Abstract
The shift from traditional energy systems to distributed systems of energy suppliers and consumers and the power volatileness in renewable energy imply the need for effective short-term prediction models. These machine learning models are based on measured sensor information. In practice, sensors might fail for several reasons. The prediction models cannot naturally cannot work properly with incomplete patterns. If the imputation method, which completes the missing data, is not appropriately chosen, a bias may be introduced. The objective of this work is to propose the k-nearest neighbor (kNN) regression as geo-imputation preprocessing step for pattern-label-based short-term wind prediction of spatio-temporal wind data sets. The approach is compared to three other methods. The evaluation is based on four turbines with neighbors of the NREL Western Wind Data Set and the values are missing uniformly distributed. The results show that kNN regression is the most superior method for imputation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Batista, G., Monard, M.C.: A study of k-nearest neighbour as an imputation method. In: HIS (2003)
Corchado, E., Wozniak, M., Abraham, A., de Carvalho, A.C.P.L.F., Snásel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)
Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., Feitosa, E.: A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews 12(6) (2008)
Jerez, J.M., Molina, I., García-Laencina, P.J., Alba, E., Ribelles, N., Martín, M., Franco, L.: Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artif. Intell. Med. 50(2) (October 2010)
Kramer, O., Gieseke, F.: Short-term wind energy forecasting using support vector regression. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślęzak, D. (eds.) SOCO 2011. AISC, vol. 87, pp. 271–280. Springer, Heidelberg (2011)
Little, R.J.A., Rubin, D.B.: Statistical analysis with missing data. John Wiley & Sons, Inc., New York (1986)
Mellit, A., Pavan, A., Benghanem, M.: Least squares support vector machine for short-term prediction of meteorological time series. Theoretical and Applied Climatology 111(1-2) (2013)
Milligan, M., Porter, K., DeMeo, E., Denholm, P., Holttinen, H., Kirby, B., Miller, N., Mills, A., O’Malley, M., Schuerger, M., Soder, L.: Wind power myths debunked. IEEE Power and Energy Magazine 7(6) (2009)
Mohandes, M., Halawani, T., Rehman, S., Hussain, A.A.: Support vector machines for wind speed prediction. Renewable Energy 29(6) (2004)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12 (2011)
Potter, C.W., Lew, D., McCaa, J., Cheng, S., Eichelberger, S., Grimit, E.: Creating the dataset for the western wind and solar integration study (u.s.a.). In: 7th International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Offshore Wind Farms (2008)
Treiber, N.A., Heinermann, J., Kramer, O.: Aggregation of features for wind energy prediction with support vector regression and nearest neighbors. In: European Conference on Machine Learning (ECML), Workshop Data Analytics for Renewable Energy Integration (DARE) (2013)
Wang, Y., Wu, D.L., Guo, C., Wu, Q.H., Qian, W.Z., Yang, J.: Short-term wind speed prediction using support vector regression. In: 2010 IEEE Power and Energy Society General Meeting (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Poloczek, J., Treiber, N.A., Kramer, O. (2014). KNN Regression as Geo-Imputation Method for Spatio-Temporal Wind Data. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-07995-0_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07994-3
Online ISBN: 978-3-319-07995-0
eBook Packages: EngineeringEngineering (R0)