Machine Learning improves Forecast Accuracy for wind Power Generation Improvements in the Percent Range

被引：0
|
作者：
不详
机构：
来源：
BWK | 2011年 / 63卷 / 09期
关键词：
D O I：
暂无
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
引用
收藏
页码：36 / +
页数：2
相关论文
共 50 条
  • [21] Wind speed forecast model for wind farm based on a hybrid machine learning algorithm
    Ul Haque, Ashraf
    Mandal, Paras
    Meng, Julian
    Negnevitsky, Michael
    INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY, 2015, 34 (01) : 38 - 51
  • [22] Chance Constrained Extreme Learning Machine for Nonparametric Prediction Intervals of Wind Power Generation
    Wan, Can
    Zhao, Changfei
    Song, Yonghua
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2020, 35 (05) : 3869 - 3884
  • [23] Role of Machine Learning Algorithms for Wind Power Generation Prediction in Renewable Energy Management
    Anushalini, T.
    Revathi, B. Sri
    IETE JOURNAL OF RESEARCH, 2024, 70 (04) : 4319 - 4332
  • [24] Predicting wind power generation using machine learning and CNN-LSTM approaches
    Malakouti, Seyed Matin
    Ghiasi, Amir Rikhtehgar
    Ghavifekr, Amir Aminzadeh
    Emami, Parvin
    WIND ENGINEERING, 2022, 46 (06) : 1853 - 1869
  • [25] Performance Comparison of Different Machine Learning Algorithms on the Prediction of Wind Turbine Power Generation
    Eyecioglu, Onder
    Hangun, Batuhan
    Kayisli, Korhan
    Yesilbudak, Mehmet
    2019 8TH INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY RESEARCH AND APPLICATIONS (ICRERA 2019), 2019, : 922 - 926
  • [26] Effect Analysis of NWP Wind Speed at Different Height on the Improvement of Wind Power Forecast Accuracy
    Zhang, Fei
    2015 4TH INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENTAL PROTECTION (ICEEP 2015), 2015, : 3383 - 3387
  • [27] Improvements in the Current Brazil's Energy Dispatch Optimization: Load Forecast and Wind Power
    Telles Esteves, Gheisa Roberta
    Macaira, Paula Medina
    Cyrino Oliveira, Fernando Luiz
    Amador, Gustavo
    Souza, Reinaldo Castro
    ICORES: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON OPERATIONS RESEARCH AND ENTERPRISE SYSTEMS, 2019, : 398 - 405
  • [28] A contrastive learning-based framework for wind power forecast
    Zhu, Nanyang
    Dai, Zemei
    Wang, Ying
    Zhang, Kaifeng
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 230
  • [29] Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning
    Pombo, Daniel Vazquez
    Bindner, Henrik W.
    Spataru, Sergiu Viorel
    Sorensen, Poul Ejnar
    Bacher, Peder
    SENSORS, 2022, 22 (03)
  • [30] Wind Speed Forecast for the Stratospheric Airship by Incremental Extreme Learning Machine
    Xu Guangyu
    Shen Shaoping
    Sun Jietao
    PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, : 4088 - 4092
12345