Abstract
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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Hittinger, E., Apt, J., Whitacre, J.F.: The effect of variability-mitigating market rules on the operation of wind power plants. Energy Syst. 5(4), 737–766 (2014)
Terciyanli, E., Demirci, T., Kucuk, D., Sarac, M., Cadirci, I., Ermis, M.: Enhanced nationwide wind-electric power monitoring and forecast system. IEEE Trans. Ind. Inf. 10(2), 1171–1184 (2014)
Ozkan, M.B., Karagoz, P.: A novel wind power forecast model: statistical hybrid wind power forecast technique (SHWIP). IEEE Trans. Ind. Inf. 11(2), 375–387 (2015)
Özkan, M.B., Küçük, D., Terciyanlı, E., Buhan, S., Demirci, T., Karagoz, P.: A data mining-based wind power forecasting method: results for wind power plants in turkey. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 268–276. Springer, Heidelberg (2013)
Tascikaraoglu, A., Uzunoglu, M.: A review of combined approaches for prediction of short-term wind speed and power. Renew. Sustain. Energy Rev. 34, 243–254 (2014)
Preede Revheim, P., Beyer, H.G.: Using bayes model averaging for wind power forecasts. In: EGU General Assembly Conference Abstracts, vol. 16, p. 2811 (2014)
Lyu, Q.C., Liu, W.Y., Zhu, D.D., Wang, W.Z., Han, X.S., Liu, F.C.: Wind power combination forecasting model based on drift. Adv. Mater. Res. 953, 522–528 (2014)
Ma, L., Li, B., Yang, Z.B., Du, J., Wang, J.: A new combination prediction model for short-term wind farm output power based on meteorological data collected by WSN. Int. J. Control Autom. 7, 171–180 (2014)
Turkish Meteorology-Office Web Site. http://www.mgm.gov.tr/
Global Forecast System (GFS) Web Site. http://www.emc.ncep.noaa.gov/index.php?branch=GFS
European Centre for Medium-Range Weather Forecasts (ECMWF) Web Site. http://www.ecmwf.int
Hall, P., Park, B.U., Samworth, R.J.: Choice of neighbor order in nearest-neighbor classification. Ann. Stat. 36(5), 2135–2152 (2008)
Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific Publication Co Inc. (2008). ISBN: 978-9812771711
Tew, C., Giraud-Carrier, C., Tanner, K., Burton, S.: Behavior-based clustering and analysis of interestingness measures for association rule mining. Data Min. Knowl. Discov. 28(4), 1004–1045 (2014)
Acknowledgment
This work is conducted in the scope of RITM (5122807) project of TÜBİTAK. We would like thank to all of the researchers who worked in implementation of the whole project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Koksoy, C.E. et al. (2015). Performance Analysis of Data Mining Techniques for Improving the Accuracy of Wind Power Forecast Combination. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2015. Lecture Notes in Computer Science(), vol 9518. Springer, Cham. https://doi.org/10.1007/978-3-319-27430-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-27430-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27429-4
Online ISBN: 978-3-319-27430-0
eBook Packages: Computer ScienceComputer Science (R0)