[1]杨屹洲,方瑞明,黄文权,等.应用小波变换和支持向量机的商业电力负荷预测[J].华侨大学学报(自然科学版),2015,36(预先出版):0.
　YANG Yi-zhou,FANG Rui-ming,HUANG Wen-quan,et al.Commercial Power Load Forecasting Based on Wavelet Transform and SVM[J].Journal of Huaqiao University(Natural Science),2015,36(预先出版):0.
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应用小波变换和支持向量机的商业电力负荷预测
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第36卷
期数:
2015年预先出版
页码:
0
栏目:
出版日期:
2027-07-30

文章信息/Info

Title:
Commercial Power Load Forecasting Based on Wavelet Transform and SVM
作者:
杨屹洲1;  方瑞明1;  黄文权1;  梁颖1;  汪亮2
1. 华侨大学 信息科学与工程学院, 福建 厦门 361021;2. 厦门埃锐圣电力科技有限公司, 福建 厦门 361002
Author(s):
YANG Yi-zhou1;  FANG Rui-ming1;  HUANG Wen-quan1;  LIANG Ying1;  Wang Liang2
1. College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China; 2. Akson Power Technology Limited, Xiamen 361002, China
关键词:
支持向量机;  小波分解;  节能;  数据采集系统;  商业电力负荷;  负荷预测;  粒子群算法
Keywords:
wavelet transform and particle swarm optimization;  energy conservation;  supervisory control and data acquisition system;  commercial power load;  load forecasting;  wavelet transform and particle swarm optimization
分类号:
TM715
文献标志码:
A
摘要:
提出一种基于小波分解和支持向量机相结合的模型,并将其应用于预测商业建筑电力负荷.首先,基于商业建筑配电系统的数据采集系统实时监测数据,分析商业负荷用电特性,指出商业负荷的随机特性造成单一预测模型精度难以满足要求.其次,提出了一种基于小波分解和粒子群支持向量机的商业电力负荷预测算法.通过小波变换把负荷序列分解为不同频段的子序列,再对这些子序列分别采用不同的粒子群支持向量机模型进行预测,引入粒子群算法对支持向量机模型参数进行寻优.最后,将各分量预测值重构得到最终预测值.实验结果证明:小波分解后和粒子群支持向量机相结合的模型精度明显优于单一支持向量机模型.
Abstract:
In order to improve the efficiency of electric of business enterprise and reduce the energy consumption of commercial building, it is necessary to accurately predict the energy consumption of it. A commercial building power load model based on wavelet transform and SVM(support vector machine)was proposed. First of all, this paper based on the data of supervisory control and data acquisitio(SCADA)system of commercial building, analyzed the characteristics of commercial load, pointed out that the single forecasting model precision is difficult to meet the requirements caused by the random characteristics of commercial load; secondly, wavelet transform and particle swarm optimization(PSO)support vector machine were proposed for commercial load forecasting. Through the wavelet transform the load sequence was decomposed into components of different frequencies, then analyzed their characteristics to build PSO-SVM model for each component, and the PSO algorithm is used to give the optimal parameters. Finally, reconstruct them to obtain the final forecast. Experimental result shows that the wavelet SVM model is better than SVM model.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2013-11-26
通信作者: 方瑞明(1972-),男,教授,主要从事电气装置智能诊断、特种电机分析与设计的研究.E-mail:fangrm@hqu.edu.cn.
基金项目: 福建省自然科学基金资助项目(2012J01223)
更新日期/Last Update: 1900-01-01