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ANALYSIS OF THE ENVIRONMENT CHARACTERISTICS INFLUENCE ON WIND POWER WITH ARTIFICIAL NEURAL NETWORKS

Gheorghe Stăvărache

First published: 2019-06-20https://doi.org/10.5593/sgem2019/4.1/s17.006View metrics

Abstract

The paper presents an analysis methodology, based on artificial neural networks, for the influences of environment characteristics on generated wind power in Republic of Moldavia. The necessity to reduce the pollution, on one hand and the increasing energy consumption on other hand, leads to an extensive use of so called "renewable energy sources" between these the most targeted being wave and wind energy. For the countries without exits to seas or oceans - like Republic of Moldova is - the wind energy remain the only one source to exploit. In order to obtain an optimal transformation of wind power into electricity, influences of several factors must be analyzed, like: wind speed and terrain roughness over the areas of wind turbine mounting and the turbine height. Based on several years of meteorological recordings, needed data are available. Due to the rapid changes in the weather conditions and terrain aspect the estimation of wind power based on regular mathematical equations is difficult to obtain. The artificial neural networks, through the capacity to find relationships between presented input-output data, are useful tools for analyzing and optimizing the values involved into wind power transformation. In the present paper, the recorded values of wind speed, terrain roughness and turbine height were used for an artificial neural network model building with the goal to find which the most influencing factor is and to optimize the turbine height for a specified placement.

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Publication details

Title
ANALYSIS OF THE ENVIRONMENT CHARACTERISTICS INFLUENCE ON WIND POWER WITH ARTIFICIAL NEURAL NETWORKS
Authors
Gheorghe Stăvărache
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th International Multidisciplinary Scientific GeoConference SGEM2019, Energy and Clean Technologies
Publisher
STEF92 Technology
Year
2019
Pages
43-50
SWS Citekey
Stavarache2019174350
ISSN
1314-2704
ISBN
978-619-7408-83-6
Language
en
Publication type
Conference Paper
Keywords
References12
  1. Paipetis S. A., Ceccarelli M., The Genius of Archimedes 23 Centuries of Influence on Mathematics, Science and Engineering. Syracuse, Springer Netherlands, 2010.

  2. Price T., James Blyth, Britain's first modern wind power pioneer. Wind Engineering, 29(3), pp. 191-200, 2008.

  3. https://library.wwindea.org/global-statistics-2018-preliminary

  4. Planul National de Acțiuni privind Energia Regenerabila pentru anii 2013-2020. Monitorul Oficial, nr. 4-8, 10 Ianuarie, pp. 25-66, 2014

  5. T. Constantinov, M. Nedealcov,V. Raileanu, O. Crivova, R. Cojocari, G. Mleavaia. Digital Climatic Atlas of Republic of Moldova. Lucrarile Simpozionului Sisteme Informationale Geografice Editia a XVIII-a, Iasi, România. p. 6-13, 2010.

  6. Karlsson D., Wind Turbine Performance Monitoring using Artificial Neural Networks With a Multi-Dimensional Data Filtering Approach, Department of Energy and Environment Chalmers University of Technology, Gothenburg, Sweden, 2014.

  7. I.M. Ragheb, Wind Shear, Roughness Classes and Turbine Energy Production, University of Illinois, 2018

  8. Tesfaye A., Zhang J.H., Zheng D.H., Shiferaw D., Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids, International Journal of Science and Engineering Applications, vol. 5, issue 3, pp 144-151, 2016.

  9. Shekhawat A.S., Wind Power Forecasting using Artificial Neural Networks, Country, International Journal of Engineering & Technology, vol. 3, issue 4, pp 993-998, 2014.

  10. Beccali M., Cirrincione G., Marvuglia A., Serporta C., Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor, vol. 87, pp 884- 893, 2010.

  11. Ciortan S., Rusu E., Prediction of the wave power in the Black Sea based on wind speed using artificial neural networks, 3th International Conference on Advances on Clean Energy Research ICACER 2018, Spain, 2018.

  12. Xiaochen W., Peng G., Xiaobin H., A Review of Wind Power Forecasting Models, Energy Procedia 12, 770 – 778, 2011.

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Number of times cited according to Crossref: 1

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