Short-Term Forecasting with Mixed-Frequency Data: A MIDASSO Approach

Short and Mid-Term Wind Power Plants Forecasting With ANN

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

Short and Mid-Term Wind Power Plants Forecasting With ANN

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

A hybrid WFA approach for Short-Term Wind Power Forecasting

Wind generation is hectic by nature, making wind power forecasting highly challenging, particularly for short time frames. Forecasting of wind power is becoming progressively more important to power system operators and electricity market.Wind power is variable and irregular over various timescales as it is weather dependent. Thus precise forecasting of wind power is acknowledged as a major con...

A New Approach to Short-Term Tsunami Forecasting

A Hybrid Approach for Short-Term Forecasting of Wind Speed

We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and a...