A large language model for advanced power dispatch
Abstract Power dispatch is essential for providing society with stable, cost-effective, and eco-friendly electricity. However, traditional methods falter as power systems grow in scale and complexity, struggling with multitasking, swift problem-solving, and human-machine collaboration. This paper in...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-91940-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850039600431497216 |
|---|---|
| author | Yuheng Cheng Huan Zhao Xiyuan Zhou Junhua Zhao Yuji Cao Chao Yang Xinlei Cai |
| author_facet | Yuheng Cheng Huan Zhao Xiyuan Zhou Junhua Zhao Yuji Cao Chao Yang Xinlei Cai |
| author_sort | Yuheng Cheng |
| collection | DOAJ |
| description | Abstract Power dispatch is essential for providing society with stable, cost-effective, and eco-friendly electricity. However, traditional methods falter as power systems grow in scale and complexity, struggling with multitasking, swift problem-solving, and human-machine collaboration. This paper introduces Grid Artificial Intelligent Assistant (GAIA), a pioneering Large Language Model (LLM) designed to assist with a variety of power system operational tasks, including operation adjustment, operation monitoring, and black start scenarios. We have developed a novel dataset construction technique that harnesses various data sources to fine-tune GAIA for optimal performance in this domain. This approach streamlines LLM training, allowing for the seamless integration of multidimensional data in power system management. Additionally, we have crafted specialized prompt strategies to boost GAIA’s input-output efficiency in dispatch scenarios. When evaluated on the ElecBench benchmark, GAIA surpasses the baseline model Large Language Model Meta AI-2 (LLaMA2) on multiple metrics. In practical applications, GAIA has demonstrated its ability to enhance decision-making processes, improve operational efficiency, and facilitate better human-machine interactions in power dispatch operations. This paper expands the application of LLMs to power dispatch and validates their practical utility, paving the way for future innovations in this field. |
| format | Article |
| id | doaj-art-e85a01e034134dbd824be83c7e1ba277 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e85a01e034134dbd824be83c7e1ba2772025-08-20T02:56:16ZengNature PortfolioScientific Reports2045-23222025-03-0115111710.1038/s41598-025-91940-xA large language model for advanced power dispatchYuheng Cheng0Huan Zhao1Xiyuan Zhou2Junhua Zhao3Yuji Cao4Chao Yang5Xinlei Cai6Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)Department of Building Environment and Energy Engineering, Hong Kong Polytechnic UniversitySchool of Electrical and Electronic Engineering, Nanyang Technological UniversityShenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)Department of Mechanical and Automation Engineering, Chinese University of Hong KongSchool of Electrical and Electronic Engineering, North China Electric Power UniversityChina Southern Power Grid (China)Abstract Power dispatch is essential for providing society with stable, cost-effective, and eco-friendly electricity. However, traditional methods falter as power systems grow in scale and complexity, struggling with multitasking, swift problem-solving, and human-machine collaboration. This paper introduces Grid Artificial Intelligent Assistant (GAIA), a pioneering Large Language Model (LLM) designed to assist with a variety of power system operational tasks, including operation adjustment, operation monitoring, and black start scenarios. We have developed a novel dataset construction technique that harnesses various data sources to fine-tune GAIA for optimal performance in this domain. This approach streamlines LLM training, allowing for the seamless integration of multidimensional data in power system management. Additionally, we have crafted specialized prompt strategies to boost GAIA’s input-output efficiency in dispatch scenarios. When evaluated on the ElecBench benchmark, GAIA surpasses the baseline model Large Language Model Meta AI-2 (LLaMA2) on multiple metrics. In practical applications, GAIA has demonstrated its ability to enhance decision-making processes, improve operational efficiency, and facilitate better human-machine interactions in power dispatch operations. This paper expands the application of LLMs to power dispatch and validates their practical utility, paving the way for future innovations in this field.https://doi.org/10.1038/s41598-025-91940-x |
| spellingShingle | Yuheng Cheng Huan Zhao Xiyuan Zhou Junhua Zhao Yuji Cao Chao Yang Xinlei Cai A large language model for advanced power dispatch Scientific Reports |
| title | A large language model for advanced power dispatch |
| title_full | A large language model for advanced power dispatch |
| title_fullStr | A large language model for advanced power dispatch |
| title_full_unstemmed | A large language model for advanced power dispatch |
| title_short | A large language model for advanced power dispatch |
| title_sort | large language model for advanced power dispatch |
| url | https://doi.org/10.1038/s41598-025-91940-x |
| work_keys_str_mv | AT yuhengcheng alargelanguagemodelforadvancedpowerdispatch AT huanzhao alargelanguagemodelforadvancedpowerdispatch AT xiyuanzhou alargelanguagemodelforadvancedpowerdispatch AT junhuazhao alargelanguagemodelforadvancedpowerdispatch AT yujicao alargelanguagemodelforadvancedpowerdispatch AT chaoyang alargelanguagemodelforadvancedpowerdispatch AT xinleicai alargelanguagemodelforadvancedpowerdispatch AT yuhengcheng largelanguagemodelforadvancedpowerdispatch AT huanzhao largelanguagemodelforadvancedpowerdispatch AT xiyuanzhou largelanguagemodelforadvancedpowerdispatch AT junhuazhao largelanguagemodelforadvancedpowerdispatch AT yujicao largelanguagemodelforadvancedpowerdispatch AT chaoyang largelanguagemodelforadvancedpowerdispatch AT xinleicai largelanguagemodelforadvancedpowerdispatch |