Advertisement

Electricity Price Forecasting Based on Enhanced Convolutional Neural Network in Smart Grid

Conference paper
  • 1.1k Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1150)

Abstract

Electricity price forecasting is significant component of smart grid. Electricity systems are managed by the electricity market. The market operators perform electricity price forecasting for an efficient energy management. This paper deals with the electricity price forecasting based on deep learning. The fluctuations in electricity prices are due to the increase in fuel prices, demand of electricity and social variables such as weather conditions, peak hours, weekdays, weekends and seasons. Therefore, there is a need to maintain equilibrium between shortage and overflow of the electricity. Deep learning is most widely used for classification, image recognition and forecasting. The proposed work is categorized into two stages: first stage is feature engineering, in which features selection is performed by Xgboost technique, while features extraction is done through Linear Discriminant Analysis (LDA). These techniques reduce the dimensionality of data and forward important data to classifier for electricity price forecasting. Second stage is price forecasting, which is based on Enhanced Convolutional Neural Network (ECNN) classifier. For validation of proposed work, three performance metrics (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE)) are used. Simulation results show that our proposed scheme outperforms existing benchmark techniques in terms of price forecasting.

References

  1. 1.
    Chen, Y., Tan, H., Song, X.: Day-ahead forecasting of non-stationary electric power demand in commercial buildings: hybrid support vector regression based. Energy Procedia 105, 2101–2106 (2017)CrossRefGoogle Scholar
  2. 2.
    Guo, Z., Zhou, K., Zhang, C., Lu, X., Chen, W., Yang, S.: Residential electricity consumption behavior: influencing factors, related theories and intervention strategies. Renew. Sustain. Energy Rev. 81, 399–412 (2018)CrossRefGoogle Scholar
  3. 3.
    Tang, N., Mao, S., Wang, Y., Nelms, R.M.: Solar power generation forecasting with a LASSO-based approach. IEEE Internet Things J. 5(2), 1090–1099 (2018)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    Agrawal, R.K., Muchahary, F., Tripathi, M.M.: Ensemble of relevance vector machines and boosted trees for electricity price forecasting. Appl. Energy 250, 540–548 (2019)CrossRefGoogle Scholar
  7. 7.
    Aryanpur, V., Atabaki, M.S., Marzband, M., Siano, P., Ghayoumi, K.: An overview of energy planning in Iran and transition pathways towards sustainable electricity supply sector. Renew. Sustain. Energy Rev. 112, 58–74 (2019)CrossRefGoogle Scholar
  8. 8.
    Wang, F., Li, K., Zhou, L., Ren, H., Contreras, J., Shafie-Khah, M., Catalão, J.P.: Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting. Int. J. Electr. Power Energy Syst. 105, 529–540 (2019)CrossRefGoogle Scholar
  9. 9.
    Wang, J., Liu, F., Song, Y., Zhao, J.: A novel model: dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. Appl. Soft Comput. 48, 281–297 (2016)CrossRefGoogle Scholar
  10. 10.
    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 (2017)CrossRefGoogle Scholar
  11. 11.
    Xiao, F., Wang, S., Fan, C.: Mining big building operational data for building cooling load prediction and energy efficiency improvement. In: 2017 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–3. IEEE, May 2017Google Scholar
  12. 12.
    Kuo, P.H., Huang, C.J.: An electricity price forecasting model by hybrid structured deep neural networks. Sustainability 10(4), 1280 (2018)CrossRefGoogle Scholar
  13. 13.
