Constrained Support Vector Machines for Photovoltaic In-Feed Prediction

被引：0
|
作者：
Hildmann, Marcus [1 ]
Rohatgi, Abhishek [2 ]
Andersson, Goeran [1 ]
机构：
[1] Swiss Fed Inst Technol, Power Syst Lab, Zurich, Switzerland
[2] Natl Univ Singapore, Inst Energy Studies, Singapore, Singapore
来源：
2013 1ST IEEE CONFERENCE ON TECHNOLOGIES FOR SUSTAINABILITY (SUSTECH) | 2013年
关键词：
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we introduce a constrained Support Vector Machine ( SVM) to predict photovoltaic (PV) in-feed. We derive the SVM algorithm with linear constraints and test the method on German PV in-feed with constraints reflecting physical boundaries. We show that the new algorithm shows a significant better performance than a constrained ordinary least squares (OLS) estimator.
引用
收藏
页码：23 / 28
页数：6
相关论文
共 50 条
  • [1] Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System
    Wenying Zhang
    Huaguang Zhang
    Jinhai Liu
    Kai Li
    Dongsheng Yang
    Hui Tian
    [J]. IEEE/CAA Journal of Automatica Sinica, 2017, 4 (03) : 520 - 525
  • [2] Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System
    Zhang, Wenying
    Zhang, Huaguang
    Liu, Jinhai
    Li, Kai
    Yang, Dongsheng
    Tian, Hui
    [J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2017, 4 (03) : 520 - 525
  • [3] Constrained efficient global optimization with support vector machines
    Anirban Basudhar
    Christoph Dribusch
    Sylvain Lacaze
    Samy Missoum
    [J]. Structural and Multidisciplinary Optimization, 2012, 46 : 201 - 221
  • [4] Learning Sign-Constrained Support Vector Machines
    Tajima, Kenya
    Tsuchida, Kohei
    Zara, Esmeraldo Ronnie R.
    Ohta, Naoya
    Kato, Tsuyoshi
    [J]. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2021, : 3264 - 3271
  • [5] Constrained efficient global optimization with support vector machines
    Basudhar, Anirban
    Dribusch, Christoph
    Lacaze, Sylvain
    Missoum, Samy
    [J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2012, 46 (02) : 201 - 221
  • [6] The Prediction of Earnings Based on Support Vector Machines
    Li Yonghen
    Xu Honge
    [J]. PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON RISK MANAGEMENT & ENGINEERING MANAGEMENT, VOLS 1 AND 2, 2008, : 891 - 894
  • [7] Using Support Vector Machines for numerical prediction
    Hussain, Shahid
    Khamisani, Vaqar
    [J]. INMIC 2007: PROCEEDINGS OF THE 11TH IEEE INTERNATIONAL MULTITOPIC CONFERENCE, 2007, : 88 - 92
  • [8] Probability prediction using support vector machines
    McKay, D
    Fyfe, C
    [J]. KES'2000: FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, VOLS 1 AND 2, PROCEEDINGS, 2000, : 189 - 192
  • [9] Product quality prediction with support vector machines
    Liu, Xinggao
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS, 2006, 3973 : 1126 - 1131
  • [10] Secondary structure prediction with support vector machines
    Ward, JJ
    McGuffin, LJ
    Buxton, BF
    Jones, DT
    [J]. BIOINFORMATICS, 2003, 19 (13) : 1650 - 1655
12345