TY  - SER
AU  - Sunida Chaokasem
AU  - Huynh,Ngoc Phien
AU  - Sadananda,Ramakoti
AU  - Tien,Hoang Le
ED  - Royal Thai Government, 
TI  - Wind data modeling and forecasting
T2  - Thesis
PY  - 2000///
CY  - Bangkok
PB  - Asian Institute of Technology
KW  - Wind forecasting
KW  - Winds
KW  - Speed
KW  - Measurement
N1  - A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies; Thesis (M.Eng.) - Asian Institute of Technology 2000
N2  - In this study, hourly wind speed and direction at Promthep cape in Phuket, are forecasted and simulated using Box-Jenkins and Backpropagation approaches without the use of other external data. It was found that ARIMA models and seasonal ARIMA models are useful for short-range forecasting but are not very good in data generation. Backpropagation network models are good for forecasting and for generating data, which resemble the observed data in terms of the important statistics (mean, variance and skewness coefficient). For forecasting, Box-Jenkins models can perform slightly better than Backpropagation network models. However, for data generation (simulation), Backpropagation models can preserve the statistics of the observed data much better than Box-Jenkins models
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B06718
ER  -