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Short-term load forecasting using machine learning and periodicity decomposition

  • Received: 07 April 2019 Accepted: 10 June 2019 Published: 26 June 2019
  • The accuracy of electricity consumption forecasts is of paramount importance in energy planning, it provides strong support for the effective energy demand management. In this work, we proposed a load forecast through the decomposition of the historical time series in relation to the historical evolution of each hour of the day. The output of these decomposition were served as input to different algorithms of machine learning. We tested our model by five machines learning methods, the achieved results are examined with three of the most commonly used evaluation measures in forecasting. The obtained results were very satisfactory.

    Citation: Abdelkarim El khantach, Mohamed Hamlich, Nour eddine Belbounaguia. Short-term load forecasting using machine learning and periodicity decomposition[J]. AIMS Energy, 2019, 7(3): 382-394. doi: 10.3934/energy.2019.3.382

    Related Papers:

  • The accuracy of electricity consumption forecasts is of paramount importance in energy planning, it provides strong support for the effective energy demand management. In this work, we proposed a load forecast through the decomposition of the historical time series in relation to the historical evolution of each hour of the day. The output of these decomposition were served as input to different algorithms of machine learning. We tested our model by five machines learning methods, the achieved results are examined with three of the most commonly used evaluation measures in forecasting. The obtained results were very satisfactory.


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