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Probability density prediction of peak load based on mixed frequency noise-assisted multivariate empirical mode decomposition

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Abstract

Accurate peak load forecasting is crucial to ensuring the reliable operation of the power system. However, existing prediction models often neglect full explanations for peak loads by factors that are often complex and fluctuating. Moreover, different factors are usually sampled at diverse frequencies, and existing processing methods have substantial limitations in mining irregular mixed-frequency multi-series information, leading to reduced accuracy. Consequently, this paper proposes an innovative ensemble peak-load prediction model that combines noise-assisted multivariate empirical mode decomposition algorithm for mixed-frequency sampling data with the quantile regression neural network to effectively tackle the aforementioned challenges. Firstly, noise-assisted multivariate empirical mode decomposition algorithm for mixed-frequency sampling data is employed to transform several mixed-frequency fluctuating time series into multiple sets of stationary sub-sequences. Secondly, the quantile prediction of peak load is performed for each group of subsequences using the quantile regression neural network. Finally, conditional quantiles under each quantile are accumulated as the samples for kernel density estimation to complete probability density forecasting. The case study validates the superiority and stability of the proposed model. Extensive experimental results on two real-world datasets from the VT and the Houston in America show that our proposed model is significantly superior to other benchmark methods regarding peak load especially on extremes.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This paper is funded by the National Natural Science Foundation (Nos. 72171068, 71771073), and the Anhui Provincial Natural Science Foundation for Distinguished Young Scholars (2108085J36). Meanwhile, we thank Dr Bo Wang (Economic & Technology Research Institute, State Grid Hubei Electric Power Company, Wuhan 430077, China) and Dr Shuo Wang (the School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK) for suggestions on improving language quality and organization.

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, China

    Yaoyao He, Yuting Liu & Wanying Zhang

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making (Hefei University of Technology), Ministry of Education, Hefei, 230009, China

    Yaoyao He

  3. Anhui Key Laboratory of Philosophy and Social Sciences of Energy and Environment Smart Management and Green Low Carbon Development, Hefei University of Technology, Hefei, 230009, China

    Yuting Liu

  4. School of Economics and Management of Anhui Jianzhu University, Hefei, 230022, Anhui, China

    Wanying Zhang

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  1. Yaoyao He
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Contributions

Yaoyao He: Conceptualization, Software, Writing. Yuting Liu: Original draft preparation, Methodology, Validation, Investigation. Wanying Zhang: Review, Editing.

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Correspondence to Yaoyao He.

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He, Y., Liu, Y. & Zhang, W. Probability density prediction of peak load based on mixed frequency noise-assisted multivariate empirical mode decomposition. Appl Intell 54, 2648–2672 (2024). https://doi.org/10.1007/s10489-024-05286-4

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