An Innovative Model Based on FCRBM for Load Forecasting in the Smart Grid

  • Ghulam Hafeez
  • Nadeem JavaidEmail author
  • Muhammad Riaz
  • Khalid Umar
  • Zafar Iqbal
  • Ammar Ali
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 994)


In this paper, an efficient model based on factored conditional restricted boltzmann machine (FCRBM) is proposed for electric load forecasting of in smart grid (SG). This FCRBM has deep layers structure and uses rectified linear unit (RELU) function and multivariate autoregressive algorithm for training. The proposed model predicts day ahead and week ahead electric load for decision making of the SG. The proposed model is a hybrid model having four modules i.e., data processing and features selection module, FCRBM based forecaster module, GWDO (genetic wind driven optimization) algorithm-based optimizer module, and utilization module. The proposed model is examined using FE grid data of USA. The proposed model provides more accurate results with affordable execution time than other load forecasting models, i.e., mutual information, modified enhanced differential evolution algorithm, and artificial neural network (ANN) based model (MI-mEDE-ANN), accurate fast converging short term load forecasting model (AFC-STLF), Bi-level model, and features selection and ANN-based model (FS-ANN).


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    Available online: Accessed 8 March 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ghulam Hafeez
    • 1
    • 2
  • Nadeem Javaid
    • 1
    Email author
  • Muhammad Riaz
    • 2
  • Khalid Umar
    • 3
  • Zafar Iqbal
    • 4
  • Ammar Ali
    • 1
  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.University of Engineering and TechnologyMardanPakistan
  3. 3.Bahria University IslambadIslamabdPakistan
  4. 4.PMAS Agriculture UniversityRawalpindiPakistan

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