کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8122795 | 1522385 | 2018 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system
ترجمه فارسی عنوان
پیش بینی سرعت باد و جهت باد با استفاده از شبکه عصبی مصنوعی، رگرسیون بردار پشتیبانی و سیستم استنتاج فازی سازگار
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
چکیده انگلیسی
In this study, three models of machine learning algorithms are implemented to predict wind speed, wind direction and output power of a wind turbine. The first model is multilayer feed-forward neural network (MLFFNN) that is trained with different data training algorithms. The second model is support vector regression with a radial basis function (SVR-RBF). The third model is adaptive neuro-fuzzy inference system (ANFIS) that is optimized with a partial swarm optimization algorithm (ANFIS-PSO). Temperature, pressure, relative humidity and local time are considered as input variables of the models. A large set of wind speed and wind direction data measured at 5-min, 10-min, 30-min and 1-h intervals are utilized to accurately predict wind speed and its direction for Bushehr. Energy and exergy analysis of wind energy for a wind turbine (E-44, 900â¯kW) is done. Also, the developed models are used to predict the output power of the wind turbine. Comparison of the statistical indices for the predicted and actual data indicate that the SVR-RBF model outperforms the MLFFNN and ANFIS-PSO models. Also, the current energy and exergy analysis presents an average of 32% energy efficiency and approximately 25% exergy efficiency of the wind turbine in the study region.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Sustainable Energy Technologies and Assessments - Volume 25, February 2018, Pages 146-160
Journal: Sustainable Energy Technologies and Assessments - Volume 25, February 2018, Pages 146-160
نویسندگان
A. Khosravi, R.N.N. Koury, L. Machado, J.J.G. Pabon,