Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 11, Issue 1, Pages 509-523Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2019.2897136
Keywords
Wind speed; Correlation; Time series analysis; Communication networks; Wind farms; Feature extraction; Wind turbines; Convolutional neural networks (CNN); deep learning; spatial and temporal correlations; wind speed prediction
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Funding
- National Key Research Program of China [2016YFB0900100]
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Leveraging both temporal and spatial correlations to predict wind speed remains one of the most challenging and less studied areas of wind speed prediction. In this paper, the problem of predicting wind speeds for multiple sites is investigated by using the spatio-temporal correlation. We proposed a deep architecture termed predictive spatio-temporal network (PSTN), which is a unified framework integrating a convolutional neural network (CNN) and a long short-term memory (LSTM). Initially, the spatial features are extracted from the spatial wind speed matrices by the CNN at the bottom of the model. Then, the LSTM captures the temporal dependencies among the spatial features extracted from contiguous time points. Finally, the predicted wind speeds are given by the last state of the top layer of the LSTM, which are generated by using the spatial features and temporal dependencies. Though composed of two kinds of architectures, PSTN is trained with one loss function in an end-to-end manner, which can learn temporal and spatial correlations jointly. Experiments for short-term predictions are conducted on real-world data, whose results demonstrate that PSTN outperforms prior methods.
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