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 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.
Highlights
The forecasting of demand plays an essential role in the electric power industry
The deep learning architecture used in this paper resembles those structures widely used in image classification: a convolutional neural network followed by an artificial neural network
The results show that the performance of the proposed structure competes with the results provided by the Réseau de Transport d’Electricité (RTE) subscription-based reference service
Summary
The forecasting of demand plays an essential role in the electric power industry. there are a wide variety of methods for electricity demand prediction ranging from those of the short term (minutes) to long term (weeks), while considering microscopic (individual consumer) to macroscopic (country-level) aggregation levels. The authors of this paper found a promising topic related to the application of modern deep learning structures to the problem of power demand forecasting. This paper describes the novel use of a particular deep neural network structure composed of a convolutional neural network (widely used in image classification) followed by an artificial neural network for the forecasting of power demand with a limited number of information sources available. Based on the conclusions and outcomes achieved in previous literature, the authors here conceptualize their solution which, as described is an effective approach to dealing with the power demand time series forecasting problem with multiples input variables, complex nonlinear relationships, and missing data. The proposed model performs better than existing approaches, as described in the Results section
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