Very-Short Term Wind Speed Forecasting Via Distance Algorithm in Machine Learning
عنوان مقاله: Very-Short Term Wind Speed Forecasting Via Distance Algorithm in Machine Learning
شناسه ملی مقاله: JR_MSEEE-2-3_003
منتشر شده در در سال 1401
شناسه ملی مقاله: JR_MSEEE-2-3_003
منتشر شده در در سال 1401
مشخصات نویسندگان مقاله:
Alireza Shaterzadeh Yazdi - Department of Electrical Engineering, Bahcesehir University, Istanbul, Turkey.
Cavit Fatih Kucuktezcan - Department of Electrical Engineering, Bahcesehir University, Istanbul, Turkey.
خلاصه مقاله:
Alireza Shaterzadeh Yazdi - Department of Electrical Engineering, Bahcesehir University, Istanbul, Turkey.
Cavit Fatih Kucuktezcan - Department of Electrical Engineering, Bahcesehir University, Istanbul, Turkey.
This paper proposes distance matrices, Euclidean, and offset translation methods in machine learning prediction of wind speed. The primary aim for this research is to design forecasting models for very short-term and short-term wind speed prediction based on these two methods by using historical data on wind speed. The test data is collected at a wind power station at ۱۰ minutes intervals. Furthermore, we evaluate the output in different time horizons in comparison to the benchmark method (persistence). To ensure the output results, comparing this method with the persistence method is essential. The proposed method performance was evaluated and compared with the conventional persistence method performance in terms of mean absolute error.
کلمات کلیدی: very short-term prediction, wind speed prediction, distance matrices, machine learning
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/2078876/