Deep probabilistic solar power forecasting with Transformer and Gaussian process approximation

Solar power generation encounters instability and unpredictability issues due to the uncertainty of weather changes. Consequently, probabilistic forecasting of solar power is essential for the effective management and integration of solar energy into the power grid, substantially enhancing the reliability and efficiency of the electrical system. Among various methods, time series analysis for probabilistic forecasting, which leverages historical data to predict future solar power generation, has become a significant area of research due to advancements in deep learning. However, existing methods often fall short inaccuracy and operational efficiency. This paper introduces an innovative deep learning framework tailored for probabilistic forecasting of solar power generation. Considering the unique distribution characteristics of solar power data, a novel data preprocessing method integrating Box-Cox and Z-score transformations is applied to the input time series data. Subsequently, a novel probabilistic time series forecasting method, leveraging a Transformer network enhanced with Gaussian process approximation, predicts solar power generation for the forthcoming 24 h. The delta method is then employed to reverse transform the forecasts into actual predicted values. Comparative analyses using a real-world solar power dataset demonstrate that the proposed model outperforms existing probabilistic forecasting networks indeterministic, probabilistic, and interval forecasting tasks. Compared to the commonly used probabilistic forecasting method MC Dropout, our method decreases the CRPS index by 22.6% on the Shenzhen dataset and 39.7% on the Xingtai dataset. Furthermore, the proposed model exhibits superior computational efficiency, reflecting an optimal balance between accuracy and computational demands.