Advertisement

An Innovative Model Based on FCRBM for Load Forecasting in the Smart Grid

  • Ghulam Hafeez
  • Nadeem JavaidEmail author
  • Muhammad Riaz
  • Khalid Umar
  • Zafar Iqbal
  • Ammar Ali
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 994)

Abstract

In this paper, an efficient model based on factored conditional restricted boltzmann machine (FCRBM) is proposed for electric load forecasting of in smart grid (SG). This FCRBM has deep layers structure and uses rectified linear unit (RELU) function and multivariate autoregressive algorithm for training. The proposed model predicts day ahead and week ahead electric load for decision making of the SG. The proposed model is a hybrid model having four modules i.e., data processing and features selection module, FCRBM based forecaster module, GWDO (genetic wind driven optimization) algorithm-based optimizer module, and utilization module. The proposed model is examined using FE grid data of USA. The proposed model provides more accurate results with affordable execution time than other load forecasting models, i.e., mutual information, modified enhanced differential evolution algorithm, and artificial neural network (ANN) based model (MI-mEDE-ANN), accurate fast converging short term load forecasting model (AFC-STLF), Bi-level model, and features selection and ANN-based model (FS-ANN).

References

  1. 1.
    Zhang, X., Wang, J., Zhang, K.: Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm. Electr. Power Syst. Res. 146, 270–285 (2017)CrossRefGoogle Scholar
  2. 2.
    Javaid, N., Hafeez, v., Iqbal, S., Alrajeh, N.: Mohamad souheil alabed, and mohsen guizani. Energy efficient integration of renewable energy sources in the smart grid for demand side management. IEEE Access 6, 77077–77096 (2018)Google Scholar
  3. 3.
    Hafeez, G., Javaid, N., Iqbal, S., Khan, F.: Optimal residential load scheduling under utility and rooftop photovoltaic units. Energies 11(3), 611 (2018)CrossRefGoogle Scholar
  4. 4.
    Lin, C.-T., Chou, L.-D.: A novel economy reflecting short-term load forecasting approach. Energy Convers. Manag. 65, 331–342 (2013)CrossRefGoogle Scholar
  5. 5.
    Ryu, S., Noh, J., Kim, H.: Deep neural network based demand side short term load forecasting. Energies 10(1), 3 (2016)CrossRefGoogle Scholar
  6. 6.
    Li, H.-Z., Guo, S., Li, C.-J., Sun, J.-Q.: A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl.-Based Syst. 37, 378–387 (2013)Google Scholar
  7. 7.
    Chen, Y., Yang, Y., Liu, C., Li, C., Li, L.: A hybrid application algorithm based on the support vector machine and artificial intelligence: an example of electric load forecasting. Appl. Math. Model. 39, 2617–2632 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hahn, H., Meyer-Nieberg, S., Pickl, S.: Electric load forecasting methods: tools for decision making. Eur. J. Oper. Res. 199(3), 902–907 (2009)CrossRefGoogle Scholar
  9. 9.
    Taylor, J.W.: An evaluation of methods for very short-term load forecasting using minute-by-minute British data. Int. J. Forecast. 24(4), 645–658 (2008)CrossRefGoogle Scholar
  10. 10.
    D. Felice, M., Yao, X.: Short-term load forecasting with neural network ensembles: a comparative study [application notes]. IEEE Comput. Intell. Mag. 6(3), 47–56 (2011)Google Scholar
  11. 11.
    Pedregal, D.J., Trapero, J.R.: Mid-term hourly electricity forecasting based on a multi-rate approach. Energy Convers. Manag. 51(1), 105–111 (2010)CrossRefGoogle Scholar
  12. 12.
    Filik, Ü.B., Gerek, Ö.N., Kurban, M.: A novel modeling approach for hourly forecasting of long-term electric energy demand. Energy Convers. Manag. 52(1), 199–211 (2011)CrossRefGoogle Scholar
  13. 13.
    López, M., Valero, S., Senabre, C., Aparicio, J., Gabaldon, A.: Application of SOM neural networks to short-term load forecasting: the spanish electricity market case study. Electr. Power Syst. Res. 91, 18–27 (2012)CrossRefGoogle Scholar
  14. 14.
    Zjavka, L., Snášel, V.: Short-term power load forecasting with ordinary differential equation substitutions of polynomial networks. Electr. Power Syst. Res. 137, 113–123 (2016)CrossRefGoogle Scholar
  15. 15.
    Liu, D., Zeng, L., Li, C., Ma, K., Chen, Y., Cao, Y.: A distributed short-term load forecasting method based on local weather information. IEEE Syst. J. 12(1), 208–215 (2018)CrossRefGoogle Scholar
  16. 16.
    Ghadimi, N., Akbarimajd, A., Shayeghi, H., Abedinia, O.: Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy 161, 130–142 (2018)CrossRefGoogle Scholar
  17. 17.
    Vrablecova, P., Ezzeddine, A.B., Rozinajová, V., Šárik, S., Sangaiah, A.K.: Smart grid load forecasting using online support vector regression. Comput. & Electr. Eng. 65, 102–117 (2018)CrossRefGoogle Scholar
  18. 18.
    González, J.P., San Roque, A.M., Perez, 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
  19. 19.
    Luo, J., Hong, T., Fang, S.-C.: Benchmarking robustness of load forecasting models under data integrity attacks. Int. J. Forecast. 34(1), 89–104 (2018)CrossRefGoogle Scholar
  20. 20.
    Ahmad, A., Javaid, N., Mateen, A., Awais, M., Khan, Z.: Short-term load forecasting in smart grids: an intelligent modular approach. Energies 12(1), 164 (2019)CrossRefGoogle Scholar
  21. 21.
    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
  22. 22.
    Amjady, N., Keynia, F., Zareipour, H.: Short-term load forecast of microgrids by a new bilevel prediction strategy. IEEE Trans. Smart Grid 1(3), 286–294 (2010)CrossRefGoogle Scholar
  23. 23.
    Amjady, N., Keynia, F.: Day-ahead price forecasting of electricity markets by mutual information technique and cascaded neuro-evolutionary algorithm. IEEE Trans. Power Syst. 24(1), 306–318 (2009)CrossRefGoogle Scholar
  24. 24.
    Abedinia, O., Amjady, N., Zareipour, H.: A new feature selection technique for load and price forecast of electrical power systems. IEEE Trans. Power Syst. 32(1), 62–74 (2017)CrossRefGoogle Scholar
  25. 25.
    Available online: https://www.pjm.com/. Accessed 8 March 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ghulam Hafeez
    • 1
    • 2
  • Nadeem Javaid
    • 1
    Email author
  • Muhammad Riaz
    • 2
  • Khalid Umar
    • 3
  • Zafar Iqbal
    • 4
  • Ammar Ali
    • 1
  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.University of Engineering and TechnologyMardanPakistan
  3. 3.Bahria University IslambadIslamabdPakistan
  4. 4.PMAS Agriculture UniversityRawalpindiPakistan

Personalised recommendations