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An Efficient CNN and KNN Data Analytics for Electricity Load Forecasting in the Smart Grid

  • Syeda Aimal
  • Nadeem JavaidEmail author
  • Tahir Islam
  • Wazir Zada Khan
  • Mohammed Y. Aalsalem
  • Hassan Sajjad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

The significant part of smart grid is to make smart grid cost-efficient by predicting electricity price and load. To improve the prediction performance we proposed an Efficient Convolutional Neural Network (ECNN) and Efficient K-nearest Neighbour (EKNN) in which the parameters are tuned. It may be difficult to deal with huge amount of load data that is coming from the electricity market. To overcome this issue, we incorporated three modules in the proposed methodology. The proposed model consists of feature engineering and classification. Feature engineering is a two-step process (feature selection and feature extraction); for the purpose of feature selection Mutual Information (MI) is used which reduces the redundancy among features and for feature extraction Recursive Feature Elimination (RFE) is used to extract the principle features from the selected features and reduces the dimensionality of features. Finally, after training the data-set and the removal of the duplicate features load prediction is done by ECNN and EKNN. The ECNN and EKNN outperforms better then traditional Convolutional Neural Network (CNN) and K-nearest Neighbour (KNN). The forecast performance is evaluated by comparing the results with MAPE, RMSE, MAE and MSE. i.e. 10.8, 7.5, 7.15, and 10.4 respectively.

References

  1. 1.
    Lago, J., De Ridder, F., De Schutter, B.: Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms. Appl. Energy 221, 386–405 (2018)CrossRefGoogle Scholar
  2. 2.
    Liu, B., Nowotarski, J., Hong, T., Weron, R.: Probabilistic load forecasting via quantile regression averaging on sister forecasts. IEEE Trans. Smart Grid 8(2), 730–737 (2017)Google Scholar
  3. 3.
    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
  4. 4.
    Zheng, H., Yuan, J., Chen, L.: Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies 10(8), 1168 (2017)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    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
  7. 7.
    Vrablecová, 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
  8. 8.
    Liu, Y., Wang, W., Ghadimi, N.: Electricity load forecasting by an improved forecast engine for building level consumers. Energy 139, 18–30 (2017)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Singh, S., Yassine, A.: Big data mining of energy time series for behavioral analytics and energy consumption forecasting. Energies 11(2), 452 (2018)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Yu, C.N., Mirowski, P., Ho, T.K.: A sparse coding approach to household electricity demand forecasting in smart grids. IEEE Trans. Smart Grid 8(2), 738–748 (2017)Google Scholar
  14. 14.
    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
  15. 15.
    Chitsaz, H., Zamani-Dehkordi, P., Zareipour, H., Parikh, P.P.: Electricity price forecasting for operational scheduling of behind-the-meter storage systems. IEEE Trans. Smart Grid 9(6), 6612–6622 (2018)CrossRefGoogle Scholar
  16. 16.
    Moghaddass, R., Wang, J.: A hierarchical framework for smart grid anomaly detection using large-scale smart meter data. IEEE Trans. Smart Grid 9(6), 5820–5830 (2018)CrossRefGoogle Scholar
  17. 17.
    Singh, N., Mohanty, S.R., Shukla, R.D.: Short term electricity price forecast based on environmentally adapted generalized neuron. Energy 125, 127–139 (2017)CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Bassamzadeh, N., Ghanem, R.: Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks. Appl. Energy 193, 369–380 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Syeda Aimal
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Tahir Islam
    • 1
  • Wazir Zada Khan
    • 2
  • Mohammed Y. Aalsalem
    • 2
  • Hassan Sajjad
    • 1
  1. 1.COMSATS UniversityIslamabadPakistan
  2. 2.Farasan Networking Research Laboratory, Department of Computer Science and Information SystemJazan UniversityJazanSaudi Arabia

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