Electric Load Forecasting Based on Sparse Representation Model

  • Fangwan Huang
  • Xiangping Zheng
  • Zhiyong YuEmail author
  • Guanyi Yang
  • Wenzhong Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)


Accurate electric load forecasting can prevent the waste of power resources and plays a crucial role in smart grid. The time series of electric load collected by smart meters are non-linear and non-stationary, which poses a great challenge to the traditional forecasting methods. In this paper, sparse representation model (SRM) is proposed as a novel approach to tackle this challenge. The main idea of SRM is to obtain sparse representation coefficients by the training set and the part of over-complete dictionary, and the rest part of over-complete dictionary multiplied with sparse representation coefficients can be used to predict the future load value. Experimental results demonstrate that SRM is capable of forecasting the complex electric load time series effectively. It outperforms some popular machine learning methods such as Neural Network, SVM, and Random Forest.


Electric load forecasting Smart grid Sparse representation 



This work is supported by the National Key R&D Program of China under Grant No. 2017YFB1002000; the National Natural Science Foundation of China under Grant No. 61772136, 61672159, 61772005; the Technology Innovation Platform Project of Fujian Province under Grant No. 2014H2005; the Research Project for Young and Middle-aged Teachers of Fujian Province under Grant No. JT180045; the Fujian Collaborative Innovation Center for Big Data Application in Governments; the Fujian Engineering Research Center of Big Data Analysis and Processing.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fangwan Huang
    • 1
    • 2
  • Xiangping Zheng
    • 1
    • 2
  • Zhiyong Yu
    • 1
    • 2
    • 3
    Email author
  • Guanyi Yang
    • 1
    • 2
  • Wenzhong Guo
    • 1
    • 2
    • 3
  1. 1.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina
  2. 2.Fujian Provincial Key Laboratory of Network Computing and Intelligent Information ProcessingFuzhou UniversityFuzhouChina
  3. 3.Key Laboratory of Spatial Data Mining and Information SharingMinistry of EducationFuzhouChina

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