A Method for Short-Term Wind Power Prediction With Multiple Observation Points

被引:122
|
作者
Khalid, Muhammad [1 ]
Savkin, Andrey V. [1 ]
机构
[1] Univ New S Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia
基金
澳大利亚研究理事会;
关键词
Adaptive filtering; networked systems; prediction; renewable energy; wind power; NEURAL-NETWORKS; ENERGY; GENERATION; SYSTEMS; MODELS; SPEED;
D O I
10.1109/TPWRS.2011.2160295
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a method to improve the short-term wind power prediction at a given turbine using information from numerical weather prediction and from multiple observation points, which correspond to the locations of nearby turbines at a particular wind farm site. The prediction of wind power is achieved in two stages; in the first stage wind speed is predicted using our proposed method. In the second stage, the wind speed to output power conversion is accomplished using power curve model. The proposed wind power prediction method is tested using real measurements and NWP data from one of the wind farm sites in Australia. The performance is compared with the persistence and Grey predictor model in terms of Mean Absolute Error and Root Mean Square Error.
引用
收藏
页码:579 / 586
页数:8
相关论文
共 50 条
  • [1] Discussion on "A Method for Short-Term Wind Power Prediction With Multiple Observation Points"
    Andreotti, Amedeo
    Carpinelli, Guido
    Proto, Daniela
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (02) : 1898 - 1898
  • [2] Closure to discussion on A method for short-term wind power prediction with multiple observation points
    School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia
    [J]. IEEE Trans Power Syst, 2013, 2 (1898-1899):
  • [3] Adaptive Filtering Based Short-Term Wind Power Prediction with Multiple Observation Points
    Khalid, Muhammad
    Savkin, Andrey V.
    [J]. 2009 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-3, 2009, : 1547 - 1552
  • [4] Advanced method for short-term wind power prediction with multiple observation points using extreme learning machines
    Mahmoud, Tawfek
    Dong, Zhao Yang
    Ma, Jin
    [J]. JOURNAL OF ENGINEERING-JOE, 2018, (01): : 29 - 38
  • [5] Development of Short-Term Prediction System for Wind Power Generation Based on Multiple Observation Points
    Khalid, Muhammad
    Savkin, Andrey V.
    [J]. SUSTAINABILITY IN ENERGY AND BUILDINGS, 2009, : 89 - 98
  • [6] Multisource Wind Speed Fusion Method for Short-Term Wind Power Prediction
    An, Jianqi
    Yin, Feng
    Wu, Min
    She, Jinhua
    Chen, Xin
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (09) : 5927 - 5937
  • [7] Short-term Wind Power Prediction Based on Soft Margin Multiple Kernel Learning Method
    Li, Jun
    Ma, Liancai
    [J]. Chinese Journal of Electrical Engineering, 2022, 8 (01): : 70 - 80
  • [8] A long short-term memory based wind power prediction method
    Huang, Yufeng
    Ding, Min
    Fang, Zhijian
    Wang, Qingyi
    Tan, Zhili
    Lil, Danyun
    [J]. 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 5927 - 5932
  • [9] Short-term Prediction Models for Wind Speed and Wind Power
    Bai, Guangxing
    Ding, Yanwu
    Yildirim, Mehmet Bayram
    Ding, Yan-Hong
    [J]. 2014 2ND INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI), 2014, : 180 - 185
  • [10] Short-term wind power prediction and error analysis
    Ma, Rui
    Wang, Lingling
    Hu, Shuju
    [J]. RENEWABLE ENERGY AND ENVIRONMENTAL TECHNOLOGY, PTS 1-6, 2014, 448-453 : 1851 - 1857