A Review of Wind Power Forecasting Models

被引:232
|
作者
Wang, Xiaochen [1 ]
Guo, Peng [1 ]
Huang, Xiaobin [1 ]
机构
[1] N China Elect Power Univ, Sch Control Sci & Engn, Beijing, Peoples R China
关键词
Wind power forecasting; models for wind prediction; physical approaches; statistical approaches; SHORT-TERM PREDICTION; NEURAL-NETWORKS; SPEED; OAXACA;
D O I
10.1016/j.egypro.2011.10.103
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
Rapid growth in wind power, as well as increase on wind generation, requires serious research in various fields. Because wind power is weather dependent, it is variable and intermittent over various time-scales. Thus accurate forecasting of wind power is recognized as a major contribution for reliable large-scale wind power integration. Wind power forecasting methods can be used to plan unit commitment, scheduling and dispatch by system operators, and maximize profit by electricity traders. In addition, a number of wind power models have been developed internationally, such as WPMS, WPPT, Prediktor, ARMINES, Previento, WPFS Ver1.0 etc. This paper provides a review on comparative analysis on the foremost forecasting models, associated with wind speed and power, based on physical methods, statistical methods, hybrid methods over different time-scales. Furthermore, this paper gives emphasis on the accuracy of these models and the source of major errors, thus problems and challenges associated with wind power prediction are presented. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of University of Electronic Science and Technology of China (UESTC)
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Wind speed and wind power forecasting models
    Lydia, M.
    Kumar, G. Edwin Prem
    Akash, R.
    [J]. ENERGY & ENVIRONMENT, 2024,
  • [2] Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models
    Wang, Jianzhou
    Song, Yiliao
    Liu, Feng
    Hou, Ru
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2016, 60 : 960 - 981
  • [3] A Review of Wind Power Forecasting & Prediction
    Yang Mao
    Wang Shaoshuai
    [J]. 2016 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), 2016,
  • [4] Review of wind power forecasting method
    Qian Z.
    Pei Y.
    Cao L.
    Wang J.
    Jing B.
    [J]. Qian, Zheng (qianzheng@buaa.edu.cn), 1600, Science Press (42): : 1047 - 1060
  • [5] Assessment of power curves in models of wind power forecasting
    de Aquino, Ronaldo R. B.
    de Albuquerque, Jonata C.
    Neto, Otoni Nobrega
    Lira, Milde M. S.
    Carvalho, Manoel A., Jr.
    Neto, Alcides Codeceira
    Ferreira, Aida A.
    [J]. 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 3915 - 3922
  • [6] Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research
    Bazionis, Ioannis K.
    Georgilakis, Pavlos S.
    [J]. ELECTRICITY, 2021, 2 (01): : 13 - 47
  • [7] Improved Wind Power Forecasting with ARIMA Models
    Hodge, Bri-Mathias
    Zeiler, Austin
    Brooks, Duncan
    Blau, Gary
    Pekny, Joseph
    Reklatis, Gintaras
    [J]. 21ST EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, 2011, 29 : 1789 - 1793
  • [8] Forecasting Models of Wind Power in Northeastern of Brazil
    de Aquino, Ronaldo R. B.
    Ludermir, Teresa B.
    Neto, Otoni Nobrega
    Ferreira, Aida A.
    Lira, Milde M. S.
    Carvalho, Manoel A., Jr.
    [J]. 2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2013,
  • [9] A review on the forecasting of wind speed and generated power
    Ma Lei
    Luan Shiyan
    Jiang Chuanwen
    Liu Hongling
    Zhang Yan
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2009, 13 (04): : 915 - 920
  • [10] A Review on Different Methods of Wind Power Forecasting
    Agarwal, Parnika
    Shukla, Prakeern
    Sahay, Kishan Bhushan
    [J]. 2018 6TH INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON), 2018,