Energy Demand Forecasting Using Deep Learning: Applications for the French Grid

Autor: Jaime Durán, Alejandro J. del Real, Fernando Dorado
Přispěvatelé: Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática
Rok vydání: 2020
Předmět:
Service (systems architecture)
Control and Optimization
Computer science
020209 energy
Computer Science::Neural and Evolutionary Computation
Feature extraction
Energy Engineering and Power Technology
02 engineering and technology
Machine learning
computer.software_genre
lcsh:Technology
Convolutional neural network
convolutional neural networks
0202 electrical engineering
electronic engineering
information engineering
Autoregressive integrated moving average
Electrical and Electronic Engineering
Engineering (miscellaneous)
Structure (mathematical logic)
energy demand forecasting
Artificial neural networks
Artificial neural network
lcsh:T
Renewable Energy
Sustainability and the Environment
business.industry
Deep learning
020208 electrical & electronic engineering
deep learning
Grid
machine learning
artificial neural networks
Energy demand forecasting
Convolutional neural networks
Artificial intelligence
business
computer
Energy (miscellaneous)
Zdroj: idUS. Depósito de Investigación de la Universidad de Sevilla
instname
Energies; Volume 13; Issue 9; Pages: 2242
idUS: Depósito de Investigación de la Universidad de Sevilla
Universidad de Sevilla (US)
Energies, Vol 13, Iss 2242, p 2242 (2020)
ISSN: 1996-1073
DOI: 10.3390/en13092242
Popis: 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.
Databáze: OpenAIRE
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