Among renewable energy sources wind energy is having an increasing influence on the supply of energy power. However wind energy is not a stationary power, depending on the fluctuations of the wind, so that is necessary to cope with these fluctuations that may cause problems the electricity grid stability. The ability to predict short-term wind speed and consequent production patterns becomes critical for the all the operators of wind energy. This paper studies several configurations of Artificial Neural Networks (ANN), a well-known tool able to estimate wind speed starting from measured data. The presented ANNs, t have been tested through data gathered in the area of Trapani (Sicily). Different models have been studied in order to determine the best architecture, minimizing statistical error. Simulation results show that the estimated values of wind speed are in good accord with the values measured by the anemometers.

Beccali, M., Culotta, S., Lo Brano, V. (2012). Short term wind speed prediction using Multi Layer Perceptron. In Proceedings of SDEWES 2012 - The 7th Conference on Sustainable Development of Energy, Water and Environment Systems.

Short term wind speed prediction using Multi Layer Perceptron

BECCALI, Marco;CULOTTA, Simona;LO BRANO, Valerio
2012-01-01

Abstract

Among renewable energy sources wind energy is having an increasing influence on the supply of energy power. However wind energy is not a stationary power, depending on the fluctuations of the wind, so that is necessary to cope with these fluctuations that may cause problems the electricity grid stability. The ability to predict short-term wind speed and consequent production patterns becomes critical for the all the operators of wind energy. This paper studies several configurations of Artificial Neural Networks (ANN), a well-known tool able to estimate wind speed starting from measured data. The presented ANNs, t have been tested through data gathered in the area of Trapani (Sicily). Different models have been studied in order to determine the best architecture, minimizing statistical error. Simulation results show that the estimated values of wind speed are in good accord with the values measured by the anemometers.
Settore ING-IND/11 - Fisica Tecnica Ambientale
2012
SDEWES 2012 - The 7th Conference on Sustainable Development of Energy, Water and Environment Systems
Ohrid, Macedonia
10
Beccali, M., Culotta, S., Lo Brano, V. (2012). Short term wind speed prediction using Multi Layer Perceptron. In Proceedings of SDEWES 2012 - The 7th Conference on Sustainable Development of Energy, Water and Environment Systems.
Proceedings (atti dei congressi)
Beccali, M; Culotta, S; Lo Brano, V
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/64318
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