Data Analytics for Electricity Load and Price Forecasting in the Smart Grid

  • Syeda Aimal
  • Nadeem JavaidEmail author
  • Amjad Rehman
  • Nasir Ayub
  • Tanzeela Sultana
  • Aroosa Tahir
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


The present strategies for the prediction of price and load may be difficult to deal with huge amount of load and price data. To resolve the problem, three modules are incorporated within the model. Firstly, the fusion of Decision Tree (DT) and Random Forest (RF) are used for feature selection and to remove the redundancy among feature. Secondly, Recursive Feature Elimination (RFE) is taken for feature extraction purpose that extracts the principle components and also used for dimensionality reduction. Finally, to forecast load and price, Support Vector Machine (SVM) and Logistic Regression (LR) as a classifiers are used through which we achieve good accuracy results in load and price prediction.



This research is supported by Al Yamamah university Riyadh Saudi Arabia.


  1. 1.
    Zhang, D., Li, S., Sun, M., O’Neill, Z.: An optimal and learning-based demand response and home energy management system. IEEE Trans. Smart Grid 7(4), 1790–1801 (2016)CrossRefGoogle Scholar
  2. 2.
    Javaid, N., Hafeez, G., Iqbal, S., Alrajeh, N., Alabed, M.S., Guizani, M.: Energy efficient integration of renewable energy sources in the smart grid for demand side management. IEEE Access 6, 77077 (2018)CrossRefGoogle Scholar
  3. 3.
    Jindal, A., Singh, M., Kumar, N.: Consumption-aware data analytical demand response scheme for peak load reduction in smart grid. IEEE Trans. Ind. Electron. (2018)Google Scholar
  4. 4.
    Wang, K., Xu, C., Guo, S.: Big data analytics for price forecasting in smart grids. In: 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2016)Google Scholar
  5. 5.
    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
  6. 6.
    Moon, J., Kim, K.H., Kim, Y., Hwang, E.: A short-term electric load forecasting scheme using 2-stage predictive analytics. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 219–226. IEEE (2018)Google Scholar
  7. 7.
    Keles, D., Scelle, J., Paraschiv, F., Fichtner, W.: Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl. Energy 162, 218–230 (2016)CrossRefGoogle Scholar
  8. 8.
    Chen, P.C., Kezunovic, M.: Fuzzy logic approach to predictive risk analysis in distribution outage management. IEEE Trans. Smart Grid 7(6), 2827–2836 (2016)CrossRefGoogle Scholar
  9. 9.
    Mujeeb, S., Javaid, N., Akbar, M., Khalid, R., Nazeer, O., Khan, M.: Big data analytics for price and load forecasting in smart grids. In: International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 77–87. Springer, Cham (2018)Google Scholar
  10. 10.
    Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.A.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)CrossRefGoogle Scholar
  11. 11.
    Mahmood, D., Javaid, N., Ahmed, I., Alrajeh, N., Niaz, I.A., Khan, Z.A.: Multi-agent-based sharing power economy for a smart community. Int. J. Energy Res. 41, 2074 (2017)CrossRefGoogle Scholar
  12. 12.
    Zhao, Z., Lee, W.C., Shin, Y., Song, K.B.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)CrossRefGoogle Scholar
  13. 13.
    Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)CrossRefGoogle Scholar
  14. 14.
    Ahmad, A., Javaid, N., Alrajeh, N., Khan, Z.A., Qasim, U., Khan, A.: A modified feature selection and artificial neural network-based day-ahead load forecasting model for a smart grid. Appl. Sci. 5(4), 1756–1772 (2015)CrossRefGoogle Scholar
  15. 15.
    Ahmed, M.S., Mohamed, A., Khatib, T., Shareef, H., Homod, R.Z., Ali, J.A.: Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy Build. 138, 215–227 (2017)CrossRefGoogle Scholar
  16. 16.
    Ahmad, A., Javaid, N., Guizani, M., Alrajeh, N., Khan, Z.A.: An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid. IEEE Trans. Ind. Inform. 13(5), 2587–2596 (2017)CrossRefGoogle Scholar
  17. 17.
    Wu, M., Wang, Y.: A feature selection algorithm of music genre classification based on ReliefF and SFS. In: IEEE International Conference on Computer and Information Science (ICIS), pp. 539–544 (2009). Processing and Communications Applications, 2009, pp. 61–64Google Scholar
  18. 18.
    Wu, M., Wang, Y.: A feature selection algorithm of music genre classification based on ReliefF and SFS. In: IEEE International Conference on Computer and Information Science (ICIS), pp. 539–544 (2015)Google Scholar
  19. 19.
    Fleury, A., Vacher, M., Noury, N.: SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans. Inf. Technol. Biomed. 14(2), 274–283 (2010)CrossRefGoogle Scholar
  20. 20.
    Huang, D., Zareipour, H., Rosehart, W.D., Amjady, N.: Data mining for electricity price classification and the application to demand-side management. IEEE Trans. Smart Grid 3(2), 808–817 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Syeda Aimal
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Amjad Rehman
    • 2
  • Nasir Ayub
    • 1
  • Tanzeela Sultana
    • 1
  • Aroosa Tahir
    • 3
  1. 1.COMSATS UniversityIslamabadPakistan
  2. 2.Al Yamamah UniversityRiyadhSaudi Arabia
  3. 3.Sardar Bahadur Khan Women UniversityQuettaPakistan

Personalised recommendations