期刊
IET RENEWABLE POWER GENERATION
卷 13, 期 12, 页码 2062-2069出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-rpg.2018.5917
关键词
learning (artificial intelligence); weather forecasting; wind power; wind power plants; feature extraction; neural nets; pattern clustering; power engineering computing; feature selection; data-driven wind speed forecasting; deep feature extraction; high-efficiency utilisation; wind energy; management; grid-connected power systems; instability; irregularity; atmosphere system; raw historical data; deep novel feature extraction approach; numerical weather prediction data; Wind Atlas; forecasting accuracy; feature selection; proper feature extraction; LSTM neural networks; long short-term memory neural networks
资金
- National Natural Science Foundation of China [11771393, 11632015]
- Zhejiang Natural Science Foundation [LZ14A010002]
Wind speed forecasting is important for high-efficiency utilisation of wind energy and management of grid-connected power systems. Due to the noise, instability and irregularity of atmosphere system, the current models based on raw historical data have encountered many problems. In this study, a deep novel feature extraction approach is developed based on stacked denoising autoencoders and batch normalisation. Then the deep features extracted from raw historical data are fed to long short-term memory (LSTM) neural networks for prediction. Meanwhile, density-based spatial clustering of applications with noise is employed to process the numerical weather prediction data. By picking out the abnormal samples, the representative training samples are selected to improve the efficiency of the model. For illustration and verification purposes, the proposed model is used to predict the wind speed of Wind Atlas for South Africa (WASA). Empirical results show that deep feature extraction can improve the forecasting accuracy of LSTM 49% than feature selection, indicating that proper feature extraction is crucial to wind speed forecasting. And the proposed model outperforms other benchmark methods at least 17%. Hence, the proposed model is promising for wind speed forecasting.
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