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Forecasting of PV Plant Output Using Interpretable Temporal Fusion Transformer Model

EasyChair Preprint no. 10888

6 pagesDate: September 12, 2023


The 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

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Md Maidul Islam and Salman Sadiq Shuvo and Md Jamal Ahmed Shohan and Md Omar Faruque},
  title = {Forecasting of PV Plant Output Using Interpretable Temporal Fusion Transformer Model},
  howpublished = {EasyChair Preprint no. 10888},

  year = {EasyChair, 2023}}
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