Short Term Load Forecasting Using Neural Network with Rough Set

  • Zhi Xiao
  • Shi-Jie Ye
  • Bo Zhong
  • Cai-Xin Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


Accurate Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors in order to develop the accuracy of predictions. Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical system, we tested the accuracy of forecasting in specific days with comparison.


Back Propagation Back Propagation Neural Network Decision Attribute Load Forecast Condition Attribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Carpinteiro, O.A.S., Reis, A.J.R., Alves da Silva, A.P.: A Hierarchical Neural Model in Short-Term Load Forecasting. Applied Soft Computing 4, 405–412 (2004)CrossRefGoogle Scholar
  2. 2.
    Topallia, A.K., Erkmenb, I.: A Hybrid Learning for Neural Networks Applied to Short Term Load Forecasting. Neurocomputing 51, 495–500 (2003)CrossRefGoogle Scholar
  3. 3.
    Liao, G., Tsao, T.: Application of Fuzzy Neural Networks and Artificial Intelligence for Load Forecasting. Electric Power Systems Research 70, 237–244 (2004)CrossRefGoogle Scholar
  4. 4.
    Satish, B., Swarup, K.S., Srinivas, S., Rao, A.H.: Effect of Temperature on Short Term Load Forecasting Using an Integrated ANN. Electric Power Systems Research 72, 95–101 (2004)CrossRefGoogle Scholar
  5. 5.
    Hong, W.C.: Forecasting Regional Electricity Load Based on Recurrent Support Vector Machines with Genetic Algorithms. Electric Power Systems Research 74, 417–425 (2005)CrossRefGoogle Scholar
  6. 6.
    Al-Kandari, A.M., Soliman, S.A., El-Hawary, M.E.: Fuzzy Short-Term Electric Load Forecasting. International Journal of Electrical Power and Energy Systems 26, 111–122 (2004)CrossRefGoogle Scholar
  7. 7.
    Kodogiannis, V.S., Anagnostakis, E.M.: Soft Computing Based Techniques for Short-Term Load Forecasting. Fuzzy Sets and Systems 128, 413–426 (2002)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Pai, P., Hong, W.: Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting. Energy Conversion and Management 46, 2669–2688 (2005)CrossRefGoogle Scholar
  9. 9.
    Yao, S.J., Song, Y.H., Zhang, L.Z., Cheng, X.Y.: Wavelet Transform and Neural Networks for Short-Term Electrical Load Forecasting. Energy Conversion and Management 41, 1975–1988 (2000)CrossRefGoogle Scholar
  10. 10.
    Zhang, B., Dong, Z.: An Adaptive Neural-Wavelet Model for Short Term Load Forecasting. Electric Power Systems Research 59, 121–129 (2001)CrossRefGoogle Scholar
  11. 11.
    Rady, E.A., Kozae, A.M., Abd El-Monsef, M.M.E.: Generalized Rough Sets. Chaos Solitons and Fractals 21, 49–53 (2004)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Hong, T., Tseng, L., Wang, S.: Learning Rules from Incomplete Training Examples by Rough Set. Expert Systems with Applications 22, 285–293 (2002)CrossRefGoogle Scholar
  13. 13.
    Zhang, D., Bi, Y.Q., Bi, Y.X., Bi, Y.M., Niu, Z., Luo, L.: Power Load Forecasting Method Base on Serial Grey Neural Network. Theory and Practice on Sysytem Engineering, 128–132 (2004)Google Scholar
  14. 14.
    Gao, J., Sun, H., Tang, G.: Application of Optimal Combined Forecasting Based on Fuzzy Synthetic Evaluation on Power Load Forecast. Journal of Systems Engineering 16, 106–110 (2001)Google Scholar
  15. 15.
    Zhao, H.: The Application to Power Load Forecasting of an Optimization Combinatorial Prediction Modal. Operation Research and Management Science 14, 115–118 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhi Xiao
    • 1
  • Shi-Jie Ye
    • 1
  • Bo Zhong
    • 2
  • Cai-Xin Sun
    • 3
  1. 1.College of Economics and Business AdministrationChongqing UniversityChongqingChina
  2. 2.College of Mathematics and PhysicsChongqing UniversityChongqingChina
  3. 3.Key Laboratory of High Voltage Engineering and Electrical New Technology, Ministry of EducationChongqing UniversityChongqingChina

Personalised recommendations