基于VMD和PSO-SVR的短期电力负荷多阶段优化预测

doi:10.11930/j.issn.1004-9649.202109157

中国电力 ›› 2022, Vol. 55 ›› Issue (8): 171-177.DOI: 10.11930/j.issn.1004-9649.202109157

基于VMD和PSO-SVR的短期电力负荷多阶段优化预测

李文武^1,2, 石强^1,2, 李丹^1,2, 胡群勇³, 唐芸¹, 梅锦超^1,2

1. 三峡大学电气与新能源学院，湖北宜昌 443002;
2. 梯级水电站运行与控制湖北省重点实验室（三峡大学），湖北宜昌 443002;
3. 广东电网有限责任公司中山供电局，广东中山 528400

收稿日期:2021-10-08 修回日期:2022-06-29 发布日期:2022-08-18
作者简介:李文武（1975—），男，博士，教授，从事人工智能在电力系统中的应用研究，E-mail：liwenwu7508@ctgu.edu.cn;石强（1995—），男，通信作者，硕士研究生，从事电力负荷预测研究，E-mail：sparkqiang1995@ctgu.edu.cn
基金资助:
国家自然科学基金资助项目（51807109）；梯级水电站运行与控制湖北省重点实验室开放基金项目（2019KJX08）；三峡大学硕士学位论文培优基金项目（2020SSPY055）。

Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR

LI Wenwu^1,2, SHI Qiang^1,2, LI Dan^1,2, HU Qunyong³, TANG Yun¹, MEI Jinchao^1,2

1. College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China;
2. Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control (China Three Gorges University), Yichang 443002, China;
3. Zhongshan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Zhongshan 528400, China

Received:2021-10-08 Revised:2022-06-29 Published:2022-08-18
Supported by:
This work is supported by National Natural Science Foundation of China (No.51807109), Open Fund of Hubei Provincial Key Laboratory of Cascaded Hydropower Stations Operation & Control (No.2019KJX08), Research Fund for Excellent Dissertation of China Three Gorges University (No.2020SSPY055)

摘要/Abstract

摘要： 为降低短期负荷序列的非线性以提升预测精度，提出一种基于多阶段优化的变分模态分解（variational mode decomposition, VMD）和粒子群算法优化支持向量回归（particle swarm optimization support vector regression, PSO-SVR）的短期电力负荷预测模型。第1阶段采用VMD优化和预处理原始负荷序列，分解获得多个较为平稳的模态分量。第2阶段利用相空间重构优化重组各序列分量，并针对各分量分别建立支持向量回归（support vector regression，SVR）预测模型。第3阶段将粒子群算法（particle swarm optimization，PSO）用于优化SVR模型内部参数，便于更好地进行训练和预测。最后累加所有序列的预测值，实现短期电力负荷预测。研究结果表明：所提方法可以取得更高的预测精度。

关键词: 短期电力负荷预测, 变分模态分解, 相空间重构, 粒子群优化

Abstract: To reduce the non-linearity of the short-term load sequence and improve the prediction accuracy, a short-term load forecasting model based on multi-stage optimization variational mode decomposition (VMD) and particle swarm optimization optimize support vector regression (PSO-SVR) is proposed. In the first stage, VMD optimization and pre-processing of the original load sequence are used to decompose and obtain multiple relatively stable modal components. In the second stage, phase space reconstruction is used to optimize and reorganize each sequence component, and establish support vector regression（SVR）prediction model for each component. In the third stage, the particle swarm optimization（PSO）algorithm is applied to optimize the internal parameters of the SVR model to facilitate better training and forecasting. Finally, the predicted values of all sequences are accumulated to realize the short-term power load forecast. The results show that the proposed method can achieve higher prediction accuracy.

Key words: short-term power load forecasting, variational mode decomposition, phase space reconstruction, particle swarm optimization

李文武, 石强, 李丹, 胡群勇, 唐芸, 梅锦超. 基于VMD和PSO-SVR的短期电力负荷多阶段优化预测[J]. 中国电力, 2022, 55(8): 171-177.

LI Wenwu, SHI Qiang, LI Dan, HU Qunyong, TANG Yun, MEI Jinchao. Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR[J]. Electric Power, 2022, 55(8): 171-177.

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基于VMD和PSO-SVR的短期电力负荷多阶段优化预测

Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR

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基于VMD和PSO-SVR的短期电力负荷多阶段优化预测

Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR

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