@article {99349,
	title = {A Novel Approach for Accurate Wind Speed Time Series Forecasting Using ICEEMDAN Decomposition and Sample Entropy through Integration of Deep Learning Models},
	journal = {International Journal of Engineering},
	volume = {39},
	number = {2},
	year = {2026},
	pages = {309-320},
	abstract = {This study proposes a novel hybrid model for wind speed forecasting (WSF) based on a three-stage framework comprising decomposition, feature selection, and forecasting. The proposed approach employs Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to decompose wind speed time series into Intrinsic Mode Functions (IMFs). A distinctive contribution of this study is the use of sample entropy as a feature selection mechanism to identify the most relevant Intrinsic Mode Functions (IMFs). The selected IMFs are then integrated through a classification-based fusion technique, significantly enhancing forecasting accuracy and distinguishing this approach from conventional methods. Two distinct forecasting approaches are evaluated using multiple performance metrics, including RMSE, MAE, MAPE, and R{\texttwosuperior}. The first approach applies the fusion technique directly to the original wind speed time series, while the second incorporates ICEEMDAN to decompose the time series. Experimental validation using two real-world datasets from Algeria demonstrates the superiority of the proposed hybrid model over individual forecasting models, yielding significant improvements in prediction accuracy, robustness, and stability. These findings underscore the effectiveness of the three-stage framework, offering a reliable and efficient solution for short-term wind speed forecasting, with potential applications in renewable energy management and grid optimization.},
	url = {https://www.ije.ir/article_218007.html},
	author = {Hatem Mezaache and Hassen Bouzgou and Christian Raymond and Nahed zemouri}
}
@article {99348,
	title = {Forecast-integrated techno-economic optimization of off-grid hybrid renewable system in hyper-arid regions: Application to Tamanrasset, Algeria},
	journal = {Energy},
	volume = {334C},
	year = {2025},
	pages = {137468},
	abstract = {This study presents a novel framework for the optimal design of an off-grid residential energy system, applied to the hyper-arid region of Tamanrasset, Algeria. The proposed hybrid renewable energy system (HRES) integrates photovoltaic panels, wind turbines, battery storage, and diesel generators. A key innovation is the integration of a green energy forecasting module within a multi-objective techno-economic optimization process. Various machine learning and deep learning models suited for time series prediction were evaluated, and the best-performing models for each meteorological parameter were selected which enables precise long-term hourly forecasts, improving system design and operational efficiency compared to conventional methods based on historical averages. The optimization targets three objectives: minimizing the Levelized Cost of Energy (LCOE), reducing the Loss of Power Supply Probability (LPSP), and maximizing the Reliability Factor (RF). Using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the system achieves an LCOE of $0.05433/kWh, an LPSP of 3.1\&nbsp;\%, and an RF of 98\&nbsp;\%, indicating a strong balance between cost and reliability. Energy contributions are 47\&nbsp;\% from solar PV, 35\&nbsp;\% from batteries, 12\&nbsp;\% from wind, and 6\&nbsp;\% from diesel. Comparison with HOMER Pro simulations confirms the superior economic performance of the MOPSO-based configuration. Sensitivity analyses underscore the critical role of forecast accuracy in HRES performance, while environmental assessments show an 80\&nbsp;\% reduction in CO2 emissions compared to diesel-only systems. The integrated forecasting module serves as a valuable decision-support tool for rural electrification, particularly in resource-constrained and climate-challenged regions.},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S036054422503110X},
	author = {Hassen Bouzgou and Hadda Zereg}
}
@inbook {97306,
	title = {Optimizing Annual Energy Output of a Residential Bifacial PV System Using Box-Behnken Design: A Case Study in Tamanrasset, Algeria},
	booktitle = {Lecture Notes in Networks and Systems ((LNNS,volume 1238)) },
	year = {2024},
	pages = {428-436},
	publisher = {Springer},
	organization = {Springer},
	abstract = {In this paper, Response Surface Methodology (RSM) with optimization is proposed to determine the optimal annual energy output of a small-scale residential bifacial PV system, considering constraints input parameters such as albedo type, module height, tilt angle, and spacing. This could be done by quantifying the relationship between the variable input parameters and the corresponding output parameter where RSM is integrated with PVsyst simulation tool for the analysis. The proposed Box-Behnken design (BBD) optimization required 29 runs for data acquisition and modelling the response surface and Design-Expert software was used to design the experiments and randomize the runs. Regression model was developed and its adequacy was verified to predict the output value at nearly all conditions. The optimization study shows that the system produces the maximum yearly energy when the ground albedo is 0.71, module height is 1.68\&nbsp;m, tilt angle is 19.42{\textdegree}, and module spacing is 7.42\&nbsp;m. These characteristics were found using the meteorological conditions in Tamanrasset, Algeria.},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-80301-7_47},
