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Wind power is a significant alternate energy in times of energy crisis. In virtue of its intermittency and fluctuation, it poses several operational challenges to grid interfaced wind energy systems. This paper introduced autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) to forecast the hourly wind speed one to four hours ahead. The models are applied to wind...
In recent years, environmental considerations have prompted the use of wind power as a renewable energy resource. However, the biggest challenge in integrating wind power into the electric grid is its intermittency. One approach to deal with wind intermittency is forecasting future values of wind power production. Thus, several wind power or wind speed forecasting methods have been reported in the...
Air temperature, relative humidity and vapor pressure data during 1993-2004 for city of Manjil in Iran were used for the estimation of wind speed in future time domain using artificial neural network method. The estimations of wind speed were made using three combinations of data sets namely: (i) month of the year, monthly mean daily air temperature and relative humidity as inputs and wind speed as...
Wind power is fluctuate, intermittent and stochastic. The large capacity wind power connected with power grids will bring austere challenge to the safety and stabilization of power system operation. Wind speed prediction is an effective approach for this problem. ANN (artificial neural network) has been used extensively in wind speed prediction. But ANN is apt to getting into local minima and its...
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