计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 58-65.doi: 10.11896/JsJkx.191000042

• 人工智能 • 上一篇    下一篇

改进的支持向量回归机在电力负荷预测中的应用

唐承娥1, 韦军2   

  1. 1 广西职业技术学院机电与信息工程学院 南宁 530226;
    2 广西壮族自治区招生考试院 南宁 530021
  • 发布日期:2020-07-07
  • 通讯作者: 唐承娥(735438514@qq.com)
  • 基金资助:
    2018年广西高校中青年教师基础能力提升项目(2018KY0956)

Application of Power Load Prediction Based on Improved Support Vector Regression Machine

TANG Cheng-e1 and WEI Jun2   

  1. 1 College of Electromechanical and Information Engineering,Guangxi Vocational and Technical College,Nanning 530226,China
    2 Guangxi Zhuang Autonomous Region Admission Examination Institute,Nanning 530021,China
  • Published:2020-07-07
  • About author:TANG Cheng-e, born in 1983, lecturer.Her main research interests include neural networks and automation of electric power systems.
  • Supported by:
    This work was supported by the 2018 Guangxi College Young and Middle-aged Teacher’s Basic Ability Improvement ProJect(2018KY0956).

摘要: 电力预测是一项重要的工程应用。为了解决多层次粒度支持向量回归机(Dynamical Granular Support Vector Regression Machine,DGSVRM)预测电力负低荷精度的问题,提出一种基于萤火虫群优化(Glowworm Swarm Optimization,GSO)算法与模式搜索算法(Pattern Search,PS)的混合算法来优化DGSVRM预测模型的关键参数。仿真实验表明,通过优化参数之后,预测模型的预测精度得到很大提高。

关键词: 多层次粒度支持向量回归机, 模式搜索算法, 萤火虫群优化

Abstract: Electricity forecasting is an important engineering application.In order to solve the accuracy problem of dynamical granular support vector regression machine for power load forecasting (DGSVRM),this paper proposes a hybrid algorithm of glowworm swarm optimization (GSO) and pattern search (PS) to optimize the key parameters of DGSVRM forecasting model.Simulation results show that the prediction accuracy is greatly improved by optimizing the parameters of the prediction model.

Key words: Dynamical granular support vector regression machine, Glowworm swarm optimization, Pattern search algorithm

中图分类号: 

