2025年7月26日 周六
Home > Archive>Volume 39, Issue 5, 2024 >163-171. DOI:10.19781/j.issn.1673-9140.2024.05.017
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Prediction method of short‑term PV output power based on PCA‑VMD‑MVO‑SVM
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(1.College of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China; 2.State Key Laboratory of Disaster Prevention and Reduction for Power Grid, Changsha 410114, China; 3.Yongzhou Power Supply Company, State Grid Hunan Electric Power Co., Ltd., Yongzhou 425000, China; 4.Hunan Red Solar New Energy Science and Technology Co., Ltd., Changsha 410111, China)

Clc Number:

TM615

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    Abstract:

    To enhance the accuracy and reliability of short-term photovoltaic (PV) output power forecasting, a hybrid model is proposed, which integrates principal component analysis (PCA), variational mode decomposition (VMD), and multi-verse optimizer (MVO) to optimize a support vector machine (SVM) for PV output power prediction. Initially, PCA's data analysis capabilities and VMD's data decomposition performance are leveraged to reduce the dimensionality and decompose the multidimensional training data. Subsequently, the extracted dataset is fed into an SVM prediction model optimized by the MVO algorithm to obtain PV output power forecast components for different intrinsic modes. Finally, the results of these forecast components are aggregated. The research findings indicate that the proposed model achieves mean absolute percentage errors (MAPEs) of 0.7453%, 0.5105%, and 1.0156% for sunny, partly cloudy, and rainy days, respectively. Taking partly cloudy weather as an example, the MAPE of the proposed model is reduced by 3.8207%, 2.9173%, and 1.8438% compared to the MVO-SVM, VMD-MVO-SVM, and PCA-MVO-SVM models, respectively.

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邹 港,赵 斌,罗 强,梁 告,王 力.基于PCA‑VMD‑MVO‑SVM的短期光伏输出功率预测方法[J].电力科学与技术学报英文版,2024,39(5):163-171. ZOU Gang, ZHAO Bin, LUO Qiang, LIANG Gao, WANG Li. Prediction method of short‑term PV output power based on PCA‑VMD‑MVO‑SVM[J]. Journal of Electric Power Science and Technology,2024,39(5):163-171.

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  • Online: December 02,2024
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