Research of load forecasting method based on ensemble Kalman filter and phase-space reconstruction

付文杰; 李 化; 杨伯青; 宋 杰

doi:10.3969/j.issn.1009-1831.2022.01.009

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Research of load forecasting method based on ensemble Kalman filter and phase-space reconstruction

更新时间：2026-03-05

- Research of load forecasting method based on ensemble Kalman filter and phase-space reconstruction
- Vol. 24, Issue 1, (2022)
- 作者机构：
  
  1. 国网河北省电力有限公司保定市供电分公司,河北,保定,071000
  2. 国电南瑞南京控制系统有限公司,南京,211100
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2022.01.009
  CLC：
- Published：2022
- 稿件说明：
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付文杰, 李化, 杨伯青, et al. Research of load forecasting method based on ensemble Kalman filter and phase-space reconstruction[J]. 2022, 24(1).
DOI：

付文杰, 李化, 杨伯青, et al. Research of load forecasting method based on ensemble Kalman filter and phase-space reconstruction[J]. 2022, 24(1). DOI： 10.3969/j.issn.1009-1831.2022.01.009.

摘要

提出一种基于集合卡尔曼滤波和相空间重构的组合模型来优化预测结果。对于负荷数据时间序列，首先使用延迟坐标嵌入方法对其进行相空间重构。基于局部平均法对下一时刻的负荷状态进行预测，其中最临近向量的数目采用循环迭代的方式选取，以获得对不同负荷序列的自适应性。根据无迹变换理论，对负荷预测值选取合适数量的Sigma点组成集合并使用集合卡尔曼滤波器进行数据同化，从而能够在容忍采样噪声的同时给出负荷的最优估计。以非侵入式量测方式获取河北保定市50个家庭用户的负荷数据作为训练集进行试验，给出了电热水器和空调负荷以及家庭总负荷的预测结果和误差分析。结果显示，与单纯基于相空间重构的预测结果进行比较，该组合模型具有更好的预测性能，且对分项电器负荷和家庭总负荷均具有较好的适应性。

Abstract

A load forecasting technique based on a combinedmodel formed by ensemble Kalman filter（EnKF）and phase -spacereconstruction（PSR）is proposed to optimize forecasting results.For the load data time-series

phase-space reconstruction is firstlyimplemented using delayed coordinate embedding. Future loadstates can be predicted by local averaging. To achieve adaptive feature to different load time - series

cyclic iterative calculation isused to select the number of nearest vectors. According to unscented transformation theory

ensemble is formed by appropriate number of Sigma points of the predicted load value and data assimilation can be realized using ensemble Kalman filter. Therefore

optimum estimation can be obtained while measurement noise exists.Using non-instrusive measurement

forecasting results and erroranalysis for loads of electrical water heater and air -conditioner aswell as total household are done by adopting 50 end users datafrom Baoding of Hebei province as training data set. The resultsshow that

compared to those derived from pure phase-space reconstruction forecasting

the proposed method features have better-forecasting performance and good adaption both for individual appliance load and total household load.

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