	author = {Khaoula Rahrah and Soufiane Taibi and Hassen Bouzgou}
}
@inbook {97305,
	title = {Determination of the Optimal Volume and Distribution of Learning Data for Solar Irradiance Forecasting Applications},
	booktitle = {Lecture Notes in Networks and Systems ((LNNS,volume 1238)) },
	year = {2024},
	pages = {584{\textendash}593},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Given the significant importance of renewable or alternative energies today, extensive research is being conducted to enhance the efficiency and reduce the costs of utilizing these energy sources. Among these studies, solar energy forecasting plays a crucial role in achieving these objectives. Accurate forecasting can optimize energy yield, improve grid management, and facilitate the integration of solar power into existing energy systems, ultimately contributing to more reliable and cost-effective renewable energy solutions. This contribution investigates how data volume influences forecasting accuracy. In particular, the impacts on forecasting accuracy of varying forecast horizons and optimal data splitting for training and testing phases are examined. Additionally, the effects on forecasting Global Horizontal Irradiance (GHI) of clustering the data into 3, 5, or 7 groups using the K-means algorithm are investigated. Five different predictive models are employed{\textemdash} multi layer perceptron (MLP), support vector regression (SVR), random forest (RF), convolutional neural network (CNN), and long short-term memory (LSTM){\textemdash} alongside the newly proposed kResCLSTM hybrid method. Using GHI observations at an arid site in southern Algeria, it is found that a 10-year time series is optimal, along with a 60\%-40\% split in it for the training vs. testing periods.},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-80301-7_63},
	author = {Houssem Eddine Louchene and Hassen Bouzgou and Christian Gueymard}
}
@inbook {97304,
	title = {Spatiotemporal Prediction of Solar Irradiance in Algeria: A CNN-Driven Approach for Solar Energy Mapping},
	booktitle = {Lecture Notes in Networks and Systems ((LNNS,volume 1238)) },
	year = {2024},
	pages = {544{\textendash}553},
	publisher = {Springer},
	organization = {Springer},
	abstract = {The first objective of this study is to estimate the potential of solar irradiance in Algeria using an artificial intelligence approach, namely, convolutional neural networks (CNNs). The second objective is to visualize the different solar components DNI, DHI, and GHI as spatial maps. Data from 267 locations for 2005{\textendash}2022, obtained from the NREL database, subdivided into training and testing data are used to build the different forecasting models. Testing data are not used when training CNNs to provide an indication of the performance at unknown locations. CNN models with 9 input variables average temperature, relative humidity, sunshine duration, wind speed, precipitation, longitude, latitude, altitude, and month were used to estimate the monthly values of GHI, DNI, and DHI. Statistical error analysis is conducted using mean absolute error (MAE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE) and coefficient of determination R2. This study shows that CNNs can be a better solution to estimate solar irradiance data. Seasonal solar mapping was developed in a GIS environment, representing locations and values of solar irradiance. These maps offer valuable insights into solar energy resources, aiding in the implementation of solar energy systems.},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-80301-7_59},
	author = {Salah Eddine Megaache and Hassen Bouzgou and Amor Gama and Naima Zerari}
}
@article {97303,
	title = {Comparative Analysis of Forecasting Methods for Renewable Energy Across Forecast Horizons},
	journal = {Algerian Journal of Renewable Energy and Sustainable Development},
	volume = {06},
	number = {02},
	year = {2024},
	pages = {231-240},
	abstract = {Sustainable energy sources like solar and wind speed provide an economically efficient source of energy. Prediction of the output of renewable energy plays a crucial role in shaping decisions concerning electrical system operation and management. Forecasting precision in renewable energy output is essential to ensuring the reliability and stability of the grid, as well as for mitigating risks and minimizing costs within the energy market and power systems. Various statistical techniques were developed to predict solar radiation and wind speed for this purpose and there are two types approaches commonly used: Deep learning and artificial Neural network (ANN). This work propose the used of three statistical methods based in Elman Recurrent Neural Network (ERNN), Long Short Term Memory (LSTM) and Support Vector Machine (SVM) to forecast the output data in different forecasting horizons. Four evaluation deferent metric are used: Forecast skill (FS), Root mean square error (RMSE), Mean Absolute Error (MAE) and coefficient of determination ( R2). These metrics confirm the robustness and accuracy of the LSTM model, validated by its RMSE, MAE, FS, and R{\texttwosuperior} values for both sites. These performances demonstrate the effectiveness of LSTM in capturing temporal patterns, with significant implications for weather forecasting and renewable energy applications.},
