Publications

2021
Zereg H, Bouzgou H. Multi-Objective Optimization of Stand-Alone Hybrid Renewable Energy System for Rural Electrification in Algeria, in IC-AIRES: International Conference on Artificial Intelligence in Renewable Energetic Systems. ; 2021.
Louchene HE, Bouzgou H, Gueymard CA. Residual Networks with Long Short Term Memory for Hourly Solar Radiation Forecasting, in IC-AIRES: International Conference on Artificial Intelligence in Renewable Energetic Systems. ; 2021.
zemouri N, Bouzgou H, Gueymard CA. Sample Entropy with One-Stage Variational Mode Decomposition for Hourly Solar Irradiance Forecasting, in International Conference on Renewable Energy Advanced Technologie and Applications (ICREATA'21). Adrar, Algeria ; 2021.
Atmani H, Bouzgou H, Gueymard CA. Deep Long Short-Term Memory with Separation Models for Direct Normal Irradiance Forecasting: Application to Tamanrasset, Algeria, in International Conference on Renewable Energy Advanced Technologie and Applications (ICREATA'21). Adrar, Algeria ; 2021.
Zereg H, Bouzgou H. Techno-Economic Analysis of a Stand-Alone Hybrid Renewable Energy System for Residentiel Electrification in Tamanrasset, Algeria, in International Conference on Renewable Energy Advanced Technologie and Applications (ICREATA'21). Adrar, Algeria ; 2021.
2020
zemouri N, Bouzgou H, Gueymard CA. Global Solar Radiation Forecasting With Evolutionary Autoregressive Models, in 4th International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES'20). ; 2020.Abstract

Nowadays, the integration of solar power into the electrical grids is vital to increase energy efficiency and profitability. Effective usage of the instable solar production of photovoltaic (PV) systems necessitates trustworthy forecasting information. Actually, this addition can gives an ameliorated service quality if the solar radiation variation can be forecasted accurately. In this paper, we propose a new forecasting approach that integrates Autoregressive Moving Average (ARMA) and Genetic algorithms (GA) to make benefit of both of them in order to forecast Global Horizontal Irradiance (GHI) component. The proposed approach is compared with the standard ARMA model. The experimental results show that, the proposed approach outperforms the classical ARMA models in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) coefficient of determination (R)2 and the normalized mean squared error (NMSE).

