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

Miguel López Santos, Xela García-Santiago, Fernando Echevarría Camarero, Gonzalo Blázquez Gil and Pablo Carrasco Ortega
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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

Energies, 2022, vol. 15, issue 14, 1-22

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.

Keywords: photovoltaic power forecast; solar energy; Temporal Fusion Transformer; deep learning; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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