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Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models

Author

Listed:
  • Yunsun Kim

    (Chief Data Officer, Hyundai Motor Group, Seoul 06797, Korea)

  • Sahm Kim

    (Department of Applied Statistics, Chung-ang University, Seoul 06974, Korea)

Abstract

This study was conducted to investigate the applicability of measuring internet traffic as an input of short-term electricity demand forecasts. We believe our study makes a significant contribution to the literature, especially in short-term load prediction techniques, as we found that Internet traffic can be a useful variable in certain models and can increase prediction accuracy when compared to models in which it is not a variable. In addition, we found that the prediction error could be further reduced by applying a new multivariate model called VARX, which added exogenous variables to the univariate model called VAR. The VAR model showed excellent forecasting performance in the univariate model, rather than using the artificial neural network model, which had high prediction accuracy in the previous study.

Suggested Citation

  • Yunsun Kim & Sahm Kim, 2021. "Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models," Mathematics, MDPI, vol. 9(18), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2347-:d:640123
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    References listed on IDEAS

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    Cited by:

    1. Javier Sánchez García & Salvador Cruz Rambaud, 2022. "Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs," Mathematics, MDPI, vol. 10(6), pages 1-15, March.

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