From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914905434619904 |
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| author | Wang, Xiaoting Li, Yuzhuo Li, Yunwei Kish, Gregory |
| author_facet | Wang, Xiaoting Li, Yuzhuo Li, Yunwei Kish, Gregory |
| contents | This work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent breakthrough in artificial intelligence, for smart grid optimizations. To fully leverage KAN's interpretability, a general framework is proposed considering complex uncertainties. The stochastic optimal power flow problem in hybrid AC/DC systems is chosen as a particularly tough case study for demonstrating the effectiveness of this framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04063 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks Wang, Xiaoting Li, Yuzhuo Li, Yunwei Kish, Gregory Systems and Control This work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent breakthrough in artificial intelligence, for smart grid optimizations. To fully leverage KAN's interpretability, a general framework is proposed considering complex uncertainties. The stochastic optimal power flow problem in hybrid AC/DC systems is chosen as a particularly tough case study for demonstrating the effectiveness of this framework. |
| title | From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2408.04063 |