Solar Irradiance Forecasting Using Temporal Fusion Transformers

Abstract

Global climate change has intensified the search for renewable energy sources. Solar power is a cost-effective option for electricity generation. Accurate energy forecasting is crucial for efficient planning. While various techniques have been introduced for energy forecasting, transformer-based models are effective for capturing long-range dependencies in data. This study proposes N hours-ahead solar irradiance forecasting framework based on variational mode decomposition (VMD) for handling meteorological data and a modified temporal fusion transformer (TFT) for forecasting solar irradiance. The proposed model decomposes raw solar irradiance sequences into intrinsic mode functions (IMFs) using VMD and optimizes the TFT using a variable screening network and a gated recurrent unit (GRU)-based encoder–decoder. Our study specifically targets the 1-h as well as different forecasting horizons for solar irradiance. The resulting deep learning model offers insights, including the prioritization of solar irradiance subsequences and an analysis of various forecasting window sizes. An empirical study shows that our proposed method has achieved high performance compared to other time series models, such as artificial neural network (ANN), long short-term memory (LSTM), CNN–LSTM, CNN–LSTM with temporal attention (CNN–LSTM-t), transformer, and the original TFT model.