A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting

Qingzhen Liu; Yuanbin Shen; Lei Wu; Jie Li; Lirong Zhuang; Shaofang Wang

doi:10.17775/CSEEJPES.2016.00080

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A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting

更新时间：2025-12-18

- A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting
- A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting
- 中国电机工程学会电力与能源系统学报(英文) 2018年4卷第2期页码：226-237
- 作者机构：
  
  1. Fuzhou University,,, CN,Fujian,Fuzhou
  2. Clarkson University,,, US,NY,Potsdam
  3. Quanzhou Electric Power Supply Co., State Grid,Quanzhou,China,362000
  4. Power Automation Department, China Electric Power Research Institute,Beijing,China,100192
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2016.00080
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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Qingzhen Liu, Yuanbin Shen, Lei Wu, 等. A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting[J]. 中国电机工程学会电力与能源系统学报(英文), 2018,4(2):226-237.

Qingzhen Liu, Yuanbin Shen, Lei Wu, et al. A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting[J]. CSEE Journal of Power and Energy Systems, 2018, 4(2): 226-237.
Qingzhen Liu, Yuanbin Shen, Lei Wu, 等. A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting[J]. 中国电机工程学会电力与能源系统学报(英文), 2018,4(2):226-237. DOI： 10.17775/CSEEJPES.2016.00080.

Qingzhen Liu, Yuanbin Shen, Lei Wu, et al. A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting[J]. CSEE Journal of Power and Energy Systems, 2018, 4(2): 226-237. DOI： 10.17775/CSEEJPES.2016.00080.

摘要

Abstract

This paper proposes a hybrid short-term load forecasting method

which is based on the fuzzy combination weights as well as the empirical mode decomposition process (FCW-EMD)

and support vector machine optimized via the Bat algorithm as well as the Kalman filtering process (KF-BA-SVM). The subjective weight is presented as a new theory and is applied to capture the inherent correlation effectively among hourly loads. Based on the proposed objective weights and subjective weights

the fuzzy combination weights theory (FCW)-a new similar day selection theory is presented

which improves the accuracy of the similar day selection

and correspondingly

makes the original data for EMD processing decrease dramatically. BA is introduced to optimize parameters of the SVM model for further improving the forecasting accuracy. Using the decomposed load series via empirical model decomposition (EMD) as inputs to SVM and further correcting the output of SVM via KF

a hybrid FCW-EMD and KF-BA-SVM short-term load forecasting method is established. Numerical case studies on the load forecasting of a transformer substation in south China show that the proposed hybrid forecasting model outperforms other forecasting methods and effectively improves the prediction accuracy.

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