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Electrical Load Demand Forecasting Using Feed-Forward Neural Networks

Author

Listed:
  • Eduardo Machado

    (Instituto Superior Técnico-IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    Department of Materials, Energy Efficiency and Complementary Generation, Electrical Energy Research Center (Cepel), University City, Fundão Island, Rio de Janeiro 21941-911, Brazil)

  • Tiago Pinto

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Vanessa Guedes

    (Department of Materials, Energy Efficiency and Complementary Generation, Electrical Energy Research Center (Cepel), University City, Fundão Island, Rio de Janeiro 21941-911, Brazil)

  • Hugo Morais

    (Instituto Superior Técnico-IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    INESC-ID, Department of Electrical and Computer Engineering, Instituto Superior Técnico-IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal)

Abstract

The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that involves the prediction of the initial forecast errors by another FFNN. The results showed that the proposed methodology was able to significantly improve the quality of load demand forecasts, demonstrating a better performance than the benchmark models.

Suggested Citation

  • Eduardo Machado & Tiago Pinto & Vanessa Guedes & Hugo Morais, 2021. "Electrical Load Demand Forecasting Using Feed-Forward Neural Networks," Energies, MDPI, vol. 14(22), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7644-:d:679891
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yan Hong & Ding Wang & Jingming Su & Maowei Ren & Wanqiu Xu & Yuhao Wei & Zhen Yang, 2023. "Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model," Sustainability, MDPI, vol. 15(14), pages 1-28, July.
    2. Hugo Gaspar Hernandez-Palma & Jonny Rafael Plaza Alvarado & Jesús Enrique García Guiliany & Guilherme Luiz Dotto & Claudete Gindri Ramos, 2024. "Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 1-10, March.
    3. Roberto Baviera & Pietro Manzoni, 2022. "Tree-Based Learning in RNNs for Power Consumption Forecasting," Papers 2209.01378, arXiv.org.
    4. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    5. Herbert Amezquita & Pedro M. S. Carvalho & Hugo Morais, 2023. "Wind Forecast at Medium Voltage Distribution Networks," Energies, MDPI, vol. 16(6), pages 1-23, March.
    6. Jingming Su & Xuguang Han & Yan Hong, 2023. "Short Term Power Load Forecasting Based on PSVMD-CGA Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    7. Ali Saleh Aziz & Mohammad Faridun Naim Tajuddin & Tekai Eddine Khalil Zidane & Chun-Lien Su & Abdullahi Abubakar Mas’ud & Mohammed J. Alwazzan & Ali Jawad Kadhim Alrubaie, 2022. "Design and Optimization of a Grid-Connected Solar Energy System: Study in Iraq," Sustainability, MDPI, vol. 14(13), pages 1-29, July.
    8. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.

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