ATA: A transformer network for solar power prediction
Year: 2025
Type: conference-paper
Abstract: With a global focus on sustainable energy, solar power is vital. However, its intermittency challenges the power grid’s stability. Conventional deterministic models fail to handle such variability. Thus, this study proposes a novel solar-power prediction method with uncertainty estimation, composed of a hybrid Transformer-LSTM architecture optimized by nature-inspired algorithms. Adaptive bandwidth kernel density estimation (ABKDE) is leveraged to capture prediction uncertainties. Experiments on real-world data show that the method improves R2 by 20% and reduces RMSE by 25% versus conventional models. It not only provides accurate point predictions but also reliable prediction intervals (95% PICP at 0.95), delineating the stochasticity of solar power well and enhancing energy-system resilience.
Authors: Yani Wang, Rongsong Li, Fuqiang Wang, Xiaobei Li
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413154205