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Feature Selection and Extraction Along with Electricity Price Forecasting Using Big Data Analytics

  • Isra Shafi
  • Nadeem JavaidEmail author
  • Aqdas Naz
  • Yasir Amir
  • Israr Ishaq
  • Kashif Naseem
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 773)

Abstract

The most important part of the smart grid (SG) is prediction of electricity price and by this prediction SG becomes cost efficient. To tackle with large amount of data in SG, it is a challenging task for existing techniques to accurately predict the electricity price. So, to handle the above mentioned problem, a framework has been proposed with three different steps: feature selection, feature extraction and classification. The purpose of feature selection is to remove irrelevant data by using extra tree classifier on the basis of pearson correlation coefficient. Feature extraction is performed using t-distributed stochastic neighbor embedding method to reduce redundancy from the selected data. For accurate electricity price forecasting, support vector machine classifier is used. Simulation results show that the proposed framework outperforms than the other methods.

Keywords

Forecasting Electricity price Support vector machine Extra tree classifier 

References

  1. 1.
    Kezunovic, M., Xie, L., Grijalva, S.: The role of big data in improving power system operation and protection. In: 2013 IREP Symposium on Bulk Power System Dynamics and Control-IX Optimization, Security and Control of the Emerging Power Grid (IREP), pp. 1–9. IEEE (2013)Google Scholar
  2. 2.
    Munshi, A.A., Yasser, A.R.M.: Big data framework for analytics in smart grids. Electr. Power Syst. Res. 151, 369–380 (2017)CrossRefGoogle Scholar
  3. 3.
    Hou, W., Ning, Z., Guo, L., Zhang, X.: Temporal, functional and spatial big data computing framework for large-scale smart grid. IEEE Trans. Emerg. Topics Comput. (2017)Google Scholar
  4. 4.
    Wang, K., Xu, C., Zhang, Y., Guo, S., Zomaya, A.: Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans. Big Data (2017)Google Scholar
  5. 5.
    Varshney, H., Sharma, A., Kumar, R.: A hybrid approach to price forecasting incorporating exogenous variables for a day ahead electricity market. In: IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1–6. IEEE, July 2016Google Scholar
  6. 6.
    Kanao, N., Yamashita, M., Yanagida, H., Mizukami, M., Hayashi, Y., Matsuki, J.: Power system harmonic analysis using state-estimation method for Japanese field data. IEEE Trans. Power Delivery 20(2), 970–977 (2005)CrossRefGoogle Scholar
  7. 7.
    Rafiei, M., Niknam, T., Khooban, M.H.: Probabilistic forecasting of hourly electricity price by generalization of ELM for usage in improved wavelet neural network. IEEE Trans. Ind. Inform. 13(1), 71–79 (2017)CrossRefGoogle Scholar
  8. 8.
    Ozozen, A., Kayakutlu, G., Ketterer, M., Kayalica, O.: A combined seasonal ARIMA and ANN model for improved results in electricity spot price forecasting: case study in Turkey. In: 2016 Portland International Conference on Management of Engineering and Technology (PICMET), pp. 2681–2690. IEEE, September 2016Google Scholar
  9. 9.
    González, J.P., San Roque, A.M., Pérez, E.A.: Forecasting functional time series with a new Hilbertian ARMAX model: application to electricity price forecasting. IEEE Trans. Power Syst. 33(1), 545–556 (2018)CrossRefGoogle Scholar
  10. 10.
    Zhao, J., Dong, Z., Li, X.: Electricity price forecasting with effective feature preprocessing. In: IEEE Power Engineering Society General Meeting, p. 8-pp. IEEE, January 2006Google Scholar
  11. 11.
    Han, H., Dang, J., Ren, E.: Comparative study of two uncertain support vector machines. In: 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), pp. 388–390. IEEE, October 2012Google Scholar
  12. 12.
    Li, Y., Guo, P., Li, X.: Short-term load forecasting based on the analysis of user electricity behavior. Algorithms 9(4), 80 (2016)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Wang, K., Hu, X., Li, H., Li, P., Zeng, D., Guo, S.: A survey on energy internet communications for sustainability. IEEE Trans. Sustain. Comput. 2(3), 231–254 (2017)CrossRefGoogle Scholar
  14. 14.
    Bakar, N.A., Rosbi, S.: Robust statistical pearson correlation diagnostics for bitcoin exchange rate with trading volume: an analysis of high frequency data in high volatility environment (2017)CrossRefGoogle Scholar
  15. 15.
    Ramadevi, G.N., Rani, K.U., Lavanya, D.: Importance of feature extraction for classification of breast cancer datasets-a study. Int. J. Sci. Innov. Math. Res. 3(2), 763–768 (2015)Google Scholar
  16. 16.
    Abdelmoula, W.M., Pezzotti, N., Hölt, T., Dijkstra, J., Vilanova, A., McDonnell, L.A., Lelieveldt, B.P.: Interactive visual exploration of 3D mass spectrometry imaging data using hierarchical stochastic neighbor embedding reveals spatiomolecular structures at full data resolution. J. Proteome Res. 17(3), 1054–1064 (2018)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Isra Shafi
    • 1
  • Nadeem Javaid
    • 2
    Email author
  • Aqdas Naz
    • 2
  • Yasir Amir
    • 1
  • Israr Ishaq
    • 1
  • Kashif Naseem
    • 1
  1. 1.Department of Computing and TechnologyAbasyn UniversityIslamabadPakistan
  2. 2.COMSATS Institute of Information TechnologyIslamabadPakistan

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