Article
Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
Author/s | Real Torres, Alejandro del
Dorado, Fernando Durán, Jaime |
Department | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Publication Date | 2020-05 |
Deposit Date | 2020-07-15 |
Published in |
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Abstract | This paper investigates the use of deep learning techniques in order to perform energy
demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional
neural network (CNN) coupled ... This paper investigates the use of deep learning techniques in order to perform energy demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional neural network (CNN) coupled with an artificial neural network (ANN), with the main objective of taking advantage of the virtues of both structures: the regression capabilities of the artificial neural network and the feature extraction capacities of the convolutional neural network. The proposed structure was trained and then used in a real setting to provide a French energy demand forecast using Action de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results show that this approach outperforms the reference Réseau de Transport d’Electricité (RTE, French transmission system operator) subscription-based service. Additionally, the proposed solution obtains the highest performance score when compared with other alternatives, including Autoregressive Integrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility of achieving high-accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms. |
Citation | Real Torres, A.d., Dorado, F. y Durán, J. (2020). Energy Demand Forecasting Using Deep Learning: Applications for the French Grid. Energies, 13 (9), Article number 2242. |
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