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

Author

Listed:
  • Alejandro J. del Real

    (Department of Systems and Automation, University of Seville, 41004 Seville, Spain)

  • Fernando Dorado

    (IDENER, 41300 Seville, Spain)

  • Jaime Durán

    (IDENER, 41300 Seville, Spain)

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.

Suggested Citation

  • Alejandro J. del Real & Fernando Dorado & Jaime Durán, 2020. "Energy Demand Forecasting Using Deep Learning: Applications for the French Grid," Energies, MDPI, vol. 13(9), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2242-:d:353651
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    References listed on IDEAS

    as
    1. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
    2. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
    3. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
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    Citations

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    Cited by:

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    2. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    3. Anna Manowska & Anna Bluszcz, 2022. "Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network," Energies, MDPI, vol. 15(13), pages 1-23, July.
    4. Maciej Slowik & Wieslaw Urban, 2022. "Machine Learning Short-Term Energy Consumption Forecasting for Microgrids in a Manufacturing Plant," Energies, MDPI, vol. 15(9), pages 1-16, May.
    5. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.
    6. José Ignacio García-Lajara & Miguel Ángel Reyes-Belmonte, 2022. "Liquefied Natural Gas and Hydrogen Regasification Terminal Design through Neural Network Estimated Demand for the Canary Islands," Energies, MDPI, vol. 15(22), pages 1-24, November.
    7. Gwiman Bak & Youngchul Bae, 2020. "Predicting the Amount of Electric Power Transaction Using Deep Learning Methods," Energies, MDPI, vol. 13(24), pages 1-30, December.
    8. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Mohmmed S. Adrees, 2021. "An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy," Sustainability, MDPI, vol. 13(11), pages 1-20, May.

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