A Flexible Deep Learning Method for Energy Forecasting

Ihab Taleb, Guillaume Guerard, Frédéric Fauberteau and Nga Nguyen
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Ihab Taleb: Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France
Guillaume Guerard: Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France
Frédéric Fauberteau: Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France
Nga Nguyen: Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France

Energies, 2022, vol. 15, issue 11, 1-16

Abstract: Load prediction with higher accuracy and less computing power has become an important problem in the smart grids domain in general and especially in demand-side management (DSM), as it can serve to minimize global warming and better integrate renewable energies. To this end, it is interesting to have a general prediction model which uses different standard machine learning models in order to be flexible enough to be used in different regions and/or countries and to give a prediction for multiple days or weeks with relatively good accuracy. Thus, we propose in this article a flexible hybrid machine learning model that can be used to make predictions of different ranges by using both standard neural networks and an automatic process of updating the weights of these models depending on their past errors. The model was tested on Mayotte Island and the mean absolute percentage error (MAPE) obtained was 1.71% for 30 min predictions, 3.5% for 24 h predictions, and 5.1% for one-week predictions.

Keywords: flexible load forecasting; time series; hybrid model; deep learning; machine learning; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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