP network, SVM shows better performance in the condition of few data.


performance evaluation thermal power plant PCA SVM binary tree quick sort algorithm 


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  1. 1.
    Vladimir, N.V., Zhang, X.G.: Nature of Statistics Theory. Tsinghua University Press, Beijing (2000)Google Scholar
  2. 2.
    Yao, X.Y., Li, Y., Wen, Q.: Low-carbon Economy Evaluation Index System of Thermal Power Industry. Journal of Ningxia University (Natural Science Edition) 12, 389–392 (2010)Google Scholar
  3. 3.
    Li, J.: Research on Performance Evaluation Index System for Thermo-Power Enterprises Based on Sustainable Development. Journal of Beijing Polytechnic College 1, 114–118 (2010)Google Scholar
  4. 4.
    Yang, K.R., Meng, F.R., Liang, Z.Z.: Adaptively Weighted PCA Algorithm. Computer Engineering and Applications 3, 189–191 (2012)Google Scholar
  5. 5.
    Wang, Y., Yang, J.A., Liu, H., Geng, Q.: A SVM Incremental Learning Algorithm Based on Inner Hull Vectors. Journal of Circuits and Systems 6, 109–113 (2011)Google Scholar
  6. 6.
    Ding, S.F., Sun, J.G., Chen, D.L., Li, Y., Jiang, X.L.: Improved SVM Decision-tree and Its Application in Remote Sensing Classification. Application Research of Computers 3, 1146–1148 (2012)Google Scholar
  7. 7.
    Feng, G.H.: Parameter Optimizing for Support Vector Machines Classification. Computer Engineering and Applications 3, 123–124 (2011)Google Scholar
  8. 8.
    Zhang, Z.Z., Dong, C.L., Chen, Z.Z., He, X.L.: Improved Fast Classifier Based on SVM and Density Clustering. Computer Engineering and Applications 2, 136–138 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xing Zhang
    • 1
  1. 1.Department of Economy ManagementNorth China Electric Power UniversityBaodingChina

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