An Extreme Gradient Boosting Algorithm for Short-Term Load Forecasting Using Power Grid Big Data

  • Liqiang Ren
  • Limin ZhangEmail author
  • Haipeng Wang
  • Qiang Guo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)


Directed at the problem of more and increasing data types and volume in power grid, a short-term power load forecasting algorithm based on big data and Extreme Gradient Boosting (XGBoost) is proposed, based on the analysis of power grid load big data low. The algorithm includes the following steps. First, the outlier data and missing data are preprocessed. Then, the K-means algorithm is used to cluster the load big data of the power grid. Finally, The XGBoost algorithm was used to train the load forecasting model, based on the impact of historical load, calendar effect and meteorological factors on the load. Simulation results show that compared with support vector machine, random forest and decision tree, the proposed algorithm has a higher prediction accuracy and smoother prediction error, with smaller mean absolute percentage error, mean absolute error and relative error.


Power grid big data Power load forecasting Extreme gradient boosting algorithm Cluster analysis 



This research was supported by the National Natural Science Foundation of China (Grant No. 91538201); Taishan scholar project special fund project (Grant No. Ts201511020).

Author Contributions

Limin Zhang, Qiang Guo design experiments and collect data. Liqiang Ren and Haipeng Wang conducted a case study and analyzed the results. Liqiang Ren wrote this paper. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.


  1. 1.
    S. Li, L. Goel, P. Wang, An ensemble approach for short-term load forecasting by extreme learning machine. Appl. Energy 170, 22–29 (2016)CrossRefGoogle Scholar
  2. 2.
    H.S. Hippert, C.E. Pedreira, R.C. Souza, Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 16(1), 44–55 (2001)CrossRefGoogle Scholar
  3. 3.
    F. Javed, N. Arshad, F. Wallin et al., Forecasting for demand response in smart grids: an analysis on use of anthropologic and structural data and short term multiple loads forecasting. Appl. Energy 96(8), 150–160 (2012)CrossRefGoogle Scholar
  4. 4.
    M.Y. Cho, J.C. Hwang, C.S. Chen, Customer short term load forecasting by using ARIMA transfer function model, in International Conference on Energy Management and Power Delivery, 1995. Proceedings of EMPD (IEEE, 1995), pp. 317–322Google Scholar
  5. 5.
    G. Dudek, Pattern-based local linear regression models for short-term load forecasting. Electr. Power Syst. Res. 130, 139–147 (2016)CrossRefGoogle Scholar
  6. 6.
    S. Li, P. Wang, L. Goel, Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electr. Power Syst. Res. 122, 96–103 (2015)CrossRefGoogle Scholar
  7. 7.
    W. Baoyi, Z. Shuo, Z. Shaomin, Distributed power load forecasting algorithm based on cloud computing and extreme learning machines. Power Syst. Technol. 38(2), 526–531 (2014)Google Scholar
  8. 8.
    W.M. Lin, C.S. Tu, R.F. Yang et al., Particle swarm optimisation aided least-square support vector machine for load forecast with spikes. IET Gener. Transm. Distrib. 10(5), 1145–1153 (2016)CrossRefGoogle Scholar
  9. 9.
    L. Yuhua, C. Hong, G. Kun et al., Research on load forecasting based on random forest algorithm. CEA 52(23), 236–243 (2016)Google Scholar
  10. 10.
    T. Chen, C. Guestrin, XGBoost: a scalable tree boosting system, in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016, pp. 785–794Google Scholar
  11. 11.
    I. Babajide Mustapha, F. Saeed, Bioactive molecule prediction using extreme gradient boosting. Molecules 21(8), 983 (2016)CrossRefGoogle Scholar
  12. 12.
    Y. Xia, C. Liu, N. Liu, Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending. Electron. Commer. Res. Appl. 24, 30–49 (2017)CrossRefGoogle Scholar
  13. 13.
    M. Liu, The Power System Load Forecasting Research Based on Wavelet and Neural Network Theory (Nanjing University of Science and Technology, Nanjing, 2012)Google Scholar
  14. 14.
    R.J. Broderick, J.R. Williams, Clustering methodology for classifying distribution feeders, in Photovoltaic Specialists Conference (IEEE, 2013), pp. 1706–1710Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Liqiang Ren
    • 1
  • Limin Zhang
    • 1
    Email author
  • Haipeng Wang
    • 1
  • Qiang Guo
    • 1
  1. 1.Institute of Information FusionNaval Aviation UniversityYantaiChina

Personalised recommendations