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Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network

Author

Listed:
  • Sahed Abdelkader

    (Maghnia University Center)

  • Kahoui Hacene

    (maghnia University center, Algeria)

Abstract

Forecasting electricity consumption is necessary for electric grid operation and utility resource planning, as well as to improve energy security and grid resilience. Thus, this research aims to investigate the prediction performance of the ARIMA and LSTM neural network model using electricity consumption data during the period 1990 to 2020. The time series for electricity consumption is divided into 70% for training data and 30% for test data. The results showed that the LSTM model provided improved forecasting accuracy than the ARIMA model.

Suggested Citation

  • Sahed Abdelkader & Kahoui Hacene, 2023. "Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network," Post-Print hal-04183403, HAL.
    • Handle: RePEc:hal:journl:hal-04183403
    Note: View the original document on HAL open archive server: https://cnrs.hal.science/hal-04183403
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    More about this item

    Keywords

    Electricity Consumption ARIMA LSTM Algeria. JEL Classification Codes: Q47; C53; C45; Electricity Consumption; ARIMA; LSTM; Algeria. JEL Classification Codes: Q47;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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