Short-term load forecasting based on hierarchical clustering algorithm and ISA-LSSVM

郑 乐; 徐青山; 冯小峰

doi:10.3969/j.issn.1009-1831.2022.05.009

您当前的位置：

首页 >

文章列表页 >

Short-term load forecasting based on hierarchical clustering algorithm and ISA-LSSVM

更新时间：2026-03-05

- Short-term load forecasting based on hierarchical clustering algorithm and ISA-LSSVM
- Vol. 24, Issue 5, (2022)
- 作者机构：
  
  1. 东南大学网络空间安全学院,南京,210096
  2. 东南大学电气工程学院,南京,210096
  3. 广东电网有限责任公司计量中心,广州,518049
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2022.05.009
  CLC：
- Published：2022
- 稿件说明：
移动端阅览
郑乐, 徐青山, 冯小峰. Short-term load forecasting based on hierarchical clustering algorithm and ISA-LSSVM[J]. 2022, 24(5).
DOI：

郑乐, 徐青山, 冯小峰. Short-term load forecasting based on hierarchical clustering algorithm and ISA-LSSVM[J]. 2022, 24(5). DOI： 10.3969/j.issn.1009-1831.2022.05.009.

摘要

针对不同类型用户的短期负荷预测，目前应用较为广泛的为支持向量机与深度学习模型。针对最小二乘支持向量机（least squares support vector machine，LSSVM）模型中超参数难以确定、模型对数据质量要求较高等问题，而集成常规优化算法又会有寻优速度慢、易陷入局部最优等问题，提出了一种混合模型。首先使用层次聚类（hierarchical clustering，HC）对原始特征数据进行聚类进而为同一类预测日建立对应LSSVM 模型，再通过改进的模拟退火算法（improved simulateanneal，ISA）对 LSSVM 中的超参数进行启发式搜索。最后通过对广东省佛山市某行业用户用电负荷进行负荷预测，与各种负荷预测模型性能进行对比，结果证明所提模型可有效提高负荷预测精度、缩短预测时间。

Abstract

For short-term load forecasting of different types of users

support vector machine and deep learning model are widely used at present. A hybrid model is proposed to solve the problems of the least squares support vector machine model

such as the difficulty in determining the super parameters

the high data quality requirements of the model

and the slow optimization speed and easy to fall into the local optimization of the integrated conventional optimization algorithm. Firstly

the original feature data is clustered by hierarchical clustering and then the corresponding least squares support vector machine model is established for the same prediction day. Then

the super parameters in least squares support vector machine are heuristic searched by the improved simulated annealing algorithm. Finally

by comparing the performance of the load forecasting model with that of various load forecasting models

the results show that the proposed model can effectively improve the ac?curacy of load forecasting and shorten the forecasting time.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Short-term power load forecasting method for industry based on multi scale weight adaptation and bidirectional gated recurrent unit

Determination of Key Moisture Contents in Particle Drying Process

Optimization for Overlaying Technology of OSB with Resin impregnated Paper

细木工板生产工艺的探讨

综合信息

Related Author

江山

李卫

汤子琪

Related Institution

阳光电源（上海）有限公司

AI问答

⁰