Vertical extrapolation of wind speed using artificial neural network hybrid system
作者: Md. Saiful IslamMohamed MohandesShafiqur Rehman
作者单位: 1King Fahd University of Petroleum and Minerals
刊名: Neural Computing and Applications, 2017, Vol.28 (8), pp.2351-2361
来源数据库: Springer Journal
DOI: 10.1007/s00521-016-2373-x
关键词: Wind profileVertical extrapolation of windGenetic algorithmParticle swarm optimizationArtificial neural networkHybrid machine learning
英文摘要: Different approaches have been used for the extrapolation of wind speed to the turbine hub height which are mainly based on logarithmic law, power law and various modifications of the two. This paper proposes two artificial neural network (ANN) hybrid system-based models using genetic algorithm and particle swarm optimization, namely GA-NN and PSO-NN for vertical extrapolation of wind speed. These models are very simple in a sense that they do not require any parametric estimation like wind shear coefficient, roughness length or atmospheric stability. Rather they use available measured wind speeds at 10, 20 and 30 m heights to estimate wind speed at higher heights up to 100 m. Proposed methods have been compared with ANN, power law and logarithmic law. Daily, monthly and yearly average...
原始语种摘要: Different approaches have been used for the extrapolation of wind speed to the turbine hub height which are mainly based on logarithmic law, power law and various modifications of the two. This paper proposes two artificial neural network (ANN) hybrid system-based models using genetic algorithm and particle swarm optimization, namely GA-NN and PSO-NN for vertical extrapolation of wind speed. These models are very simple in a sense that they do not require any parametric estimation like wind shear coefficient, roughness length or atmospheric stability. Rather they use available measured wind speeds at 10, 20 and 30 m heights to estimate wind speed at higher heights up to 100 m. Proposed methods have been compared with ANN, power law and logarithmic law. Daily, monthly and yearly average...
全文获取路径: Springer  (合作)
分享到:
来源刊物:
影响因子:1.168 (2012)

×
关键词翻译
关键词翻译
  • extrapolation 外推法
  • network 网络
  • neural 神经系统的
  • artificial 人为的
  • swarm 
  • optimization 最佳化
  • speed 速率
  • machine 机器
  • existing 现行
  • algorithm 算法