A feature engineering approach to wind power forecasting
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DOI: 10.1016/j.ijforecast.2013.07.007
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Cited by:
- Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
- Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
- Gilbert, Ciaran & Browell, Jethro & McMillan, David, 2021. "Probabilistic access forecasting for improved offshore operations," International Journal of Forecasting, Elsevier, vol. 37(1), pages 134-150.
- Zhongrong Zhang & Yiliao Song & Feng Liu & Jinpeng Liu, 2016. "Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis," Sustainability, MDPI, vol. 8(2), pages 1-30, January.
- Bamooeifard, Alireza, 2020. "Future studies in Iran development plans for wind power, a system dynamics modeling approach," Renewable Energy, Elsevier, vol. 162(C), pages 1054-1064.
- Wang, Lin & Lv, Sheng-Xiang & Zeng, Yu-Rong, 2018. "Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China," Energy, Elsevier, vol. 155(C), pages 1013-1031.
- Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017.
"Predicting recessions with boosted regression trees,"
International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
- Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions With Boosted Regression Trees," Working Papers 2015-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
- Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
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Keywords
GEFCom; Feature engineering; Gradient boosted decision trees; Linear regression; Machine learning; Time series;All these keywords.
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