Prediction of Wind Speed Using Hybrid Techniques

Abstract

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: (1) As a single time series containing all measurements, and (2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 h in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-squares support vector machines, empirical mode decomposition, and the wavelet transform. Moreover, the traditional and simple auto-regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a matlab implementation showed that the least-squares support vector machine using data as a single time series outperformed the other combinations, obtaining the least root mean square error (RMSE).

Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

“…e fusion combination is optimized by other prediction methods in different prediction stages, including input data stabilization, model parameter optimization, and output error correction. Based on empirical mode decomposition (EMD) [22][23][24][25], variational mode decomposition (VMD) [26][27][28][29], analytical mode decomposition (AMD) [30,31], the wavelet decomposition [14,25,32], and so on, the wind speed sequence data was preprocessed to make the data stable. Better prediction results are achieved.…”

A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm

“…Song and Paek [9] studied the dynamic simulations of a wind turbine to predict its annual energy production using a computational fluid dynamics code for wind farm. Lopez et al [10] predicted wind speed in the short-term using three non-parametric statistical regression techniques. Moreover, other authors used particular transformations of the data, such as Box-Cox and the normal inverse Gaussian transformation, to obtain data closer to a Gaussian distribution [11][12][13].…”