Download PDFOpen PDF in browserForecasting of PV Plant Output Using Interpretable Temporal Fusion Transformer ModelEasyChair Preprint 108886 pages•Date: September 12, 2023AbstractThe stochastic nature of solar energy generation poses a challenge for grid operators, especially with higher penetration of solar-based renewables in the grid. This paper proposes an attention-based temporal fusion transformer (TFT) model for short-term (an hour ahead) photovoltaic (PV) power forecasting using available geographic data such as solar irradiation, temperature, and statistical features extracted from historical PV data. TFT utilizes a self-attention layer for long-term dependencies where recurrent networks are used for local processing. The model selects relevant features through a series of gating layers to achieve high performance for multi-horizon forecasting. The temporal fusion transformer model also provides interpretable insights into the temporal dynamics of different features. A real-world PV dataset has been utilized to compare the model performance with some other state-of-the-art forecasting models. Keyphrases: Interpretable Machine Learning, Multi-horizon forecasting, PV forecasting, Temporal Fusion Transformer
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