  • TP391
[1] SAAB S,BADR E,NASR G.Univariate modeling and forecasting of energy consumption:the case of electricity in Lebanon.Energy,2001,26(1):1-14.
[2] GOIA A,MAY C,FUSAI G Functional clustering and linear regression for peak load forecasting.International Journal of Forecasting,2010,26(4):700-711.
[3] AMARA WICKRAMA H A,HUNT L C.Electricity demand for Sri Lanka:a time series analysis.Energy,2008,33(5):724-739.
[4] HIPPERT H S,PEDREIRA C E,SOUZA R C.Neural networks for short-term load forecasting:A review and evaluation.IEEE Transactions on Power Systems,2001,16(1):44-55.
[5] MANDAL P,SENJYU T,NAOMITSU U,et al.A neural network based several-hour-ahead electric load forecasting using similar days approach.International Journal of Electrical Power & Energy Systems,2006,28(6):367-373.
[6] LAURET P,FOCK E,RANDRIANARIVONY R N,et al.
[7] Bayesian neural network approach to short time load forecasting.Energy Conversion and Management,2008,49(5):1156-1166.
[8] CRISTIANINI N,TAYLOR J S.支持向量机导论.李国正,王猛,曾华军,译.北京:电子工业出版社,2004.
[9] VAPNIK V N.The nature of statistical learning theory.BeiJing:Tsinghua University Press,2000.
[10] SAPANKEVYCH N L,SANKAR R.Time Series Prediction Using Support Vector Machines:A Survey.IEEE Computational Intelligence Magazine,2009,4(2):24-38.
[11] ELATTAR E E,GOULERMAS J,WU Q H.Electric Load Forecasting Based on Locally Weighted Support Vector Regression.IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews,2010,40(4):438-447.
[12] WANG J,ZHU W,ZHANG W,et al.A trend fixed model combined with the E-SVR for short-tenor on firstly and seasonal adJustment forecasting of electricity demand.Policy,2009,37(11):4901-4909.
[13] DUAN P,XIE K C,GUO T T,et al.Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques.Energies,2011,4(1):173-184.
[14] BAO Y,HU Z,XIONG T.A PSO and pattern search based memetic algorithm for SVMs parameters optimization.Neurocomputing,2013,117:98-106.
[15] CHAPELLE,VAPNIK V,BOUSQUE T,et al.Choosing multiple parameters for support vector machines.Machine Learning,2002,46(1/2/3):131-159.
[16] GOMES T A F,PRUDENCIO R B C,SOARES C,et al.Combining meta-learning and search techniques to select parameters for support vector machines.Neurocomputing,2012,75(1):3-13.
[17] HONG W C,DONG Y,ZHANG W Y,et al.Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm .International Journal of Electrical Power&Energy Systems,2013,44(1):604-614.
[18] HONG W C.Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm.Energy,2011,36(9):5568-5578.
[19] AREIBI S,YANG Z.Effective Memetic Algorithms for VLSI design genetical gorithms local search mufti level clustering.Evolutionary Computation,2004,12(3):327-353.
[20] BAO Y,HU Z,XIONG T.A PSO and pattern search based memetic algorithm for SVMs parameters optimization.Neurocomputing,2013,117:98-106.
[21] KRISHNANAND K N,GHOSE D.Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications.Multiagent and Grid Systems-An International Journal,2006,2:209-222.
[22] YARINEZHAD R,SARABI A.A New Routing Algorithm for Vehicular Ad-hoc Networks based on Glowworm Swarm Optimization Algorithm.Journal of AI and Data Mining,2019,7(1):69-76.
[23] KALAISELVI T,NAGARAJA P,ABDUL B Z.A Comprehensive Study on Glowworm Swarm Optimization.ComputationalMethods,Communication Techniques and Informatics,2017,8(1):332-337.
[24] QIONG,PENG L.A Self-Adaptive Step Glowworm Swarm Optimization Approach.International Journal of Computational Intelligence and Applications,2019,18(1):1950004-1-195004-11.
[25] GUO H S,WANG W J.Dynamical Granular Support Vector Regression Machine.Journal of Software,2013,24(11):2535-2547.
[26] VAPNIK V N.Statistical Learning Theory.New York:John Wiley & Sons,1998.
[27] BERMEJO P,GAMEZ J A,PUERTA J M.AGRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets.Pattern.Recognition Letters,2011,32(5):701-711.
[28] VAPNIK V,GOLOWICH SE,SMOLA A.Support vector method for function approximation,regression estimation,and signal processing//Proc.of the Neural Information Processing Systems.Cambridge:MIT Press,1997.
[29] COLLOBERT R,BENGIO S.SVMTorch:Support vector ma-chines for large-scale regression problems.Journal of Machine Learning Research,2001,1:143-160.
[30] TAY FEH,CAO L J.Application of support vector machines in financial time series for ecasting.Omega,2001,29(4):309-317.
[31] HERNANDEZ L,BALADRON C,AGUIAR J M,et al.A Survey on Electric Power Demand Forecasting:Future Trends in Smart Grids,Microgrids and Smart Buildings.IEEE Communications Surveys & Tutorials,2014,16(3):1460-1495.
[32] MOMMA M,BENNETT K P.A pattern search method for model selection of support vector regression//Proceedings of the Second Siam International Conference on Data Mining.2002:261-274.
[33] DOLAN E D J,LEWIS R M,TORCZON V.On the local convergence of pattern search.SIAM Journal of Optimization,2003,14(2):567-583.
[34] KENEDY J R.EBERHAR T.Optimization//Proceedings Particle swarm of TEEE Tnternational Conference on Neural Networks.1995:1942-948.
[35] ZHANG W Y,HONG W C,DONG Y C,et al.Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting.Energy,2012,45(1):850-858.
[36] HU Z Y.Research on Parameter Optimization of Support Vector Machine Based on SVM.Wuhan:Huazhong University of Science and Technology,2011.
[37] HSU C W,CHANG C C,LIN C J.A practical guide to support vector classification,Department of Computer Science.National Taiwan University,2008.
[38] HYNDMAN R J,KOEHLER A B.Another look at measures of forecast accuracy.International Journal of Forecasting,2006,22(4):679-688.
[39] ARMSTRONG J S,COLLOPY F.Error measures for generalizing about forecasting methods:empirical comparisons.International Journal of Forecasting,1992,8(1):69-80.
[40] DIEBOLD F X,MARIANO R.Comparing predictive accuracy .Journal of Business and Economic Statistics,1995,13:253-263.
[41] CONOVER W.Practical nonparametric statistics(2nd ed).New York:Wiley & Sons,1980.
[42] LIU X J,FANG J A.Long Term Load Forecasting Based on a Time-Variant Ratio MultiobJective Optimization Fuzzy Time Series Model.Mathematical Problems in Engineering,2013,2013:1-7.
[43] WANG J,ZHU W,ZHANG W,et al.A trend fixed model combined with the E-SVR for short-tenor on firstly and seasonal adJustment forecasting of electricity demand.Policy,2009,37(11):4901-4909.
[1] 吴斌,崔志勇,倪卫红.
具有混合群智能行为的萤火虫群优化算法研究
Research on Glowworm Swarm Optimization with Hybrid Swarm Intelligence Behavior
计算机科学, 2012, 39(5): 198-200.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!