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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.14820 |
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| _version_ | 1866910799184789504 |
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| author | Tseng, Yen-hsuan |
| author_facet | Tseng, Yen-hsuan |
| contents | This paper presents a comprehensive analysis of power plant performance using the inverse Gaussian (IG) distribution framework. We combine theoretical foundations with practical applications, focusing on both combined cycle and nuclear power plant contexts. The study demonstrates the advantages of the IG distribution in modeling right-skewed industrial data, particularly in power generation. Using the UCI Combined Cycle Power Plant Dataset, we establishthe superiority of IG-based models over traditional approaches through rigorous statistical testing and model validation. The methodology developed here extends naturally to nuclear power plant applications, where similar statistical patterns emerge in operational data. Our findings suggest that IG-based models provide more accurate predictions and better capture the underlying physical processes in power generation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14820 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inverse Gaussian Distribution, Introduction and Applications:Comprehensive Analysis of Power Plant Performance: A Study of Combined Cycle and Nuclear Power Plant Tseng, Yen-hsuan Applications This paper presents a comprehensive analysis of power plant performance using the inverse Gaussian (IG) distribution framework. We combine theoretical foundations with practical applications, focusing on both combined cycle and nuclear power plant contexts. The study demonstrates the advantages of the IG distribution in modeling right-skewed industrial data, particularly in power generation. Using the UCI Combined Cycle Power Plant Dataset, we establishthe superiority of IG-based models over traditional approaches through rigorous statistical testing and model validation. The methodology developed here extends naturally to nuclear power plant applications, where similar statistical patterns emerge in operational data. Our findings suggest that IG-based models provide more accurate predictions and better capture the underlying physical processes in power generation systems. |
| title | Inverse Gaussian Distribution, Introduction and Applications:Comprehensive Analysis of Power Plant Performance: A Study of Combined Cycle and Nuclear Power Plant |
| topic | Applications |
| url | https://arxiv.org/abs/2501.14820 |