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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...
Wind generation is the most widespread form of renewable energy, with a high degree of penetration in traditional electricity networks. Hence, the influence of wind power generation over the efficient operation of power systems is increasingly complex. This determines the actors playing in the wind energy market to show an increased interest in developing efficient forecasting models for power generated...
In this paper, wind speed and the power generated are predicted using phase space reconstruction and artificial neural network (ANN) method. First of all, data of wind speed is preprocessed with theory of phase space reconstruction to obtain the optimal length of time series for forecasting. And then, RBF artificial neural network is used to forecast the wind speed. Finally, the output power of a...
The development of wind generation has rapidly progressed over the last decade, the most important application for wind power prediction is to reduce the need for balancing energy and reserve power, which are needed to integrate wind power into the balancing of supply and demand in the electricity supply system. This paper presents a new method of wind power prediction in short-term with Artificial...
This paper presents the hybridization of global and mesoscale weather forecasting models with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at aero-generators in a wind park is an important parameter used to predict the total energy production of the park. Our model for short-term wind speed forecast integrates two different meteorological...
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