Electricity Load and Price Forecasting Using Machine Learning Algorithms in Smart Grid: A Survey

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


Conventional grid moves towards Smart Grid (SG). In conventional grids, electricity is wasted in generation-transmissions-distribution, and communication is in one direction only. SG is introduced to solve prior issues. In SG, there are no restrictions, and communication is bi-directional. Electricity forecasting plays a significant role in SG to enhance operational cost and efficient management. Load and price forecasting gives future trends. In literature many data-driven methods have been discussed for price and load forecasting. The objective of this paper is to focus on literature related to price and load forecasting in last four years. The author classifies each paper in terms of its problems and solutions. Additionally, the comparison of each proposed technique regarding performance are presented in this paper. Lastly, papers limitations and future challenges are discussed.


  1. 1.
    Hossain, E., Khan, I., Un-Noor, F., Sikander, S.S., Sunny, M.S.H.: Application of big data and machine learning in smart grid, and associated security concerns: a review. IEEE Access 7, 1396013988 (2019)Google Scholar
  2. 2.
    Kuster, C., Rezgui, Y., Mourshed, M.: Electrical load forecasting models: a critical systematic review. Sustain. Cities Soc. 35, 257–270 (2017)CrossRefGoogle Scholar
  3. 3.
    Tong, C., Li, J., Lang, C., Kong, F., Niu, J., Rodrigues, J.J.: An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders. J. Parallel Distrib. Comput. 117, 267–273 (2018)CrossRefGoogle Scholar
  4. 4.
    Lago, J., De Ridder, F., Vrancx, P., De Schutter, B.: Forecasting day-ahead electricity prices in Europe: the importance of considering market integration. Appl. Energy 211, 890–903 (2018)CrossRefGoogle Scholar
  5. 5.
    Mujeeb, S., Javaid, N., Javaid, S.: Data analytics for price forecasting in smart grids: a survey. In: 2018 IEEE 21st International Multi-Topic Conference (INMIC), pp. 1–10. IEEE, November 2018.
  6. 6.
    Miraftabzadeh, S.M., Foiadelli, F., Longo, M., Pasetti, M.: A survey of machine learning applications for power system analytics. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I and CPS Europe), pp. 1–5. IEEE, June 2019Google Scholar
  7. 7.
    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, January 2018Google Scholar
  8. 8.
    Zheng, J., Xu, C., Zhang, Z., Li, X.: Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In: 2017 51st Annual Conference on Information Sciences and Systems (CISS), pp. 1–6. IEEE, March 2017Google Scholar
  9. 9.
    Qiu, X., Ren, Y., Suganthan, P.N., Amaratunga, G.A.: Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl. Soft Comput. 54, 246–255 (2017)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Qiu, X., Suganthan, P.N., Amaratunga, G.A.: Ensemble incremental learning random vector functional link network for short-term electric load forecasting. Knowl.-Based Syst. 145, 182–196 (2018)CrossRefGoogle Scholar
  12. 12.
    Fan, G.F., Guo, Y.H., Zheng, J.M., Hong, W.C.: Application of the weighted K-nearest neighbor algorithm for short-term load forecasting. Energies 12(5), 916 (2019)CrossRefGoogle Scholar
  13. 13.
    Zhang, J., Wei, Y.M., Li, D., Tan, Z., Zhou, J.: Short term electricity load forecasting using a hybrid model. Energy 158, 774–781 (2018)CrossRefGoogle Scholar
  14. 14.
    Fan, G.F., Peng, L.L., Hong, W.C.: Short term load forecasting based on phase space reconstruction algorithm and bi-square Kernel regression model. Appl. Energy 224, 13–33 (2018)CrossRefGoogle Scholar
  15. 15.
    Fan, G.F., Peng, L.L., Zhao, X., Hong, W.C.: Applications of hybrid EMD with PSO and GA for an SVR-based load forecasting model. Energies 10(11), 1713 (2017)CrossRefGoogle Scholar
  16. 16.
    Yang, Z., Ce, L., Lian, L.: Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Appl. Energy 190, 291–305 (2017)CrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    Zhang, J.L., Zhang, Y.J., Li, D.Z., Tan, Z.F., Ji, J.F.: Forecasting day-ahead electricity prices using a new integrated model. Int. J. Electr. Power Energy Syst. 105, 541–548 (2019)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    Angamuthu Chinnathambi, R., Mukherjee, A., Campion, M., Salehfar, H., Hansen, T.M., Lin, J., Ranganathan, P.: A multi-stage price forecasting model for day-ahead electricity markets. Forecasting 1(1), 26–46 (2019)CrossRefGoogle Scholar
  23. 23.
    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), 68 (2020)CrossRefGoogle Scholar
  24. 24.
    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), 10 (2020)CrossRefGoogle Scholar
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.COMSATS University IslamabadIslamabadPakistan

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