DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866916375948165120 |
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| author | Wang, Zhixian Wen, Qingsong Zhang, Chaoli Sun, Liang Wang, Yi |
| author_facet | Wang, Zhixian Wen, Qingsong Zhang, Chaoli Sun, Liang Wang, Yi |
| contents | Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch. The integration of renewable energy sources and the occurrence of external events, such as the COVID-19 pandemic, have rapidly increased uncertainties in load forecasting. The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty. Separating these types of uncertainties can help decision-makers better understand where and to what extent the uncertainty is, thereby enhancing their confidence in the following decision-making. This paper proposes a diffusion-based Seq2Seq structure to estimate epistemic uncertainty and employs the robust additive Cauchy distribution to estimate aleatoric uncertainty. Our method not only ensures the accuracy of load forecasting but also demonstrates the ability to separate the two types of uncertainties and be applicable to different levels of loads. The relevant code can be found at \url{https://anonymous.4open.science/r/DiffLoad-4714/}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_01001 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model Wang, Zhixian Wen, Qingsong Zhang, Chaoli Sun, Liang Wang, Yi Machine Learning Artificial Intelligence Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch. The integration of renewable energy sources and the occurrence of external events, such as the COVID-19 pandemic, have rapidly increased uncertainties in load forecasting. The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty. Separating these types of uncertainties can help decision-makers better understand where and to what extent the uncertainty is, thereby enhancing their confidence in the following decision-making. This paper proposes a diffusion-based Seq2Seq structure to estimate epistemic uncertainty and employs the robust additive Cauchy distribution to estimate aleatoric uncertainty. Our method not only ensures the accuracy of load forecasting but also demonstrates the ability to separate the two types of uncertainties and be applicable to different levels of loads. The relevant code can be found at \url{https://anonymous.4open.science/r/DiffLoad-4714/}. |
| title | DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2306.01001 |