Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Energy Demand Forecasting Using Deep Learning: Application to the French Grid

Version 1 : Received: 8 March 2020 / Approved: 10 March 2020 / Online: 10 March 2020 (03:40:31 CET)

A peer-reviewed article of this Preprint also exists.

del Real, A.J.; Dorado, F.; Durán, J. Energy Demand Forecasting Using Deep Learning: Applications for the French Grid. Energies 2020, 13, 2242. del Real, A.J.; Dorado, F.; Durán, J. Energy Demand Forecasting Using Deep Learning: Applications for the French Grid. Energies 2020, 13, 2242.

Journal reference: Energies 2020, 13, 2242
DOI: 10.3390/en13092242

Abstract

This paper investigates the use of deep learning techniques to perform energy demand forecasting. Specifically, the authors have adapted a deep neural network originally thought for image classification and composed of a convolutional neural network (CNN) followed by a multilayered fully connected artificial neural network (ANN). The convolutional part of the network was fed with a grid of temperature forecasting data distributed in the area of interest in order to extract a featured temperature. The subsequent ANN is then fed with this calculated temperature along with other data related to the timing of the forecast. The proposed structure was first trained and then used in a real setting aimed to provide the French energy demand forecast using ARPEGE forecasting weather data. The results show that the performance of this approach is in the line of the performance provided by the reference RTE subscription-based service, which opens the possibility to obtain high accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.

Subject Areas

energy; demand; forecasting; deep; learning; machine; convolutional; artificial; neural; networks

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