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Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

Author

Listed:
  • Miguel López Santos

    (Galicia Institute of Technology (ITG), 15003 A Coruña, Spain)

  • Xela García-Santiago

    (Galicia Institute of Technology (ITG), 15003 A Coruña, Spain)

  • Fernando Echevarría Camarero

    (Galicia Institute of Technology (ITG), 15003 A Coruña, Spain)

  • Gonzalo Blázquez Gil

    (Galicia Institute of Technology (ITG), 15003 A Coruña, Spain)

  • Pablo Carrasco Ortega

    (Galicia Institute of Technology (ITG), 15003 A Coruña, Spain)

Abstract

The energy generated by a solar photovoltaic (PV) system depends on uncontrollable factors, including weather conditions and solar irradiation, which leads to uncertainty in the power output. Forecast PV power generation is vital to improve grid stability and balance the energy supply and demand. This study aims to predict hourly day-ahead PV power generation by applying Temporal Fusion Transformer (TFT), a new attention-based architecture that incorporates an interpretable explanation of temporal dynamics and high-performance forecasting over multiple horizons. The proposed forecasting model has been trained and tested using data from six different facilities located in Germany and Australia. The results have been compared with other algorithms like Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), using statistical error indicators. The use of TFT has been shown to be more accurate than the rest of the algorithms to forecast PV generation in the aforementioned facilities.

Suggested Citation

  • Miguel López Santos & Xela García-Santiago & Fernando Echevarría Camarero & Gonzalo Blázquez Gil & Pablo Carrasco Ortega, 2022. "Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting," Energies, MDPI, vol. 15(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5232-:d:866410
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    References listed on IDEAS

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    Cited by:

    1. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Wu, Binrong & Yu, Sihao & Peng, Lu & Wang, Lin, 2024. "Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition," Energy, Elsevier, vol. 294(C).
    3. Jimyung Kang & Jooseung Lee & Soonwoo Lee, 2023. "Data-Driven Minute-Ahead Forecast of PV Generation with Adjacent PV Sector Information," Energies, MDPI, vol. 16(13), pages 1-16, June.

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