he wind has constantly been a natural partner in propelling our societies forward. The wind is often considered as one of the most complex meteorological parameters to predict. This is a consequence of the compound interactions between large scale of natural forcing phenomena such as pressure, temperature differences, earth rotation, and local characteristics of the earth surface. The predicting technique employed depends essentially on the available information and the time scale in question (horizon), and thus its application. In this book it is proposed to deal with the prediction of wind speed by two different and independent methodologies: In the first one, the proposed static system seeks to get the best prediction performance from a set of predicting algorithms, this is done by using a new approach, where the outputs yielded by the different single prediction architectures are combined by three fusion methods. In the second one, the wind speed prediction problem is formulated in the framework of time series. Several variable selection techniques were investigated to find the optimal number of historical wind speed values in order to get the best prediction performance.
A new approach based on multiple architecture system (MAS) for the prediction of wind speed is proposed. The motivation behind the proposed approach is to combine the complementary predictive powers of multiple models in order to improve the performance of the prediction process. The proposed MAS can be implemented by associating the predictions obtained from the different regression algorithms (MLR, MLP, RBF and SVM) making up the ensemble by three fusion strategies (simple, weighted and non-linear). The efficiency of the proposed approach has been assessed on a real data set recorded from seven locations in Algeria during a period of 10 years. The experimental results point out that the proposed MAS approach is capable of improving the precision of the wind speed prediction compared to the traditional prediction methods.
In this paper, we propose a novel approach for the estimation of the concentration of chemical components through spectrophotometric measurements. It is based on the exploitation of the whole spectral information available in the original spectral data space by means of a Multiple Regression System (MRS) whose design is performed in three successive steps. The first one aims at a simple partitioning of the original spectral data space into subspaces of reduced dimensionality. The second step consists in training a (linear or nonlinear) regression method in each of the subspaces obtained in the previous step. In the third and final step, the estimates provided by the ensemble of regressors are combined in order to produce a global estimate of the concentration of the chemical component of interest. For this purpose, two linear and one nonlinear combination strategies are explored.
The experimental assessment of the MRS was carried out on two different datasets: 1) a wine dataset for the determination of alcohol concentration by mid-infrared spectroscopy; and 2) an orange juice dataset where near-infrared reflectance spectroscopy is used to estimate the saccharose concentration. The obtained results suggest that the proposed MRS approach represents a promising alternative to the traditional regression methods.