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Title: ANALYSIS OF THE ENVIRONMENT CHARACTERISTICS INFLUENCE ON WIND POWER WITH ARTIFICIAL NEURAL NETWORKS
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ANALYSIS OF THE ENVIRONMENT CHARACTERISTICS INFLUENCE ON WIND POWER WITH ARTIFICIAL NEURAL NETWORKS
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1314-2704
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English
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19
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4.1
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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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conference
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19th International Multidisciplinary Scientific GeoConference SGEM 2019
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19th International Multidisciplinary Scientific GeoConference SGEM 2019, 30 June - 6 July, 2019
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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43-50
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30 June - 6 July, 2019
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website
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cdrom
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5811
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wind power; neural networks; analyze; optimize
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