An extreme learning machine based very short-term wind power forecasting method for complex terrain

被引：0
|
作者：
Acikgoz, Hakan [1 ]
Yildiz, Ceyhun [2 ]
Sekkeli, Mustafa [3 ]
机构：
[1] Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineering, Gaziantep Islam Science and Technology University, Gaziantep, Turkey
[2] Vocational School of Elbistan, Department of Electricity and Energy, Kahramanmaraş İstiklal University, Kahramanmaraş, Turkey
[3] Faculty of Engineering and Architecture, Department of Electrical and Electronics Engineering, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Turkey
来源：
Energy Sources, Part A: Recovery, Utilization and Environmental Effects | 2020年 / 42卷 / 22期
关键词：
Global positioning system - Machine learning - Mean square error - Wind speed - Application programs - Neural networks - Complex networks - Landforms - Knowledge acquisition - Weather forecasting;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this study, wind power forecasting is performed for a Wind Power Plant (WPP) with an installed capacity of 135 MW in Turkey. The ruggedness index (RIX) of the terrain where WPP was installed is analyzed with Wind Atlas Analysis and Application Program (WAsP). According to the obtained RIX value, the terrain of WPP is found to be complex. Due to the complexity of the terrain, wind power forecasting becomes difficult. To deal with this problem, a forecasting method with fast, accurate, and high performance is needed. Therefore, Extreme Learning Machine (ELM) based method is proposed for wind power forecasting in this study. Electrical and meteorological measurements are obtained from WPP for the application of the proposed method. These measurements are provided with high quality measuring devices. Also, Global Positioning System (GPS) time synchronization is used to prevent lags between measurements. The wind speed, wind direction, and wind power data of 1-year period are obtained from WPP. These data are used to compare the proposed method with a classical Artificial Neural Network (ANN) based method in terms of two, three and four hours-ahead wind power forecast performances. In the forecast studies performed for all data related to 2, 3, and 4-hours ahead, Normalized Root Mean Square Error (NRMSE) values of ELM are obtained as 7.01, 10.12, and 12.06, respectively, while these values are found as 8.19, 12.18, and 13.09 for ANN. In addition, the values of Correlation Coefficients (R) of the proposed forecast method results regarding 2, 3, and 4-hours ahead are 0.96588, 0.93528, and 0.88984, respectively. The R values related to ANN are observed as 0.95421, 0.91373, and 0.87576, respectively. According to the obtained results, it is observed that ELM has better performance features than classic method under all forecast conditions and it is clearly seen that ELM has by far short training time than other one. © 2020 Taylor & Francis Group, LLC.
引用
收藏
页码：2715 / 2730
相关论文
共 50 条
  • [1] An extreme learning machine based very short-term wind power forecasting method for complex terrain
    Acikgoz, Hakan
    Yildiz, Ceyhun
    Sekkeli, Mustafa
    ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2020, 42 (22) : 2715 - 2730
  • [2] Short-term wind power forecasting method based on a causal regularized extreme learning machine
    Yang M.
    Zhang S.
    Wang B.
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2024, 52 (11): : 127 - 136
  • [3] Short-Term Solar Power Forecasting Based on CEEMDAN and Kernel Extreme Learning Machine
    Gun, Ali Riza
    Dokur, Emrah
    Yuzgec, Ugur
    Kurban, Mehmet
    ELEKTRONIKA IR ELEKTROTECHNIKA, 2023, 29 (02) : 28 - 34
  • [4] Short-Term Wind Power Forecasting by Advanced Machine Learning Models
    Li, Yun-Lun
    Zhu, Zheng-An
    Chang, Yun-Kai
    Chiang, Chen-Kuo
    2020 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C 2020), 2021, : 412 - 415
  • [5] Short-term load forecasting method based on ensemble improved extreme learning machine
    Cheng, Song
    Yan, Jianwei
    Zhao, Dengfu
    Wang, Quan
    Wang, Haiming
    Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2009, 43 (02): : 106 - 110
  • [6] Short-term wind power prediction based on extreme learning machine with error correction
    Zhi Li
    Lin Ye
    Yongning Zhao
    Xuri Song
    Jingzhu Teng
    Jingxin Jin
    Protection and Control of Modern Power Systems, 2016, 1 (1)
  • [7] Short-term wind power prediction based on extreme learning machine with error correction
    Li, Zhi
    Ye, Lin
    Zhao, Yongning
    Song, Xuri
    Teng, Jingzhu
    Jin, Jingxin
    PROTECTION AND CONTROL OF MODERN POWER SYSTEMS, 2016, 1 (01)
  • [8] Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting
    Tang, Pingzhou
    Chen, Di
    Hou, Yushuo
    CHAOS SOLITONS & FRACTALS, 2016, 89 : 243 - 248
  • [9] Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine
    An, Guoqing
    Jiang, Ziyao
    Chen, Libo
    Cao, Xin
    Li, Zheng
    Zhao, Yuyang
    Sun, Hexu
    SUSTAINABILITY, 2021, 13 (18)
  • [10] Very short-term probabilistic forecasting of wind power based on OKDE
    Wang, Sen
    Sun, Yonghui
    Chen, Li
    Wu, Pengpeng
    Zhou, Wei
    Yuan, Chang
    2022 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2022, : 1108 - 1112
12345