Application of recurrent neural network to long-term-ahead generating power forecasting for wind power generator

被引:47
作者
Senjyu, Tomonobu [1 ]
Yona, Atsushi [1 ]
Urasaki, Naomitsu [1 ]
Funabashi, Toshihisa [2 ]
机构
[1] Univ Ryukyus, Fac Engn, Dept Elect & Elect Engn, Okinawa, Japan
[2] Meidensha Corp, Tokyo, Japan
来源
2006 IEEE/PES POWER SYSTEMS CONFERENCE AND EXPOSITION. VOLS 1-5 | 2006年
关键词
neural network; long-term-ahead forecasting; wind power generation; wind speed forecasting;
D O I
10.1109/PSCE.2006.296487
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 [动力工程及工程热物理]; 0820 [石油与天然气工程];
摘要
In recent years, there have been problems such as environmental pollution resulting from consumption of fossil fuel, e.g., coal and oil. Thus, introduction of an alternative energy source such as wind energy is expected. Wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to predict the power output for wind power generators as accurate as possible, it requires the method of wind speed estimation. In this paper, a technique consider the wind speed of each month, and confirm the validity of Neural Network (NN) to predict wind speed by computer simulations. Since Recurrent Neural Network (RNN) is known as good tool for time-series data forecasting, the authors propose an application of RNN for the wind speed prediction. The proposed method in this paper does not require complicated calculations and mathematical model.
引用
收藏
页码:1260 / +
页数:2
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