NDLI: Weather division-based wind power forecasting model with feature selection

Content Provider	IET Digital Library
Author	Qin, Liang Xiong, Yindi Liu, Kaipei
Abstract	The sample division-based hybrid model is an enforceable approach to improve wind power forecasting accuracy in the short term. These models up to now prefer to keep the input same for all the individual schemes, which weaken the effort of division and restrict the further improvement of the accuracy. To this end, a weather division-based wind power forecasting model with ensemble feature selection is proposed for refinement. The methodology comprises three stages: the division of wind power associated weather based on hierarchical clustering with the DTW distance metric, ensemble feature selection framework considering both predictive accuracy and stability, and wind power prediction based on machine learning algorithms for each weather type. As a test case, the proposed methodology is applied to the data of a wind farm group in Northwest China. With respect to the single models, the proposed method has improved the predictive accuracy by up to 30% at three error metrics, and the weather associated features are discussed.
Starting Page	3050
Ending Page	3060
Page Count	11
ISSN	17521416
Volume Number	13
e-ISSN	17521424
Issue Number	Issue 16, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/13/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.0263
Journal	IET Renewable Power Generation
Publisher Date	2019-10-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Ensemble Feature Selection Framework Knowledge Engineering Technique Learning in AI Load Forecasting Power Engineering Computing Power System Planning And Layout Predictive Accuracy Sample Division-based Hybrid Model Statistics Weather Division-based Wind Power Forecasting Model Wind Power Wind Power Forecasting Accuracy Wind Power Plant Wind Power Prediction
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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