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Publications

2017
zemouri N, Bouzgou H, Gueymard CA. Gaussian Process with Linear Discriminant Analysis for Predicting Hourly Global Horizontal Irradiance in Tamanrasset, Algeria. The 5th International Conference on Electrical Engineering (ICEE-B) (IEEE) [Internet]. 2017 :1-5. Publisher's VersionAbstract
In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian Process (GP) model combined with Linear Discriminate Analysis (LDA) as dimensionality reduction method is proposed. To evaluate the proposed approach, its performance is assessed using three scenarios: long window (latest 50 variables), short window (latest 5 variables) and persistence. To evaluate the performance of the proposed forecast model, the results of the different scenarios are compared to that of Extreme Learning Machines (ELM). Based on measured irradiance data from Tamanrasset, Algeria, the present results describe the performance of the combination of LDA with GP for forecasting hourly global solar irradiance.
Bouzgou H, Gueymard CA. Minimum redundancy – Maximum relevance with extreme learning machines for global solar radiation forecasting: Toward an optimized dimensionality reduction for solar time series. Solar Energy (Elsevier) [Internet]. 2017;158 :559-609. Publisher's VersionAbstract
Solar energy is expected to provide a major contribution to the future global energy supply, while helping the transition toward a carbon-free economy. Because of its variable character, its efficient use will necessitate trustworthy forecast information of its availability in a variety of spatial and time scales, depending on application. This study proposes a new forecasting approach for irradiance time series that combines mutual information measures and an Extreme Learning Machine (ELM). The method is referred to as Minimum Redundancy – Maximum Relevance (MRMR). To assess the proposed approach, its performance is evaluated against four scenarios: long window (latest 50 variables), short window (latest 5 variables), standard Principal Components Analysis (PCA) and clear-sky model. All these scenarios are applied to three typical forecasting horizons (15-min ahead, 1-h ahead and 24-h ahead). Based on measured irradiance data from 20 sites representing a variety of climates, the test results reveal that the selection of a good set of relevant variables positively impacts the forecasting performance of global solar radiation. The present findings show that the proposed approach is able to improve the accuracy of solar irradiance forecasting compared to other proposed scenarios.
2016
Mezaache H, Bouzgou H, Raymond C. Kernel Principal Components Analysis with Extreme Learning Machines for Wind Speed Prediction, in Seventh International Renewable Energy Congress "IREC2016". Hammamet, Tunisia ; 2016 :1-5. Publisher's VersionAbstract
Nowadays, wind power and precise forecasting are of great importance for the development of modern electrical grids. In this paper we propose a prediction system for time series based on Kernel Principal Component Analysis (KPCA) and Extreme Learning Machine (ELM). To compare the proposed approach, three dimensionality reduction techniques were used: full space (50 variables), part of space (last four variables) and classical Principal Components Analysis (PCA). These models were compared using three evaluation criteria: mean absolute error (MAE), root mean square error (RMSE), and normalized mean square error (NMSE). The results show that the reduction of the original input space affects positively the prediction output of the wind speed. Thus, It can be concluded that the non linear model (KPCA) model outperform the other reduction techniques in terms of prediction performance.
2014
Assas O, Bouzgou H, Fetah S, Salmi M, Boursas A. Use of the artificial neural network and meteorological data for predicting daily global solar radiation in Djelfa, Algeria. International Conference on Composite Materials & Renewable Energy Applications (ICCMREA), (IEEE) [Internet]. 2014 :1-5. Publisher's VersionAbstract
This paper presents a set of artificial neural network models (ANN) to estimate daily global solar radiation (GSR) on a horizontal surface using meteorological variables: (mean daily extraterrestrial solar radiation intensity G0, the maximum possible sunshine hours S0, mean daily relative humidity H, mean daily maximum air temperature T, mean daily atmospheric pressure P and wind speed Vx) for Djelfa city in Algeria. In order to consider the effect of the different meteorological parameters on daily global solar radiation prediction, four following combinations of input features are considered: 1) Day of the year, G0, S0, T and Vx. 2) Day of the year, G0, S0, T, P and Vx. 3) Day of the year, G0, S0, T, H, P and Vx. 4) Day of the year, G0, S0, T, H and Vx. These models were compared using three evaluation criteria: Mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE). The results show that the two parameters: atmospheric pressure and relative humidity affect the prediction output of global solar radiation. In addition, the results show that the relative humidity is the most important features influencing the prediction performance. It can be concluded that fourth model can be used for forecasting daily global solar radiation in other locations in Algeria.
Salmi M, Bouzgou H, Al-Douri Y, Boursas A. Evaluation of the Hourly Global Solar Radiation on a Horizontal Plane for Two Sites in Algeria. Advanced Materials Research [Internet]. 2014;925 :641-645. Publisher's VersionAbstract
