Wind Power Prediction based on Recurrent Neural Network with Long Short-Term Memory Units

被引：0

作者：

Dong, Danting ^{[1
]}

Sheng, Zhihao ^{[1
]}

Yang, Tiancheng ^{[2
]}

机构：

[1] Univ Toronto, Comp Engn, Toronto, ON, Canada

[2] Univ Waterloo, Comp Sci, Waterloo, ON, Canada

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY AND POWER ENGINEERING (REPE 2018) | 2018年

关键词：

component; wind power; renewable energy; prediction model; deep learning; recurrent neural network; long short-term memory; MODEL;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Wind power is one of the most promising renewable energy sources, it is clean, safe and inexhaustible. However, predicting wind signal has always been challenging because the time series data is nonlinear, non-stationary and chaotic. In this paper, we provide a novel predicting framework including a recurrent neural network (RNN) structure model with long-short term memory (LSTM) units and an effective forecasting map adapted to different prediction horizons. We compare our new approach with concurrent methods and show that our new method is more effective in predicting wind power.

引用

页码：34 / 38

页数：5

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