Optimized DHT-RBF model as replacement of ARMA-RBF model for wind power forecasting

Author

Mukhopadhyay, Saibal ; Panigrahi, Pratap Kumar ; Mitra, Abhijit ; Bhattacharya, Pallab ; Sarkar, Mohanchur ; Das, Pritam

Author_Institution

Phys. Sci. Dept., IISER, Kolkata, India

fYear

2013

fDate

25-26 March 2013

Firstpage

415

Lastpage

419

Abstract

ARMA-Neural model is an established useful model for the Wind Power forecasting purpose. In the current work we introduced Discrete Hilbert Transform (DHT)-Neural Model which provides better result than the ARMA-Neural Model. We know that a signal and its´ DHT produces the same Energy Spectrum. Based on this concept in this paper DHT is used for Wind Speed forecasting purpose. Thereafter the RBF neural network is used on this to forecast wind power. Taking the data of measured wind speed from Weather Forecasting Bureau Report as example, we validate the method described above.

Keywords

Hilbert transforms; load forecasting; radial basis function networks; weather forecasting; wind power; ARMA-RBF model replacement; ARMA-neural model; DHT-neural model; RBF neural network; Weather Forecasting Bureau report; discrete Hilbert transform-neural model; energy spectrum; optimized DHT-RBF model; wind power forecasting; wind speed forecasting; Forecasting; Neural networks; Transfer functions; Transforms; Wind power generation; Wind speed; ARMA Model; Discrete Hilbert Transform; RBF Neural Network; Wind Power;

fLanguage

English

Publisher

ieee

Conference_Titel

Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN), 2013 International Conference on

Conference_Location

Tirunelveli

Print_ISBN

978-1-4673-5037-2

Type

conf

DOI

10.1109/ICE-CCN.2013.6528534

Filename

6528534