    Gholipour Khajeh, M., Maleki, A., Rosen, M.A., Ahmadi, M.H.: Electricity price forecasting using neural networks with an improved iterative training algorithm. Int. J. Ambient Energy 39(2), 147–158 (2018)CrossRefGoogle Scholar
  14. 14.
    Jiang, L., Hu, G.: Day-ahead price forecasting for electricity market using long-short term memory recurrent neural network. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 949-954. IEEE, November 2018Google Scholar
  15. 15.
    Ugurlu, U., Oksuz, I., Tas, O.: Electricity price forecasting using recurrent neural networks. Energies 11(5), 1255 (2018)CrossRefGoogle Scholar
  16. 16.
    Afrasiabi, M., Mohammadi, M., Rastegar, M., Kargarian, A.: Probabilistic deep neural network price forecasting based on residential load and wind speed predictions. IET Renew. Power Gener. 13(11), 1840–1848 (2019)CrossRefGoogle Scholar
  17. 17.
    Yixian, L.I.U., Roberts, M.C., Sioshansi, R.: A vector autoregression weather model for electricity supply and demand modeling. J. Mod. Power Syst. Clean Energy 6(4), 763–776 (2018)CrossRefGoogle Scholar
  18. 18.
    Wang, K., Xu, C., Zhang, Y., Guo, S., Zomaya, A.Y.: Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans. Big Data 5(1), 34–45 (2017)CrossRefGoogle Scholar
  19. 19.
    Chen, Y., Kloft, M., Yang, Y., Li, C., Li, L.: Mixed kernel based extreme learning machine for electric load forecasting. Neurocomputing 312, 90–106 (2018)CrossRefGoogle Scholar
  20. 20.
    Chen, K., Hu, J., He, J.: A framework for automatically extracting overvoltage features based on sparse autoencoder. IEEE Trans. Smart Grid 9(2), 594–604 (2016)Google Scholar
  21. 21.
    Samuel, O., Alzahrani, F.A., Hussen Khan, R.J.U., Farooq, H., Shafiq, M., Afzal, M.K., Javaid, N.: Towards modified entropy mutual information feature selection to forecast medium-term load using a deep learning model in smart homes. Entropy 22(1), 2020 (2020)CrossRefGoogle Scholar
  22. 22.
    Khalid, R., Javaid, N., Al-zahrani, F.A., Aurangzeb, K., Qazi, E.U.H., Ashfaq, T.: Electricity load and price forecasting using Jaya-Long Short Term Memory (JLSTM) in smart grids. Entropy 22(1), 2020 (2020)Google Scholar
  23. 23.
    Mujeeb, S., Javaid, N.: ESAENARX and DE-RELM: novel schemes for big data predictive analytics of electricity load and price. Sustain. Cities Soc. 51, 101642 (2019)CrossRefGoogle Scholar
  24. 24.
    Mujeeb, S., Alghamdi, T.A., Ullah, S., Fatima, A., Javaid, N., Saba, T.: Exploiting deep learning for wind power forecasting based on big data analytics. Appl. Sci. 9(20), 4417 (2019)CrossRefGoogle Scholar
  25. 25.
    Naz, A., Javaid, N., Rasheed, M.B., Haseeb, A., Alhussein, M., Aurangzeb, K.: Game theoretical energy management with storage capacity optimization and photo-voltaic cell generated power forecasting in micro grid. Sustainability 11(10), 2763 (2019)CrossRefGoogle Scholar
  26. 26.
    Naz, A., Javed, M.U., Javaid, N., Saba, T., Alhussein, M., Aurangzeb, K.: Short-term electric load and price forecasting using enhanced extreme learning machine optimization in smart grids. Energies 12(5), 866 (2019)CrossRefGoogle Scholar
  27. 27.
    Mujeeb, S., Javaid, N., Ilahi, M., Wadud, Z., Ishmanov, F., Afzal, M.K.: Deep long short-term memory: a new price and load forecasting scheme for big data in smart cities. Sustainability 11(4), 987 (2019)CrossRefGoogle Scholar
  28. 28.
    Guo, Z., Zhou, K., Zhang, X., Yang, S.: A deep learning model for short-term power load and probability density forecasting. Energy 160, 1186–1200 (2018) CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.Computer Information ScienceHigher Colleges of TechnologyFujairahUnited Arab Emirates

Personalised recommendations