Deep Learning Application: Load Forecasting in Big Data of Smart Grids

  • Abdulaziz AlmalaqEmail author
  • Jun Jason Zhang
Part of the Studies in Computational Intelligence book series (SCI, volume 865)


Load forecasting in smart grids is still exploratory; despite the increase of smart grids technologies and energy conservation research, many challenges remain for accurate load forecasting using big data or large-scale datasets. This chapter addresses the problem of how to improve the forecasting results of loads in smart grids, using deep learning methods that have shown significant progress in various disciplines in recent years. The deep learning methods have the potential ability to extract problem-relevant features and capture complex large-scale data distributions. Existing research in load forecasting tends to focus on finding predicted loads using small historical datasets and the behavior of the load’s consumers in smart grids. Moreover, current research which applies the conventional deep learning methods for load forecasting has shown better performance than conventional load forecasting methods. However, there is little evidence that researchers have addressed the issue of hybridizing different deep learning methods for complex large-scale load forecasting in smart grids, with the intent of building a robust predictive model in smart grids and understanding the relationships that exist between different predictive models and deep learning methods. Consequently, the purpose of this chapter is to provide an overview of how the load forecasting performances using deep learning methods in smart grids can be improved.


Energy consumption prediction Deep learning Load forecasting Smart grids 


  1. 1.
    Gungor, V.C., et al.: Smart grid technologies: communication technologies and standards. IEEE Trans. Ind. Inf. 7(4), 529–539 (2011)CrossRefGoogle Scholar
  2. 2.
    Deng, R., Yang, Z., Chow, M., Chen, J.: A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans. Ind. Inf. 11(3), 570–582 (2015)CrossRefGoogle Scholar
  3. 3.
    Almalaq, A., Edwards, G.: A review of deep learning methods applied on load forecasting. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 511–516 (2017)Google Scholar
  4. 4.
    Raza, M.Q., Khosravi, A.: A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sustain. Energy Rev. 50, 1352–1372 (2015)CrossRefGoogle Scholar
  5. 5.
    Khatoon, S., Ibraheem, Singh, A.K., Priti: Effects of various factors on electric load forecasting: an overview. In: 2014 6th IEEE Power India International Conference (PIICON), pp. 1–5 (2014)Google Scholar
  6. 6.
    Fahad, M.U., Arbab, N.: Factor affecting short term load forecasting. J. Clean Energy Technol. 2(4), 305–309 (2014)CrossRefGoogle Scholar
  7. 7.
    Feinberg, E.A., Genethliou, D.: Load Forecasting. In: Chow, J.H., Wu, F.F., Momoh, J. (eds.) Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence, pp. 269–285. Springer US, Boston, MA (2005)CrossRefGoogle Scholar
  8. 8.
    Ji, P., Xiong, D., Wang, P., Chen, J.: A study on exponential smoothing model for load forecasting. In: 2012 Asia-Pacific Power and Energy Engineering Conference, pp. 1–4 (2012)Google Scholar
  9. 9.
    Amjady, N.: Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans. Power Syst. 16(3), 498–505 (2001)CrossRefGoogle Scholar
  10. 10.
    Hagan, M.T., Behr, S.M.: The time series approach to short term load forecasting. IEEE Trans. Power Syst. 2(3), 785–791 (1987)CrossRefGoogle Scholar
  11. 11.
    Ding, Q.: Long-term load forecast using decision tree method. In: 2006 IEEE PES Power Systems Conference and Exposition, pp. 1541–1543 (2006)Google Scholar
  12. 12.
    Yu, Z., Haghighat, F., Fung, B.C.M., Yoshino, H.: A decision tree method for building energy demand modeling. Energy Build. 42(10), 1637–1646 (2010)CrossRefGoogle Scholar
  13. 13.
    Chen, B.-J., Chang, M.-W., et al.: Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Trans. Power Syst. 19(4), 1821–1830 (2004)Google Scholar
  14. 14.
    Pai, P.-F., Hong, W.-C.: Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers. Manag. 46(17), 2669–2688 (2005)CrossRefGoogle Scholar
  15. 15.
    Zhu, Z, Sun, Y., Li, H.: Hybrid of EMD and SVMs for short-term load forecasting. In: 2007. ICCA 2007. IEEE International Conference on Control and Automation, pp. 1044–1047 (2007)Google Scholar
  16. 16.
    Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E., Damborg, M.J.: Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 6(2), 442–449 (1991)CrossRefGoogle Scholar
  17. 17.
    Hayati, M., Shirvany, Y.: Artificial neural network approach for short term load forecasting for Illam region. World Acad. Sci. Eng. Technol. 28, 280–284 (2007)Google Scholar
