Dual-Agent Transformer Network Based on Prompt Engineering for Voltage Prediction
Year: 2025
Type: article
Abstract: 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.
Source: IEEE Access
Authors: Wanting Xu, Amirrudin Kamsin, Ling Jiang, Yuan Mei, Xinyue Zhou, Jun Huang
Institutions: City University Malaysia, Wuhu Institute of Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4411358801