基于RWT-SVM的台区配电网日前负荷预测研究
作者:
作者单位:

南瑞集团国网电力科学研究院有限公司

中图分类号:

TM743?????????????????????????????????????????

基金项目:

国家电网公司科技项目(524608210006)


Research on Load Forecasting Method of Distributed Power Grids Based on RWT-SVM
Affiliation:

Nari Group Corporation

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    摘要:

    前负荷预测是电力调度中心制定调度计划的重要工作,对于制定合理的调度计划,保证电力系统运行的安全可靠性具有重要意义。电力负荷时间序列通常存在随机误差,并且基于智能算法的预测模型为了充分提取负荷信息,结构复杂,计算量大。针对该问题,本文利用台区配电网的历史电力负荷时间序列,提出一种基于重复小波变换-支持向量机(Repeated wavelet transform-support vector machine,RWT-SVM)混合模型的日前电力负荷预测方法。该方法利用小波变换技术将台区配电网电力负荷时间序列分解为多个子序列;利用平均绝对值误差计算每个子序列的预报误差贡献度;对平均绝对值误差最大的序列进行进一步分解,从而提升模型的预测能力,得到精度更高的预测结果。仿真结果表明所提出的RWT-SVM混合模型的预测方法与其它3种方法相比精度更高。

    Abstract:

    Day-ahead load forecasting is an important task for the power dispatching center to formulate dispatching plans. It is of great significance for formulating reasonable dispatching plans and ensuring the safety and reliability of power system operation. The time series of power loads experience random errors. The structures of intelligent algorithm based prediction models are complex, and calculation loads are heavy to fully extract load information. To solve this problem, this paper proposes a day-ahead power load forecasting method based on repeated wavelet transform-support vector machine(RWT-SVM) by using the historical power load time series of distributed power grids. The method uses WT to decompose the power load time series of distributed power grids into multiple subsequences; the MAE is applied to calculate the prediction error contribution of each subsequence; further decomposes the sequence with the largest MAE to improve the prediction ability of the model. The simulation results show that the forecasting method based on RWT-SVM is more accurate than other methods.

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丁宏,陶晓峰,陆春艳,张士成.基于RWT-SVM的台区配电网日前负荷预测研究[J].南京信息工程大学学报,,():

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  • 收稿日期:2022-05-31
  • 最后修改日期:2022-06-15
  • 录用日期:2022-06-16

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