کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
302047 512526 2009 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
پیش نمایش صفحه اول مقاله
Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction
چکیده انگلیسی

This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Renewable Energy - Volume 34, Issue 6, June 2009, Pages 1451–1457
نویسندگان
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