A Short-term Load Forecasting Method Based on Support Vector Machine with Whale Optimization Parameters

Chen You-peng; Chen Jing-hua

doi:10.12052/gdutxb.190099

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A Short-term Load Forecasting Method Based on Support Vector Machine with Whale Optimization Parameters

更新时间：2026-05-27

- A Short-term Load Forecasting Method Based on Support Vector Machine with Whale Optimization Parameters
- Vol. 37, Issue 3, Pages: 75-81(2020)
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.190099
  CLC： TM734
- Online First：12 May 2020，
  
  Published：2020
- 稿件说明：
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陈友鹏, 陈璟华. 基于鲸鱼优化参数的最小二乘支持向量机短期负荷预测方法[J]. 广东工业大学学报, 2020, 37(3): 75-81.

Chen You-peng, Chen Jing-hua. A Short-term Load Forecasting Method Based on Support Vector Machine with Whale Optimization Parameters[J]. 2020, 37(3): 75-81.
陈友鹏, 陈璟华. 基于鲸鱼优化参数的最小二乘支持向量机短期负荷预测方法[J]. 广东工业大学学报, 2020, 37(3): 75-81. DOI： 10.12052/gdutxb.190099.

Chen You-peng, Chen Jing-hua. A Short-term Load Forecasting Method Based on Support Vector Machine with Whale Optimization Parameters[J]. 2020, 37(3): 75-81. DOI： 10.12052/gdutxb.190099.

摘要

大量分布式能源站的出现以及电动汽车的普及

给电力系统的安全、经济运行带来影响的同时

传统的负荷预测方法也面临挑战。针对这个问题

提出了利用鲸鱼算法优化最小二乘支持向量机(Whale Optimization Algorithm-Least Squares Support Vector Machine

WOA-LSSVM)进行短期电力系统负荷预测。利用鲸鱼算法全局寻优能力强、收敛速度快的优点

弥补最小二乘支持向量机(Least Squares Support Vector Machine

LSSVM)选参的盲目性，提高LSSVM的负荷预测精度。采用WOA-LSSVM对2013年浙江某地区历史负荷数据预测未来1 d的负荷

并与粒子群优化最小二乘支持向量机模型和标准LSSVM模型预测结果对比。结果表明

基于鲸鱼优化LSSVM的短期负荷预测具有较高的预测精度

相对误差较小。

Abstract

With the emergence of a large number of distributed energy stations and the popularity of electric vehicles

the security and economic operation of the power system are affected. At the same time

the traditional load forecasting methods are also facing challenges. To solve this problem

a Whale Optimization Algorithm-Least Squares Support Vector Machine (WOA-LSSVM) is proposed for short-term power system load forecasting. Using the advantages of whale algorithm such as strong global optimization ability and fast convergence speed

the blindness of parameter selection of LS-SVM is overcome

and the load forecasting accuracy of LS-SVM is improved. WOA-LSSVM is used to forecast the load of a certain area in Zhejiang province in the next day based on the historical load data in 2013. The results are compared with those of the particle swarm optimization least squares support vector machine model and the standard LSSVM model. The results show that the short-term load forecasting based on the whale optimization LSSVM has a higher forecasting accuracy and a smaller relative error.

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references

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