Transfer learning for short-term wind speed prediction with deep neural networks

As a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical parad...

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Veröffentlicht in:Renewable energy Jg. 85; S. 83 - 95
Hauptverfasser: Hu, Qinghua, Zhang, Rujia, Zhou, Yucan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.01.2016
Schlagworte:
Deep neural networks
learning
neural networks
prediction
Stacked denoising autoencoder
Transfer learning
uncertainty
weather forecasting
wind farms
wind power
wind speed
Wind speed prediction
Wind speed prediction
Deep neural networks
Transfer learning
Stacked denoising autoencoder
ISSN:0960-1481, 1879-0682
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Zusammenfassung:As a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical paradigms. NWP is usually unavailable or spatially insufficient. Data-driven modeling is an effective candidate. As to some newly-built wind farms, sufficient historical data is not available for training an accurate model, while some older wind farms may have long-term wind speed records. A question arises regarding whether the prediction model trained by data coming from older farms is also effective for a newly-built farm. In this paper, we propose an interesting trial of transferring the information obtained from data-rich farms to a newly-built farm. It is well known that deep learning can extract a high-level representation of raw data. We introduce deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and then finely tune the mapping with data coming from newly-built farms. In this way, the trained network transfers information from one farm to another. The experimental results show that prediction errors are significantly reduced using the proposed technique. •An interesting trial is proposed for transferring information from data-rich farms to a newly-built farm.•We construct a transfer learning framework for wind speed prediction based on deep neural networks.•A collection of experiments are described for showing the effectiveness of the proposed technique.
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ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2015.06.034