2019
zemouri N, Bouzgou H, Gueymard CA. Multimodel ensemble approach for hourly global solar irradiation forecasting. The European Physical Journal Plus (Springer-Verlag) [Internet]. 2019;134 :594. Publisher's VersionAbstract
This contribution proposes a novel solar time series forecasting approach based on multimodel statistical ensembles to predict global horizontal irradiance (GHI) in short-term horizons (up to 1 hour ahead). The goal of the proposed methodology is to exploit the diversity of a set of dissimilar predictors with the  purpose of increasing the accuracy of the forecasting process. The performance of a specific multimodel ensemble forecast showing an improved forecast skill is demonstrated and compared to a variety of individual single models. The proposed system can be applied in two distinct ways. The first one incorporates the forecasts acquired from the different forecasting models constituting the ensemble via a linear combination (combination-based). The other one consists of a novel methodology that delivers as output the forecast provided by the specific model (involved in the ensemble) that delivers the maximum precision in the zone of the variable space connected with the considered GHI time series (selection-based approach). This forecasting model is issued from an appropriate division of the variable space. The efficiency of the proposed methodology has been evaluated using high-quality measurements carried out at 1min intervals at four radiometric sites representing widely different radiative climates (Arid, Temperate, Tropical, and High Albedo). The obtained results emphasize that, at all sites, the proposed multi-model ensemble is able to increase the accuracy of the forecasting process using the different combination approaches, with a significant performance improvement when using the classification strategy.
Zerari N, Abdelhamid S, Bouzgou H, Raymond C. Bidirectional deep architecture for Arabic speech recognition. Open Computer Science (De Gruyter) [Internet]. 2019;9 :92–102. Publisher's VersionAbstract
Nowadays, the real life constraints necessitates
controlling modern machines using human intervention
by means of sensorial organs. The voice is one of the human
senses that can control/monitor modern interfaces.
In this context, Automatic Speech Recognition is principally
used to convert natural voice into computer text as
well as to perform an action based on the instructions
given by the human. In this paper, we propose a general
framework for Arabic speech recognition that uses Long
Short-Term Memory (LSTM) and Neural Network (Multi-
Layer Perceptron: MLP) classifier to cope with the nonuniform
sequence length of the speech utterances issued
fromboth feature extraction techniques, (1)Mel Frequency
Cepstral Coefficients MFCC (static and dynamic features),
(2) the Filter Banks (FB) coefficients. The neural architecture
can recognize the isolated Arabic speech via classification
technique. The proposed system involves, first, extracting
pertinent features from the natural speech signal
using MFCC (static and dynamic features) and FB. Next,
the extracted features are padded in order to deal with the
non-uniformity of the sequences length. Then, a deep architecture
represented by a recurrent LSTM or GRU (Gated
Recurrent Unit) architectures are used to encode the sequences
ofMFCC/FB features as a fixed size vector that will
be introduced to a Multi-Layer Perceptron network (MLP)
to perform the classification (recognition). The proposed
system is assessed using two different databases, the first
one concerns the spoken digit recognition where a comparison
with other related works in the literature is performed,
whereas the second one contains the spoken TV
commands. The obtained results show the superiority of
the proposed approach.
Bouzgou H, Gueymard CA. Fast Short-Term Global Solar Irradiance Forecasting with Wrapper Mutual Information. Renewable Energy (Elsevier) [Internet]. 2019;133 :1055-1065. Publisher's VersionAbstract
Accurate solar irradiance forecasts are now key to successfully integrate the (variable) production from large solar energy systems into the electricity grid. This paper describes a wrapper forecasting methodology for irradiance time series that combines mutual information and an Extreme Learning Machine (ELM), with application to short forecast horizons between 5-min and 3-hour ahead. The method is referred to as Wrapper Mutual Information Methodology (WMIM). To evaluate the proposed approach, its performance is compared to that of three dimensionality reduction scenarios: full space (latest 50 variables), partial space (latest 5 variables), and the usual Principal Component Analysis (PCA). Based on measured irradiance data from two arid sites (Madina and Tamanrasset), the present results reveal that the reduction of the historical input space increases the forecasting performance of global solar radiation. In the case of Madina and forecast horizons from 5-min to 30-min ahead, the WMIM forecasts have a better coefficient of determination (R2 between 0.927 and 0.967) than those using the next best performing strategy, PCA (R2 between 0.921 and 0.959). The Mean Absolute Percentage Error (MAP) is also better for WMIM [7.4–10.77] than for PCA [8.4–11.55]. In the case of Tamanrasset and forecasting horizons from 1-hour to 3-hours ahead, the WMIM forecasts have an R2 between 0.883 and 0.957, slightly better than the next best performing strategy (PCA) (R2 between 0.873 and 0.910). The Normalized Mean Squared Error (NMSE) is similarly better for WMIM [0.048–0.128] than for PCA [0.105–0.130]. It is also found that the ELM technique is considerably more computationally efficient than the more conventional Multi Layer Perceptron (MLP). It is concluded that the proposed mutual information-based variable selection method has the potential to outperform various other proposed techniques in terms of prediction performance.
2018
Mezaache H, Bouzgou H. Auto-Encoder with Neural Networks for Wind Speed Forecasting, in International Conference on Communications and Electrical Engineering (ICCEE) . IEEE ; 2018 :1-5. Publisher's VersionAbstract
The use of wind energy is progressively utilized to produce electrical energy. Wind energy is related to the variation of some atmospheric variables such as wind direction, wind speed, air density and atmospheric pressure. Recently, numerous methods base on Artificial Intelligence techniques to forecast wind speed have been proposed in the literature. In this paper a new artificial intelligence approach for wind speed time series forecasting is proposed, it is composed from two blocs: The first one is based on the use of a deep architecture. The Autoencoder which is a type of deep neural networks, utilized generally for Denoising, is employed to reduce the wind speed input dimensionality. In the second bloc of the proposed methodology, the Elman neural network is employed to forecast future values of wind series, it is a kind of recurrent neural networks that are very sensitive to historical variations. To evaluate our approach we used the following error indicators: Root Mean Square Error (RMSE),Mean Absolute Bias Error (MABE), Mean Absolute Percentage Error (MAPE)and the coefficient of determination (R 2 ). The obtained results are compared with those of the Extreme Learning Machine method.
Zerari N, Abdelhamid S, Bouzgou H, Raymond C. Bi-directional recurrent end-to-end neural network classifier for spoken Arab digit recognition. 2nd International Conference on Natural Language and Speech Processing (ICNLSP) IEEE [Internet]. 2018 :1-6. Publisher's VersionAbstract
Automatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.
2017
zemouri N, Bouzgou H. Ensemble of Support Vector Methods to Estimate Global Solar Radiation In Algeria. International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES2017 (Springer) [Internet]. 2017 :1-5. Publisher's VersionAbstract
In this paper, we propose a set of times series forecasting techniques based on the combination of Support Vector Regression methods to predict global horizontal solar radiation in Algeria. The models were constructed and tested using different architectures of Support Vector Machine (SVM), namely, (RBF kernel, Polinomial kernel and Linear kernel). We use individual time series models and linear combination techniques to predict global solar radiation indifferent sites in Algeria. For this aim, the recorded data of 4 stations spread over Algeria were used to build different combination schemes for the different times series algorithms. The efficiency of the different models was calculated using a number of statistical indicators: the Mean Absolute Percentage Error (MAPE), the Mean Squared Error (RMSE), Mean Bias Error (MABE) and the Coefficient of Determination (R 2). The results obtained from these models were compared with the measured data.
Atmani H, Bouzgou H, Gueymard CA. Intra-hour Forecasting of Direct Normal Solar Irradiance using Variable Selection with Artificial Neural Networks. International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES2017 (Springer) [Internet]. 2017 :1-5. Publisher's VersionAbstract
Renewable Energy Sources (RES) are one of the key solutions to handle the world’s future energy needs, while decreasing carbon emissions. To produce electricity, large concentrating solar power plants depend on Direct Normal Irradiance (DNI), which is rapidly variable under broken clouds conditions. To work at optimum capacity while maintaining stable grid conditions, such plants require accurate DNI forecasts for various time horizons. The main goal of this study is the forecasting of DNI over two short-term horizons: 15-min and 1-h. The proposed system is purely based on historical local data and Artificial Neural Networks (ANN). For this aim, 1-min solar irradiance measurements have been obtained from two sites in different climates. According to the forecast results, the coefficient of determination (R2) ranges between 0.500 and 0.851, the Mean Absolute Percentage Error (MAPE) between 0.500 and 0.851, the Normalized Mean Squared Error (NMSE) between 0.500 and 0.851, and the Root Mean Square Error (RMSE) between 0.065 kWh/m2 and 0.105 kWh/m2. The proposed forecasting models show a reasonably good forecasting capability, which is decisive for a good management of solar energy systems.
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.

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