基于GA和SVM的电力负荷预测方法研究

计算机科学 ›› 2014, Vol. 41 ›› Issue (Z6): 91-93.

基于GA和SVM的电力负荷预测方法研究

孟凡喜,屈鸿,侯孟书

电子科技大学计算机科学与工程学院计算智能实验室成都611731;电子科技大学计算机科学与工程学院计算智能实验室成都611731;电子科技大学计算机科学与工程学院计算智能实验室成都611731

出版日期:2018-11-14 发布日期:2018-11-14

Method of Short-term Load Forecasting Based on GA and SVM

MENG Fan-xi,QU Hong and HOU Meng-shu

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 提出一种基于支持向量机(SVM)技术和遗传算法优化技术(GA)的电力系统短期负荷预测算法。以历史数据、气象因素和日历因素等作为输入,建立预测模型,对未来1个小时的电力负荷值进行预测。该模型采用结构风险最小化原则替代传统的经验风险最小化,以充分提炼出原始数据和其它数据的一些信息,并采用遗传算法对支持向量机中的参数进行优化来提高预测模型的预测能力和训练速度,并具有良好的泛化能力。实验表明,使用上述方法进行短期电力负荷预测,具有良好的有效性和可行性,与BP网络法预测的结果相比具有更好的精度和较强的鲁棒性。

关键词: 电力系统负荷预测,短期电力负荷预测,支持向量机,遗传算法优化中图法分类号TP391文献标识码A

Abstract: In this paper,a method based on support vector machine and genetic algorithm was proposed for the power system load forecasting．In this method,a next hour load forecast is developed by using structure risk minimization instead of traditional empiric risk minimization to mine more information from the original data.The genetic algorithm is used to optimize the SVM parameters to improve the performance of forecasting and the training speed．Historical load,atmospheric data and the calendar factors are the model inputs．Forecasting results show that this model is effective and feasible,as well as the better robustness and forecast accuracy than the BP neural method．

Key words: Power system load forecasting,Short-term load forecasting,Support vector machine,Genetic algorithm optimization

孟凡喜,屈鸿,侯孟书. 基于GA和SVM的电力负荷预测方法研究[J]. 计算机科学, 2014, 41(Z6): 91-93. https://doi.org/

MENG Fan-xi,QU Hong and HOU Meng-shu. Method of Short-term Load Forecasting Based on GA and SVM[J]. Computer Science, 2014, 41(Z6): 91-93. https://doi.org/

参考文献

[1] 康重庆,夏清,刘楠．电力系统负荷预测[M].北京:中国电力出版社,2007
[2] 庞清乐．基于粗糙集理论的神经网络预测算法及其在短期负荷预测中的应用[J]．电网技术,2010,34(12):168-173
[3] Cai Y,Wang J Z ,Tang Y,et al．An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) & HSARTMAP (hyper-spherical ARTMAP network) neural network[J]．Energy,2011,36
[4] 马文晓,白晓民,沐连顺.基于人工神经网络和模糊推理的短期负荷预测方法[J].电网技术,2003,7(5):29-32
[5] Rahman S,Bhatnagar R．An expert system based algorithm for short term load forecast[J]．IEEE Transactions on Power Systems,1998,3(2)
[6] Hong W C．Application of chaotic ant swarm optimization in electric load forecasting[J]．Energy Policy,2010,38(10)
[7] Al-Hamadi H M,Soliman S A．Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model[J].Electric Power Systems Research,2004,68(1)
[8] Huang S J,Shih K R．Short-term load forecasting via ARMA model identification including nongaussian process considerations[J]．IEEE Transactions on Power Systems,2003,18(2):673
[9] Keerthi S,Gibert E．Convergence of a generalized SMO algorithm for SVM classifier design[J]．Machine Learning,2002,46(3):351-360
[10] 雷英杰,张善文,李续武,等.MATLAB遗传算工具箱及其应用[M].西安:西安电子科技大学出版社,2005
[11] Reis A J R,Silva A P A．Feature extraction via multi-resolution analysis for short-term load forecasting[J]．IEEE Trans on Power Systems,2006,21(1):392-401
[12] 赵杰辉,葛少云,刘自发．基于主成分分析的径向基函数神经网络在电力系统负荷预测中的应用[J]．电网技术,2004,28(5):35-40

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed