Title :
A detailed literature review on wind forecasting
Author :
Chandra, D.R. ; Kumari, M. Sharmila ; Sydulu, M.
Author_Institution :
Electr. Engineeringg Dept., NIT WARANGAL, Warangal, India
Abstract :
There are several forecasting methods available to estimate the uncertainty of the wind. Wind behavior is chaotic in nature. These forecasting methods are used to predict wind power generation capacity for the grid. With the introduction of smart grid has created enough space for integrating renewable (wind power) in to the grid. Several methods have been proposed by researchers to estimate the wind speed. In present days there is a lot of research is going on to estimate the wind speed by using mathematical, biologically inspired computing methods to minimize the prediction error. This paper presents a review of several forecasting techniques which are using presently. This paper will be helpful for the new researchers who are going to work in this area. This paper will also be helpful to the wind farm operators to know about the present wind estimation model capabilities and will give an idea to estimate the wind speed at their particular wind farms.
Keywords :
autoregressive moving average processes; biocomputing; load forecasting; neural nets; power system analysis computing; smart power grids; wind power plants; biologically inspired computing methods; prediction error; smart grid; wind behavior; wind estimation model; wind farm; wind forecasting; wind power generation capacity; wind speed estimation; wind uncertainty; Atmospheric modeling; Forecasting; Power systems; Predictive models; Wind forecasting; Wind power generation; Wind speed; Adaptive Neuro-Fuzzy Inference System (ANFIS); Auto Regressive Moving Average (ARMA); Computational fluid dynamics; Numerical Weather prediction(NWP); neural networks; wind forecasting;
Conference_Titel :
Power, Energy and Control (ICPEC), 2013 International Conference on
Conference_Location :
Sri Rangalatchum Dindigul
Print_ISBN :
978-1-4673-6027-2
DOI :
10.1109/ICPEC.2013.6527734