Short-term load forecasting using machine learning and periodicity decomposition

Abdelkarim El khantach; Mohamed Hamlich; Nour eddine Belbounaguia; Abdelkarim El khantach; Mohamed Hamlich; Nour eddine Belbounaguia

doi:10.3934/energy.2019.3.382

AIMS Energy

2019, Volume 7, Issue 3: 382-394. doi: 10.3934/energy.2019.3.382

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Research article Topical Sections

Short-term load forecasting using machine learning and periodicity decomposition

1.
Laboratory of Physics of Atmosphere, Modeling and Simulation LPAMS. FSTM, Mohammedia. BP 146 Mohammedia 20650 Morocco. Hassan II University Casablanca
2.
LSSIEE ENSAM Casablanca. 150 Avenue Nile Sidi Othman Casablanca 20670, Morocco. Hassan II University Casablanca

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.
- load forecasting,
- machine learning,
- periodicity decomposition,
- time series,
- smart grid
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

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Abstract

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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