Forecast of Infrequent Wind Power Ramps Based on Data Sampling Strategy

Yuka Takahashi*, Yu Fujimoto, Yasuhiro Hayashi


研究成果: Conference article査読

11 被引用数 (Scopus)


The introduction of wind power generation has been promoted in Japan. However, wind power is an unstable power source because its output varies according to the weather. Particularly, sudden changes in output, which could adversely affect the power system, are called ramps and may cause serious problems in the power system. In this paper, the authors discuss the ramp event forecast by using classifiers. A serious issue in this setup is that classification based forecast tends to be inaccurate since the occurrence of such a ramp is relatively rare. This problem is called the class imbalance problem in the machine learning field. To overcome the class imbalance problem in ramp forecast, several data sampling approaches are implemented. The effectiveness of these sampling approaches is experimentally evaluated by using a real-world wind power generation dataset. The results show that the implemented approaches drastically improved the forecast accuracy.

ジャーナルEnergy Procedia
出版ステータスPublished - 2017
イベント11th International Renewable Energy Storage Conference, IRES 2017 - Dusseldorf, Germany
継続期間: 2017 3月 142017 3月 16

ASJC Scopus subject areas

  • エネルギー一般


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