Forecasting of Wind Speed using Feature Selection and Neural Networks

Senthil Kumar Paramasivan, Daphne Lopez

Abstract


Wind energy is rapidly increasing and it is becoming a significant contributor to the electricity grid. Wind speed is an important factor in wind power production and integration. This paper presents a wind speed forecasting using feature selection method and bagging neural network.  Feature selection plays an essential role in the machine learning environment and especially in the prediction task. The ReliefF feature selection method is used for identification of necessary features for wind speed forecast and reduces the complexity of the model. A detailed investigation is to forecast wind speed with meteorological time series data as input variable using a bagging neural network. Performance is evaluated in terms of mean square error when using the feature selection method with the bagging neural network.


Keywords


Bagging; Feature selection; Forecasting; Neural network; ReliefF; Wind speed

Full Text:

PDF

References


Jaesung jung, Robert P.Broadwater, “Current status and future for wind speed and power forecastingâ€, Renewable and Sustainable Energy Reviews Vol .31,pp. 762-777, 2014.

Monterio, R.Beesa, V.Miranda, A.Botterud,,J.Wang and G.Conzelmarn, “Wind Power Forecasting: State-of-the Art 2009â€, Argonne National Laboratory, Nov 2009.

Melam Bhaskar, Amit Jain, N. Venkata srinath, “Wind Speed Forecasting: Present Statusâ€, International Conference on Power Technology, 2010.

Mohammad Monfared, Hasan Rastegar, Hossein Madadi Kojabadi,â€A new strategy for wind speed forecasting using artificial intelligent methodsâ€, Renewable Energy, Vol.34, pp.845–848, 2009.

Jujie Wang , Wenyu Zhang, Jianzhou Wan, Tingting Han, Lingbin Kong,†A novel hybrid approach for wind speed predictionâ€, Information Sciences Vol. 273, pp. 304–318, 2014.

Xiaobing Kong, XiangjieLiu a,n, RuifengShi, KwangY.Lee, “Wind speed prediction using reduced support vector machines with feature selectionâ€, Neurocomputing, Vol.169,pp. 449–456, 2015.

Richard Jensen, Qiang Shen, “ Computational intelligence and feature selectionâ€, IEEE press series on computational intelligence, A John Wiley & Sons, Inc., Publication.(2008).

Kira, K., Rendell, L.A., “A practical approach to feature selectionâ€, Proceedings of the Ninth International Conference on Machine Learning. pp. 249–256, (1992).

Selwyn Piramuthu, “Evaluating feature selection methods for learning in data mining applicationsâ€, European Journal of Operational Research 156 (2004) 483–494.

Girish Chandrashekar and Ferat Sahin, A survey on feature selection methods, computer and Electrical Engineering Vol.40, pp. 16-28,2014.

Vipin kumar and sonajharia minz, feature selection: A literature review, Smart computing Review, vol 4, No 3, pp.211-229, 2014.

Pramod Kumar Singh , Dimension Reduction Methods in High Dimensional Data Mining, Available: www.sau.ac.in/~mlta/studymaterial/22-12-2013/PKSingh.pdf

Marko Robnik-ˇSikonja and Igor Kononenko , “Theoretical and Empirical Analysis of ReliefF and RReliefFâ€, Machine Learning Journal Vol. 53, pp.:23-69, 2003.

Liuzhi Yin, Yong Ge, Keli Xiao, Xuehua Wang , Xiaojun Quan , Feature selection for high-dimensional imbalanced data, Vol.105, pp3-11, 2013.

S.N. Sivanandam and S.N. Deepa, “Principle of Soft computingâ€, Wiley India (P)Ltd,2008.

Rasit Ata, “Artificial neural networks applications in wind energy systems: a reviewâ€, Renewable and Sustainable Energy Reviews , Vol.49,pp.534–562, 2015.

Zhi-Hua Zhou, “Ensemble Methods Foundations and Algorithmsâ€, CRC Press Taylor & Francis Group 2012.

Chih-Fong Tsai,Yu-Feng Hsu,David C.Yen, A comparative study of classfier ensembles for bankruptcy prediction, Vol.24,pp977-984, 2014.

A.S. Khwaja, M. Naeem, A. Anpalagan, A. Venetsanopoulos, B. Venkatesh, “Improved short-term load forecasting using bagged neural networksâ€, Electric Power Systems Research, Vol. 125,pp. 109–115, 2015.

E. Shafiei1, H. Jazayeri-Rad, Improving the Identification Performance of an Industrial Process Using Multiple Neural Networks, American Journal of Intelligent Systems, Vol. 2, Issue 4, pp. 40-44, 2012.

Huan Long,ZZijun Zhang Yan Su, Analysis of daily solar power prediction with data-driven approaches, Applied Energy, Vol.126,pp29-37, 2014.

ReliefF algorithm, http://users.cis.fiu.edu/~yzhan004/Algorithm/reliefF.html.

Marko Robnik-Sikonja, Igor Kononenko, Theoretical and Empirical Analysis of ReliefF and RReliefF, Machine Learning Journal (2003)53:23-69.




DOI (PDF): https://doi.org/10.20508/ijrer.v6i3.3855.g6866

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2025; 

h=35,

Average citation per item=6.59

Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43

Category Quartile:Q4