Title :
A comparative analysis for wind speed prediction
Author :
Tarade, R.S. ; Katti, P.K.
Author_Institution :
Electr. Eng. Dept., Dr. Babasaheb Ambedkar Technol. Univ., Lonere, India
Abstract :
Predicting short term wind speed is essential in order to prevent systems in-action from the effects of strong winds. It also helps in using wind energy as an alternative source of energy, mainly for Electrical power generation. Wind speed prediction has applications in Military and civilian fields for air traffic control, rocket launch, ship navigation etc. The wind speed in near future depends on the values of other meteorological variables, such as atmospheric pressure, moisture content, humidity, rainfall etc. There are number of methods for prediction. Using different methods for wind speed prediction and comparing these results we can obtain accurate method of prediction. This paper reports ARIMA, Artificial Neural Network and polynomial curve fitting model for short term wind speed prediction.
Keywords :
curve fitting; neural nets; polynomials; power engineering computing; wind power; ARIMA; air traffic control; alternative energy source; artificial neural network; atmospheric pressure; civilian field; comparative analysis; electrical power generation; humidity; meteorological variable; military field; moisture content; polynomial curve fitting model; rainfall; rocket launch; ship navigation; short term wind speed prediction; wind energy; Biological neural networks; Curve fitting; Mathematical model; Neurons; Polynomials; Predictive models; Wind speed; ARIMA; Artificial neural networks; Back propagation; Regression; Short term wind speed prediction; polynomial curve fitting;
Conference_Titel :
Energy, Automation, and Signal (ICEAS), 2011 International Conference on
Conference_Location :
Bhubaneswar, Odisha
Print_ISBN :
978-1-4673-0137-4
DOI :
10.1109/ICEAS.2011.6147167