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Wind Speed Prediction with Genetic Algorithm

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Advances in Intelligent Networking and Collaborative Systems (INCoS 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 8))

Abstract

Nowadays trends pay attention to used renewable energy sources, e.g. wind – wind energy or sun irradiance – solar energy, as a source of electrical power. This kind of energy sources are very unstable and inconstancy (nonstationary) over the time. The proper and accurate wind speed or sun irradiance prediction is necessary to control the power grid. This paper presents short time wind prediction algorithm with genetic column subset selection problem. It uses multiple weather data sources, genetics algorithm for features selection, and the prediction is done by a neural network. The genetic algorithm chooses the most important features for the prediction algorithm.

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References

  1. Zhao, P., Xia, J., Dai, Y., He, J.: Wind speed prediction using support vector regression. In: 2010 5th IEEE Conference on Industrial Electronics and Applications, pp. 882–886, June 2010

    Google Scholar 

  2. Bhaskar, K., Singh, S.: Awnn-assisted wind power forecasting using feed-forward neural network. IEEE Trans. Sustain. Energ. 3(2), 306–315 (2012)

    Article  Google Scholar 

  3. Liu, D., Niu, D., Wang, H., Fan, L.: Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew. Energy 62, 592–597 (2014)

    Article  Google Scholar 

  4. Azad, H., Mekhilef, S., Ganapathy, V.: Long-term wind speed forecasting and general pattern recognition using neural networks. IEEE Transact. Sustain. Energ. 5(2), 546–553 (2014)

    Article  Google Scholar 

  5. Wang, J., Zhang, W., Li, Y., Wang, J., Dang, Z.: Forecasting wind speed using empirical mode decomposition and elman neural network. Appl. Soft Comput. J. 23, 452–459 (2014)

    Article  Google Scholar 

  6. Wang, S., Zhang, N., Wu, L., Wang, Y.: Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew. Energy 94, 629–636 (2016)

    Article  Google Scholar 

  7. Meng, A., Ge, J., Yin, H., Chen, S.: Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Convers. Manage. 114, 75–88 (2016)

    Article  Google Scholar 

  8. Shi, J., Lee, W.J., Liu, Y., Yang, Y., Wang, P.: Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans. Ind. Appl. 48(3), 1064–1069 (2012)

    Article  Google Scholar 

  9. Xu, R., Chen, H., Sun, X.: Short-term photovoltaic power forecasting with weighted support vector machine. In: 2012 IEEE International Conference on Automation and Logistics, Zhengzhou, pp. 248–253 (2012). doi:10.1109/ICAL.2012.6308206

  10. Mandal, P., Madhira, S., Ul haque, A., Meng, J., Pineda, R.: Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques. Procedia Comput. Sci. 12, 332–337 (2012)

    Article  Google Scholar 

  11. Prokop, L., Misak, S., Snasel, V., Krmer, P., Platos, J.: Photovoltaic power plant power output prediction using fuzzy rules. Przeglad Elektrotechniczny 89(11), 77–80 (2013)

    Google Scholar 

  12. Zeng, J., Qiao, W.: Short-term solar power prediction using a support vector machine. Renew. Energ. 52, 118–127 (2013)

    Article  Google Scholar 

  13. Li, Z., Mahbobur Rahman, S., Vega, R., Dong, B.: A hierarchical approach using machine learning methods in solar photovoltaic energy production forecasting. Energies 9(1), 55 (2016)

    Article  Google Scholar 

  14. Dolara, A., Grimaccia, F., Leva, S., Mussetta, M., Ogliari, E.: A physical hybrid artificial neural network for short term forecasting of PV plant power output. Energies 8(2), 1138–1153 (2015)

    Article  Google Scholar 

  15. Teo, T.T., Logenthiran, T., Woo, W.L.: Forecasting of photovoltaic power using extreme learning machine. In: 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), Bangkok, pp. 1–6 (2015). doi:10.1109/ISGT-Asia.2015.7387113

  16. Kromer, P., Platos, J., Snasel, V.: Genetic algorithm for the column subset selection problem. In: 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems, pp. 16–22, July 2014

    Google Scholar 

  17. Boutsidis, C., Mahoney, M.W., Drineas, P.: An improved approximation algorithm for the column subset selection problem. In: Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2009, Philadelphia, PA, USA, pp. 968–977. Society for Industrial and Applied Mathematics (2009)

    Google Scholar 

  18. Friedberg, S.: Linear Algebra, 4th edn. Prentice-Hall Of India Pvt. Limited (2003)

    Google Scholar 

  19. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  20. Zurada, J.: Introduction to Artificial Neural Systems. West Publishing Co., St. Paul (1992)

    Google Scholar 

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Acknowledgment

This work was supported by the Czech Science Foundation under the grant no. GACR GJ16-25694Y and in part by Grant of SGS No. SP2017/85 VŠB - Technical University of Ostrava, Czech Republic.

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Authors and Affiliations

  1. FEECS, Department of Computer Science, VŠB - Technical University of Ostrava, 17. Listopadu 15/2172, 708 33, Ostrava - Poruba, Czech Republic

    Michal Prilepok

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  1. Michal Prilepok
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Correspondence to Michal Prilepok .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, Ontario, Canada

    Isaac Woungang

  3. School of Business, UNSW Australia, Canberra, Aust Capital Terr, Australia

    Omar Khadeer Hussain

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Prilepok, M. (2018). Wind Speed Prediction with Genetic Algorithm. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_29

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  • DOI: https://doi.org/10.1007/978-3-319-65636-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65635-9

  • Online ISBN: 978-3-319-65636-6

  • eBook Packages: EngineeringEngineering (R0)

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