We present three models for the estimation of hourly global solar radiation for two sites in Algeria, namely: Djelfa (Latitude 34.68°N, Longitude 3.25°E, Altitude 1126 (m)) and Ain Bessem (Latitude 36.31°N, Longitude 3.67°E, Altitude 629 (m)). The models are: the Gaussian distribution model, the model by Collares-Pereira-RabI and the H.A. model (Hourly absolute modelling approach). The experimental assessment was done using recorded values of the hourly global solar radiation on a horizontal plane during the period 2000-2004. The obtained results show a close similarity between the solar radiation values calculated by the three models and the measured values, especially for the first model. The experimental validation shows promising results for the estimation and precise prediction of the hourly global solar radiation.
Bouzgou H. A fast and accurate model for forecasting wind speed and solar radiation time series based on extreme learning machines and principal components analysis. Journal of Renewable and Sustainable Energy (AIP) [Internet]. 2014;6 (1) :013114. Publisher's VersionAbstract
Precise forecasting of renewable energies such as solar and wind is becoming one of the very important concerns in developing modern electrical grids. Hence, establishing appropriate tools of weather forecasting with satisfactory accuracy becomes an essential preoccupation in today's changing power world. In this paper, an approach based on Principal Component Analysis (PCA) and Extreme Learning Machines (ELM) is proposed for the forecasting of time series. The PCA maps the data into a smaller subspace in which the components accounts for as much of the variability in the input space as possible. The variables extracted by the PCA are then introduced to the extreme learning machines, a learning algorithm much faster than the traditional gradient-based learning algorithms. The experiments carried out on three time series lead to: (i) The PCA as variable selection method shows a positive impact on the accuracy of the forecasting process. (ii) ELM model is significantly faster than Multi-Layer Perceptron Network, Radial Basis Function Networks, and Least Squares Support Vector Machines, while preserving the same accuracy level.
2013
Bouzgou H. Regression with Hyperdimensional Features: Application to Chemometric Calibration. Germany: LAP Lambert Academic Publishing; 2013 pp. 110. Publisher's VersionAbstract
The automatic analysis of data acquired with hyperdimensional sensors is rather challenging since it should be carried out in hyperdimensional feature spaces. The huge size of the feature space involves the so-called curse of dimensionality. This latter is due to the unbalancing between the number of features and the number of samples. In this book, it is proposed to exploit the whole information available in the original hyperdimensional feature space by means of the fusion of multiple regression methods. The development of the proposed multiple regression systems will include three main steps. The first one is related to the partition of the original hyperdimensional feature space into subspaces of reduced dimensionality. The second step consists in training in each of the subspaces obtained in the previous step a regression method. Finally, in the third and final step, the results provided by the different regression methods will be combined in order to produce a global estimate of the physical parameter of interest with an expected higher accuracy with respect to what can be achieved by the classical regression approach based on feature selection.
2012
Fetah S, Assas O, Salmi M, Bouzgou H, Boursas A. Modelling of global solar radiation in Algeria based on geographical and all climatic parameters, in Deuxième Séminaire International sur les Energies Nouvelles et Renouvelables. Ghardaia, Algeria ; 2012 :1-5.
Bouzgou H. Advanced Methods for the Processing and Analysis of Multidimensional Signals: Application to Wind Speed. Department of Electronics, University of Batna2 [Internet]. 2012. Publisher's Version
Bouzgou H. Automatic Analysis of Highdimensional Signals: Advanced Wind Speed Forecasting Techniques. Germany: LAP Lambert Academic Publishing; 2012 pp. 115. Publisher's VersionAbstract
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.
2011
Bouzgou H, Benoudjit N. Multiple architecture system for wind speed prediction. Applied Energy (Elsevier) [Internet]. 2011;88 (7) :2463-2471. Publisher's VersionAbstract
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.
2010
Salmi M, Bouzgou H, Laissaoui L. Estimation de l’irradiation Solaire Globale dans la ville de M’sila (Algérie), in 1ère Conférence Maghrébine sur les Matériaux et l’énergie, University of Gafsa, Tunisia. Gafsa, Tunisia ; 2010 :1-4.
2009
Salmi M, Bouzgou H, Boursas A. A Comparison Study of Solar Energy : Application to Arabic Countries, in 3rd Conference of Basic Science, University of Aljabal Algharibi, Lybia. Aljabal Algharibi, Lybia ; 2009 :1-4.
Benoudjit N, Melgani F, Bouzgou H. Multiple regression systems for spectrophotometric data analysis. Chemometrics and Intelligent Laboratory Systems (Elsevier) [Internet]. 2009;95 (2) :144-149. Publisher's VersionAbstract

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.

2007
Benoudjit N, Bouzgou H, Melgani F. Combination of Multiple Estimators for Hyperdimensional Data Analysis, in Proceedings of: International Conference on Electrical Engineering Design & Technologies, Tunisia: ICEEDT. Hammamet, Tunisia ; 2007 :1-5.
2006
Bouzgou H. Development of Multiple Regression Systems for Hyperdimensional Spectral Spaces. Department of Electronics, University of Batna2 [Internet]. 2006. Publisher's Version

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