Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models

被引:188
|
作者
Madsen, Henrik [1 ]
Pinson, Pierre [2 ]
Kariniotakis, George [2 ]
Nielsen, Henrik [1 ]
Nielsen, Torben [1 ]
机构
[1] Tech Univ Denmark, Informat & Math Modelling, DK-2800 Lyngby, Denmark
[2] Ecole Mines Paris, Ctr Energy & Proc, F-06904 Sophia Antipolis, France
关键词
Wind power forecasting; prediction error; performance evaluation; evaluation protocol;
D O I
10.1260/030952405776234599
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term wind-power prediction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated, using results from both on-shore and offshore wind farms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems.
引用
收藏
页码:475 / 489
页数:15
相关论文
共 50 条
  • [1] 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
  • [2] Error Evaluation of Short-Term Wind Power Forecasting Models
    Singh, Upma
    Rizwan, M.
    [J]. INVENTIVE COMPUTATION AND INFORMATION TECHNOLOGIES, ICICIT 2021, 2022, 336 : 541 - 559
  • [3] Performance Comparison of Models for Fast Short-term Wind Speed Prediction
    Mao, Meiqin
    Chen, Shilong
    Cao, Yu
    Zhao, Yongchao
    Chang, Liuchen
    [J]. 2013 4TH IEEE INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS FOR DISTRIBUTED GENERATION SYSTEMS (PEDG), 2013,
  • [4] Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models
    Zhang, Chi
    Zeng, Jie
    Xie, Ning
    Yang, Ping
    Zhang, Yujia
    Zhang, Zhen
    [J]. 2016 3RD INTERNATIONAL CONFERENCE ON MANUFACTURING AND INDUSTRIAL TECHNOLOGIES, 2016, 70
  • [5] 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
  • [6] A Hybrid Algorithm for Short-Term Wind Power Prediction
    Xiong, Zhenhua
    Chen, Yan
    Ban, Guihua
    Zhuo, Yixin
    Huang, Kui
    [J]. ENERGIES, 2022, 15 (19)
  • [7] Short-term prediction of wind power with a clustering approach
    Kusiak, Andrew
    Li, Wenyan
    [J]. RENEWABLE ENERGY, 2010, 35 (10) : 2362 - 2369
  • [8] Short-Term Prediction of Wind Power Based on Deep Long Short-Term Memory
    Qu Xiaoyun
    Kang Xiaoning
    Zhang Chao
    Jiang Shuai
    Ma Xiuda
    [J]. 2016 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2016, : 1148 - 1152
  • [9] Short-term wind power prediction based on combined long short-term memory
    Zhao, Yuyang
    Li, Lincong
    Guo, Yingjun
    Shi, Boming
    Sun, Hexu
    [J]. IET GENERATION TRANSMISSION & DISTRIBUTION, 2024, 18 (05) : 931 - 940
  • [10] A review on short-term and ultra-short-term wind power prediction
    Xue, Yusheng
    Yu, Chen
    Zhao, Junhua
    Li, Kang
    Liu, Xueqin
    Wu, Qiuwei
    Yang, Guangya
    [J]. Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2015, 39 (06): : 141 - 151