Short-term wind speed prediction at Bogdanci power plant in FYROM using an artificial neural network

被引：8
|
作者：
Jha, Sunil Kr. [1 ,2 ]
Bilalovikj, Jasmin [3 ]
机构：
[1] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam
[2] Ton Duc Thang Univ, Fac Sci Appl, Ho Chi Minh City, Vietnam
[3] Univ Informat Sci & Technol St Paul Apostle, Fac Comp Sci & Engn, Ohrid, North Macedonia
来源：
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY | 2019年 / 38卷 / 06期
关键词：
Renewable energy; artificial neural networks; wind speed prediction; wind energy; CORAL-REEFS OPTIMIZATION; RENEWABLE ENERGY; DECOMPOSITION; ALGORITHM; MODELS; BANKS;
D O I：
10.1080/14786451.2018.1516668
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The present study targets short-term wind speed prediction of the wind turbine station at Bogdanci in the Former Yugoslav Republic of Macedonia (FYROM), using artificial neural network (ANN) method. Wind directions and meteorological parameters (temperature, pressure, and humidity) measured at the interval of 10 min in between May 2015 and September 2015 have been used as the input of ANN to predict four kinds of wind speed (rotation mean, hub mean, tip low mean, and base mean). The best performance (R-2 = 0.84 - 0.86) of ANN method was achieved using wind direction base mean (WDBM) in September 2015, and using temperature (R-2 = 0.77 - 0.80) in May 2015. Reasonable performance of ANN method was achieved in the rest of the month.
引用
收藏
页码：526 / 541
页数：16
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