Performance Analysis of Data Mining Techniques for Improving the Accuracy of Wind Power Forecast Combination

Date

2015

Author

Koksoy, Ceyda Er
Ozkan, Mehmet Baris
Küçük, Dilek
Bestil, Abdullah
Sonmez, Sena
Buhan, Serkan
Demirci, Turan
Karagöz, Pınar
Birturk, Aysenur

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Efficient integration of renewable energy sources into the electricity grid has become one of the challenging problems in recent years. This issue is more critical especially for unstable energy sources such as wind. The focus of this work is the performance analysis of several alternative wind forecast combination models in comparison to the current forecast combination module of the wind power monitoring and forecast system of Turkey, developed within the course of the RITM project. These accuracy improvement studies are within the scope of data mining approaches, Association Rule Mining (ARM), Distance-based approach, Decision Trees and k-Nearest Neighbor (k-NN) classification algorithms and comparative results of the algorithms are presented.

Subject Keywords

Wind power forecasting, Association rule mining, K-nearest neighbor, Decision tree

URI

https://hdl.handle.net/11511/28356

Journal

Data Analytics for Renewable Energy Integration

DOI

https://doi.org/10.1007/978-3-319-27430-0_4

Collections

Department of Computer Engineering, Article

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Citation Formats

C. E. Koksoy et al., “Performance Analysis of Data Mining Techniques for Improving the Accuracy of Wind Power Forecast Combination,” Data Analytics for Renewable Energy Integration, pp. 44–55, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/28356.