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Short-term forecasting of Indonesia electricity generation using MATLAB based on NARX neural network

Author

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  • Nicholas Pranata
  • Fahmy Rinanda Saputri

Abstract

Electricity consumption, production, and supply based on fossil fuels have increased due to population growth, urbanization, and technological development, leading to environmental damage in countries like Indonesia. In response to this issue, electricity forecasting is essential. This study applies to a Nonlinear Autoregressive with Exogenous Input (NARX) neural network to forecast one year ahead of electricity generation using MATLAB. Two algorithms are used for comparison: Levenberg-Marquardt and Bayesian Regularization. The data is classified using a standard method of 70%−30% split, with 30 hidden layers and a standard delay of 2-time steps. The results show that both algorithms achieve an R² value above 0.9 and a Mean Absolute Percent Error (MAPE) of under 3%, with the Levenberg-Marquardt algorithm demonstrating marginally superior performance. These results indicate that the model provides valuable insights for forecasting annual electricity generation in Indonesia over a short timeframe.

Suggested Citation

  • Nicholas Pranata & Fahmy Rinanda Saputri, 2026. "Short-term forecasting of Indonesia electricity generation using MATLAB based on NARX neural network," PLOS ONE, Public Library of Science, vol. 21(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0340268
    DOI: 10.1371/journal.pone.0340268
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