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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2407.10878 |
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| _version_ | 1866913431494328320 |
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| author | Peter, Philipp Kai Li, Yulin Li, Ziyue Ketter, Wolfgang |
| author_facet | Peter, Philipp Kai Li, Yulin Li, Ziyue Ketter, Wolfgang |
| contents | Natural gas demand is a crucial factor for predicting natural gas prices and thus has a direct influence on the power system. However, existing methods face challenges in assessing the impact of shocks, such as the outbreak of the Russian-Ukrainian war. In this context, we apply deep neural network-based Granger causality to identify important drivers of natural gas demand. Furthermore, the resulting dependencies are used to construct a counterfactual case without the outbreak of the war, providing a quantifiable estimate of the overall effect of the shock on various German energy sectors. The code and dataset are available at https://github.com/bonaldli/CausalEnergy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10878 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market Peter, Philipp Kai Li, Yulin Li, Ziyue Ketter, Wolfgang Machine Learning Artificial Intelligence Natural gas demand is a crucial factor for predicting natural gas prices and thus has a direct influence on the power system. However, existing methods face challenges in assessing the impact of shocks, such as the outbreak of the Russian-Ukrainian war. In this context, we apply deep neural network-based Granger causality to identify important drivers of natural gas demand. Furthermore, the resulting dependencies are used to construct a counterfactual case without the outbreak of the war, providing a quantifiable estimate of the overall effect of the shock on various German energy sectors. The code and dataset are available at https://github.com/bonaldli/CausalEnergy. |
| title | Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.10878 |