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Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control

Author

Listed:
  • Fermín Rodríguez

    (Ceit-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain
    Universidad de Navarra, Tecnun, Manuel Lardizabal 13, Ceit, Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain)

  • Fernando Martín

    (Ceit-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain
    Universidad de Navarra, Tecnun, Manuel Lardizabal 13, Ceit, Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain)

  • Luis Fontán

    (Ceit-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain
    Universidad de Navarra, Tecnun, Manuel Lardizabal 13, Ceit, Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain)

  • Ainhoa Galarza

    (Ceit-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain
    Universidad de Navarra, Tecnun, Manuel Lardizabal 13, Ceit, Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain)

Abstract

Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg–Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed during the validation phase. Moreover, while the root mean square error was 0.07 MW, the mean absolute error was 0.05 MW. The results suggest that the forecaster’s accuracy is considered sufficient for installation in smart grids or other power systems and for predicting future energy demand at the chosen sites.

Suggested Citation

  • Fermín Rodríguez & Fernando Martín & Luis Fontán & Ainhoa Galarza, 2020. "Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control," Energies, MDPI, vol. 13(19), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5210-:d:424341
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    References listed on IDEAS

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    Cited by:

    1. M. C. Pegalajar & L. G. B. Ruiz, 2022. "Time Series Forecasting for Energy Consumption," Energies, MDPI, vol. 15(3), pages 1-3, January.
    2. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).

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