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DiffLoad: Uncertainty Quantification in Electrical Load Forecasting With the Diffusion Model
descriptionPublicationkeyboard_double_arrow_right Article , Preprint 2025Embargo end date: 01 Jan 2023Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Authors: Zhixian Wang; Qingsong Wen; Chaoli Zhang; Liang Sun; +1 Authors
Zhixian Wang; Qingsong Wen; Chaoli Zhang; Liang Sun; Yi Wang;
doi: 10.1109/tpwrs.2024.3449032 , 10.48550/arxiv.2306.01001
arXiv: 2306.01001 , http://arxiv.org/abs/2306.01001
doi: 10.1109/tpwrs.2024.3449032 , 10.48550/arxiv.2306.01001
arXiv: 2306.01001 , http://arxiv.org/abs/2306.01001
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/}. Accepted by IEEE Transactions on Power Systems, 2024
arXiv.org e-Print Ar...arrow_drop_down
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Preprint . 2023
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Preprint . 2023
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IEEE Transactions on Power Systems
Article . 2025 . Peer-reviewed
License: IEEE Copyright
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Article . 2023
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arXiv.org e-Print Ar...arrow_drop_down
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Preprint . 2023
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Preprint . 2023
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Article . 2025 . Peer-reviewed
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Article . 2023
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DiffLoad: Uncertainty Quantification in Electrical Load Forecasting With the Diffusion Model
descriptionPublicationkeyboard_double_arrow_right Article , Preprint 2025Embargo end date: 01 Jan 2023Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Authors: Zhixian Wang; Qingsong Wen; Chaoli Zhang; Liang Sun; +1 Authors
Zhixian Wang; Qingsong Wen; Chaoli Zhang; Liang Sun; Yi Wang;
doi: 10.1109/tpwrs.2024.3449032 , 10.48550/arxiv.2306.01001
arXiv: 2306.01001 , http://arxiv.org/abs/2306.01001
doi: 10.1109/tpwrs.2024.3449032 , 10.48550/arxiv.2306.01001
arXiv: 2306.01001 , http://arxiv.org/abs/2306.01001
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/}. Accepted by IEEE Transactions on Power Systems, 2024
arXiv.org e-Print Ar...arrow_drop_down
arXiv.org e-Print Archive
Preprint . 2023
Data sources: arXiv.org e-Print Archive
arXiv.org e-Print Archive
Preprint . 2023
Data sources: arXiv.org e-Print Archive
IEEE Transactions on Power Systems
Article . 2025 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2023
License: arXiv Non-Exclusive Distribution
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citeCite
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arXiv.org e-Print Ar...arrow_drop_down
arXiv.org e-Print Archive
Preprint . 2023
Data sources: arXiv.org e-Print Archive
arXiv.org e-Print Archive
Preprint . 2023
Data sources: arXiv.org e-Print Archive
IEEE Transactions on Power Systems
Article . 2025 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2023
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
linkLink to
shareshareShare
citeCite
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Please grant OpenAIRE to access and update your ORCID works.
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DiffLoad: Uncertainty Quantification in Electrical Load Forecasting With the Diffusion Model