Dual-Agent Transformer Network Based on Prompt Engineering for Voltage Prediction - Wanting Xu; Amirrudin Kamsin; Ling Jiang; Yuan Mei; Xinyue Zhou; Jun Huang (IEEE Access, 2025)
Voltage prediction plays a crucial role in ensuring power quality and enhancing the efficiency of power grid operations. Driven by the need for accurate and reliable voltage prediction methods, this paper introduces a novel dual-agent transformer network based on prompt engineering. By integrating prompt embeddings derived from historical voltage trends into the input embeddings, the proposed method effectively captures temporal correlations within voltage data. Utilizing a dual-agent attention mechanism, which reduces computational complexity by 42% compared to standard transformers, while the prompt embeddings enable precise capture of temporal patterns. Extensive simulations on real-world datasets demonstrate that our approach outperforms existing state-of-the-art models, achieving a 0.5% improvement in prediction accuracy. This advancement provides a robust solution for power system scheduling and operation, underscoring the importance and practical value of our work in advancing voltage prediction techniques.