This paper investigates the use of deep learning techniques in order to perform energy\ndemand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional\nneural network (CNN) coupled with an artificial neural network (ANN), with the main objective of\ntaking advantage of the virtues of both structures: the regression capabilities of the artificial neural\nnetwork and the feature extraction capacities of the convolutional neural network. The proposed\nstructure was trained and then used in a real setting to provide a French energy demand forecast using\nAction de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results\nshow that this approach outperforms the reference Réseau de Transport dâ??Electricité (RTE, French\ntransmission system operator) subscription-based service. Additionally, the proposed solution obtains\nthe highest performance score when compared with other alternatives, including Autoregressive\nIntegrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility\nof achieving high-accuracy forecasting using widely accessible deep learning techniques through\nopen-source machine learning platforms.
Loading....