A New Combination Model for Offshore Wind Power Prediction Considering the Number of Climbing Features
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53401-0_31
Reference13 articles.
1. Zhang, H., Yan, J., Liu, Y., et al.: Multi-source and temporal attention network for probabilistic wind power prediction. IEEE Trans. Sustain. Energy 12, 2205–2218 (2021)
2. Ahmadi, A., Nabipour, M., Mohammadi-Ivatloo, B., et al.: Long-term wind power forecasting using tree-based learning algorithms. IEEE Access 8, 151511–151522 (2020)
3. Sun, Z., Zhao, M.: Short-term wind power forecasting based on VMD decomposition, ConvLSTM networks and error analysis. IEEE Access 8, 134422–134434 (2020)
4. Zou, F., Fu, W., Fang, P., et al.: A hybrid model based on multi-stage principal component extraction, GRU network and KELM for multi-step short-term wind speed forecasting. IEEE Access 8, 222931–222943 (2020)
5. Dong, X., Sun, Y., Li, Y.: Spatio-temporal convolutional network based power forecasting of multiple wind farms (2021)
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