Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914220934692864 |
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| author | Zhang, Hanwen Zhang, Ruichen Zhang, Wei Niyato, Dusit Wen, Yonggang Miao, Chunyan |
| author_facet | Zhang, Hanwen Zhang, Ruichen Zhang, Wei Niyato, Dusit Wen, Yonggang Miao, Chunyan |
| contents | The energy optimization and demand side management (DSM) of Internet of Things (IoT)-enabled microgrids are being transformed by generative artificial intelligence, such as large language models (LLMs). This paper explores the integration of LLMs into energy management, and emphasizes their roles in automating the optimization of DSM strategies with Internet of Electric Vehicles (IoEV) as a representative example of the Internet of Vehicles (IoV). We investigate challenges and solutions associated with DSM and explore the new opportunities presented by leveraging LLMs. Then, we propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. The results demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles, and highlight our solution's significant advancements in energy efficiency and user adaptability. This work shows LLMs' potential in energy optimization of the IoT-enabled microgrids and promotes intelligent DSM solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15544 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles Zhang, Hanwen Zhang, Ruichen Zhang, Wei Niyato, Dusit Wen, Yonggang Miao, Chunyan Machine Learning Artificial Intelligence The energy optimization and demand side management (DSM) of Internet of Things (IoT)-enabled microgrids are being transformed by generative artificial intelligence, such as large language models (LLMs). This paper explores the integration of LLMs into energy management, and emphasizes their roles in automating the optimization of DSM strategies with Internet of Electric Vehicles (IoEV) as a representative example of the Internet of Vehicles (IoV). We investigate challenges and solutions associated with DSM and explore the new opportunities presented by leveraging LLMs. Then, we propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. The results demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles, and highlight our solution's significant advancements in energy efficiency and user adaptability. This work shows LLMs' potential in energy optimization of the IoT-enabled microgrids and promotes intelligent DSM solutions. |
| title | Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.15544 |