Forecasting of PV Plant Output Using Interpretable Temporal Fusion Transformer Model

EasyChair Preprint 10888

Abstract

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

@booklet{EasyChair:10888,
  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 10888},
  year      = {EasyChair, 2023}}