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An Effective Short-Term Electrical Load Forecasting Model: A Constructive Neural Network Approach

Author

Listed:
  • Kazi Rafiqul Islam
  • Md. Monirul Kabir
  • Amit Shaha Surja
  • Md. Shahid Iqbal

Abstract

In this paper, an Effective Electrical Load Forecasting (EELF) model has been introduced based on Feed-Forward Neural Network (FFNN) which utilizes the constructive method during training. The key aspect of this model is to automate the FFNN architecture during training phase in order to forecast the electrical load. Thus, the robustness of standard FFNN increases while forecasting the electrical load. Moreover, this proposed model can efficiently overcome the existing limitations of FFNN to successfully predict the fast load changes and also the holiday loads. The model has been named as Constructive Approach for Effective Electrical Load Forecasting (CAEELF) on a short-term basis. In order to evaluate the performance of CAEELF, Spain's daily electrical load demand data have been used. Furthermore, extensive experimental results and comparisons have been shown to validate the acceptability of proposed CAEELF for electrical load prediction over other standard FFNN models.

Suggested Citation

  • Kazi Rafiqul Islam & Md. Monirul Kabir & Amit Shaha Surja & Md. Shahid Iqbal, 2022. "An Effective Short-Term Electrical Load Forecasting Model: A Constructive Neural Network Approach," European Journal of Engineering and Technology Research, European Open Science, vol. 7(4), pages 14-20, July.
    • Handle: RePEc:epw:ejeng0:v:7:y:2022:i:4:id:62855
    DOI: 10.24018/ejeng.2022.7.4.2855
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    References listed on IDEAS

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    1. Fan, Guo-Feng & Yu, Meng & Dong, Song-Qiao & Yeh, Yi-Hsuan & Hong, Wei-Chiang, 2021. "Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling," Utilities Policy, Elsevier, vol. 73(C).
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