  18. 18.
    Kandil, N., Wamkeue, R., Saad, M., Georges, S.: An efficient approach for short term load forecasting using artificial neural networks. Int. J. Electr. Power Energy Syst. 28(8), 525–530 (2006)CrossRefGoogle Scholar
  19. 19.
    Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)CrossRefGoogle Scholar
  20. 20.
    González, P.A., Zamarreño, J.M.: Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy Build. 37(6), 595–601 (2005)CrossRefGoogle Scholar
  21. 21.
    Tsakoumis, A.C., Vladov, S.S., Mladenov, V.M.: Electric load forecasting with multilayer perceptron and Elman neural network. In: 6th Seminar on Neural Network Applications in Electrical Engineering, pp. 87–90 (2002)Google Scholar
  22. 22.
    Dudek, G.: Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting. Int. J. Forecast. 32(3), 1057–1060 (2016)CrossRefGoogle Scholar
  23. 23.
    Kuo, P.-H., Huang, C.-J.: An electricity price forecasting model by hybrid structured deep neural networks. Sustainability 10(4), 1280 (2018)CrossRefGoogle Scholar
  24. 24.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)Google Scholar
  25. 25.
    Amarasinghe, K., Marino, D.L., Manic, M.: Deep neural networks for energy load forecasting. In: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), pp. 1483–1488 (2017)Google Scholar
  26. 26.
    Khan, S., Javaid, N., Chand, A., Khan, A.B.M., Rashid, F., Afridi, I.U.: Electricity load forecasting for each day of week using deep CNN. In: Kalbitzer, U., Jack, K.M. (eds.) Primate Life Histories, Sex Roles, and Adaptability, pp. 1107–1119. Springer International Publishing, Cham (2019)Google Scholar
  27. 27.
    Kollia, I., Kollias, S.: A deep learning approach for load demand forecasting of power systems. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, pp. 912–919 (2018)Google Scholar
  28. 28.
    Dong, X., Qian, L., Huang, L.: A CNN based bagging learning approach to short-term load forecasting in smart grid. In: 2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 1–6 (2017)Google Scholar
  29. 29.
    Shi, H., Xu, M., Li, R.: Deep learning for household load forecasting—a novel pooling deep RNN. IEEE Trans. Smart Grid 9(5), 5271–5280 (2018)CrossRefGoogle Scholar
  30. 30.
    Yu, Z., Niu, Z., Tang, W., Wu, Q.: Deep learning for daily peak load forecasting–a novel gated recurrent neural network combining dynamic time warping. IEEE Access 7, 17184–17194 (2019)CrossRefGoogle Scholar
  31. 31.
    Bedi, J., Toshniwal, D.: Deep learning framework to forecast electricity demand. Appl. Energy 238, 1312–1326 (2019)CrossRefGoogle Scholar
  32. 32.
    Kong, W., Dong, Z.Y., Hill, D.J., Luo, F., Xu, Y.: Short-Term residential load forecasting based on resident behaviour learning. IEEE Trans. Power Syst. 33(1), 1087–1088 (2018)CrossRefGoogle Scholar
  33. 33.
    Marino, D.L., Amarasinghe, K., Manic, M.: Building energy load forecasting using deep neural networks. In: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 7046–7051 (2016)Google Scholar
  34. 34.
    Gan, D., Wang, Y., Zhang, N., Zhu, W.: Enhancing short-term probabilistic residential load forecasting with quantile long–short-term memory. J. Eng. 2017(14), 2622–2627 (2017)Google Scholar
  35. 35.
    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 (2017)Google Scholar
  36. 36.
    Chung, J., Gülçehre, Ç., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: CoRR (2014).
  37. 37.
    Kumar, S., Hussain, L., Banarjee, S., Reza, M.: Energy load forecasting using deep learning approach-LSTM and GRU in spark cluster. In: 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), pp. 1–4 (2018)Google Scholar
  38. 38.
    Gao, X., Li, X., Zhao, B., Ji, W., Jing, X., He, Y.: Short-term electricity load forecasting model based on EMD-GRU with feature selection. Energies 12(6), 1140 (2019)CrossRefGoogle Scholar
  39. 39.
    Almalaq, A., Zhang, J.J.: Evolutionary deep learning-based energy consumption prediction for buildings. IEEE Access 7, 1520–1531 (2019)CrossRefGoogle Scholar
  40. 40.
    Bouktif, S., Fiaz, A., Ouni, A., Serhani, M.A.: Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies 11(7) (2018)Google Scholar
  41. 41.
    Long-Term Energy Consumption & Outdoor Air Temperature For 11 Commercial Buildings-Openei Datasets. (2019)Google Scholar
  42. 42.
    Chollet, F. et al.: Keras. GitHub (2015)Google Scholar
  43. 43.
    Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.The Department of Electrical EngineeringUniversity of HailHailSaudi Arabia
  2. 2.The Department of Power EngineeringWuhan UniversityWuhanChina

Personalised recommendations