Prediction of Short Term Power Output of Wind Farms based on Least Squares Method

被引:0
|
作者
Dutta, S. [1 ]
Overbye, T. J. [1 ]
机构
[1] Univ Illinois, Dept Elect & Comp Engn, Urbana, IL 61801 USA
关键词
cross correlation; prediction of power output; varying time lag cross correlation; wind power forecasting; SPEED PREDICTION; GENERATION;
D O I
暂无
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
Ability to reasonably predict the power outputs of wind farms enables a better management of reserves and control mechanisms of turbines. Wind power forecasting is also important in assessing the wind energy potential of a region at the planning stages. This paper focuses on the prediction of power generation from wind farms in the short term. It proposes a least squares based method of predicting the total power output of a group of wind farms distributed over a region. A test study has been conducted by predicting the wind power output of four hypothetical wind farms located at four counties in the state of Illinois, the Bureau County, Henry County, Mercer County and Knox County. The wind speeds at the latter three counties predicted from the measured wind speeds at the former are compared to the actual measured wind speeds. Results validate the effectiveness and accuracy of the proposed method for predicting the power output of wind farms in the short term. Comparison with the Persistence Model shows that the proposed model yields superior short term wind speed predictions.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Short-term Wind Power Interval Prediction Based on Wind Speed of Numerical Weather Prediction and Monte Carlo Method
    Yang, Mao
    Dong, Hao
    [J]. Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2021, 45 (05): : 79 - 85
  • [32] Short-term wind speed prediction in wind farms based on banks of support vector machines
    Ortiz-Garcia, Emilio G.
    Salcedo-Sanz, Sancho
    Perez-Bellido, Angel M.
    Gascon-Moreno, Jorge
    Portilla-Figueras, Jose A.
    Prieto, Luis
    [J]. WIND ENERGY, 2011, 14 (02) : 193 - 207
  • [33] Short-term Wind Power Prediction Method Based on Dynamic Wind Power Weather Division of Time Sequence Data
    Xiong, Yindi
    Liu, Kaipei
    Qin, Liang
    Ouyang, Tinghui
    He, Jiayi
    [J]. Dianwang Jishu/Power System Technology, 2019, 43 (09): : 3353 - 3359
  • [34] Ultra-Short-Term Multistep Prediction of Wind Power Based on Representative Unit Method
    Yang, Mao
    Liu, Lei
    Cui, Yang
    Su, Xin
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2018, 2018
  • [35] Short-Term Wind Power Prediction Method Based on Combination of Meteorological Features and CatBoost
    MOU Xingyu
    CHEN Hui
    ZHANG Xinjing
    XU Xin
    YU Qingbo
    LI Yunfeng
    [J]. Wuhan University Journal of Natural Sciences, 2023, 28 (02) : 169 - 176
  • [36] Calculation model and method of output power loss of wind farms and wind turbines
    Mei, Huawei
    Mi, Zengqiang
    Bai, Junliang
    [J]. Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2014, 38 (02): : 12 - 16
  • [37] A novel ultra-short-term wind power prediction method based on XA mechanism
    Peng, Cheng
    Zhang, Yiqin
    Zhang, Bowen
    Song, Dan
    Lyu, Yi
    Tsoi, Ahchung
    [J]. APPLIED ENERGY, 2023, 351
  • [38] Prediction of ultra-short-term wind power based on BBO-KELM method
    Li, Jun
    Li, Meng
    [J]. JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2019, 11 (05)
  • [39] An Ultra-Short-Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion
    Pi, Yuzhen
    Yuan, Quande
    Zhang, Zhenming
    Wen, Jingya
    Kou, Lei
    [J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2024,
  • [40] A Multichannel-Based CNN and GRU Method for Short-Term Wind Power Prediction
    Gao, Jian
    Ye, Xi
    Lei, Xia
    Huang, Bohao
    Wang, Xi
    Wang, Lili
    [J]. ELECTRONICS, 2023, 12 (21)