	url = {https://ajresd.univ-adrar.edu.dz/index.php?journal=AJRESD\&page=article\&op=view\&path\%5B\%5D=256},
	author = {Nahed zemouri and Hatem Mezaache and Hassen Bouzgou}
}
@conference {91110,
	title = {A Set of Forecasting Methods to Predict Solar Irradiance and Wind Speed for Different Horizons},
	booktitle = {Premi{\`e}re Conf{\'e}rence Nationale (en ligne) sur les Applications de l{\textquoteright}Intelligence Artificielle et le D{\'e}veloppement Durable (1{\`e}re CNAIADD{\textquoteright}2024)},
	year = {2024},
	author = {Nahed zemouri and Hatem Mezaache and Hassen Bouzgou}
}
@conference {90362,
	title = {Machine Learning Approach to Estimate Ultraviolet Irradiance from Solar Radiation Data},
	booktitle = {2023 International Conference on Electrical Engineering and Advanced Technology (ICEEAT)},
	year = {2023},
	pages = {1-6},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {One primary cause of degradation for materials exposed to the outdoors is ultraviolet (UV) light. To estimate their rate of degradation and determine their longevity (service life), truthful quantification of the UV irradiation incident on their surfaces is needed. In this paper, several neural models are proposed to estimate three UV components based on the broadband global horizontal irradiance and various atmospheric constituents. Using spectral results obtained with the SMARTS model for a large variety of atmospheric conditions and sun positions, three groups of experiments are carried out: the first one is designed to estimate the total UV irradiance at the surface (280{\textendash}400 nm), whereas the second and third groups aim at estimating its UV-A and UV-B fractions, respectively. The results generally show a good prediction performance, particularly for the first two groups.},
	url = {https://ieeexplore.ieee.org/abstract/document/10426472},
	author = {Naima Zerari and Christian Gueymard and Hassen Bouzgou and Christian Raymond}
}
@conference {87311,
	title = {Multi-Step Solar Power Forecasting using Deep Learning Methods},
	booktitle = { International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE)},
	year = {2022},
	url = {https://ieeexplore.ieee.org/abstract/document/10093688},
	author = {Nahed zemouri and Hassen Bouzgou and Chouder, Aissa and Mohamed DOUAK}
}
@conference {53647,
	title = {Multi-Objective Optimization of Stand-Alone Hybrid Renewable Energy System for Rural Electrification in Algeria},
	booktitle = {IC-AIRES: International Conference on Artificial Intelligence in Renewable Energetic Systems},
	year = {2021},
	author = {Hadda Zereg and Hassen Bouzgou}
}
@conference {53646,
	title = {Residual Networks with Long Short Term Memory for Hourly Solar Radiation Forecasting},
	booktitle = {IC-AIRES: International Conference on Artificial Intelligence in Renewable Energetic Systems},
	year = {2021},
	author = {Houssem Eddine Louchene and Hassen Bouzgou and Christian A. Gueymard}
}
@conference {52389,
	title = {Sample Entropy with One-Stage Variational Mode Decomposition for Hourly Solar Irradiance Forecasting},
	booktitle = {International Conference on Renewable Energy Advanced Technologie and Applications (ICREATA{\textquoteright}21)},
	year = {2021},
	address = {Adrar, Algeria},
	author = {Nahed zemouri and Hassen Bouzgou and Christian A. Gueymard}
}
@conference {52388,
	title = {Deep Long Short-Term Memory with Separation Models for Direct Normal Irradiance Forecasting: Application to Tamanrasset, Algeria},
	booktitle = {International Conference on Renewable Energy Advanced Technologie and Applications (ICREATA{\textquoteright}21)},
	year = {2021},
	address = {Adrar, Algeria},
	author = {Hanane Atmani and Hassen Bouzgou and Christian A Gueymard}
}
@conference {52387,
	title = {Techno-Economic Analysis of a Stand-Alone Hybrid Renewable Energy System for Residentiel Electrification in Tamanrasset, Algeria},
	booktitle = {International Conference on Renewable Energy Advanced Technologie and Applications (ICREATA{\textquoteright}21)},
	year = {2021},
	address = {Adrar, Algeria},
	author = {Hadda Zereg and Hassen Bouzgou}
}
@conference {39606,
	title = {Global Solar Radiation Forecasting With Evolutionary Autoregressive Models},
	booktitle = {4th International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES{\textquoteright}20)},
	year = {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).

},
	author = {Nahed zemouri and Hassen Bouzgou and Christian A. Gueymard}
}
@article {11147,
	title = {Multimodel ensemble approach for hourly global solar irradiation forecasting},
	journal = {The European Physical Journal Plus (Springer-Verlag)},
	volume = {134},
	year = {2019},
	pages = {594},
	abstract = {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\&nbsp; 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.},
	url = {https://link.springer.com/article/10.1140/epjp/i2019-12966-5},
	author = {Nahed zemouri and Hassen Bouzgou and Christian A. Gueymard}
}
@article {8688,
	title = {Bidirectional deep architecture for Arabic speech recognition},
	journal = {Open Computer Science (De Gruyter)},
	volume = {9},
	year = {2019},
	pages = {92{\textendash}102},
	abstract = {Nowadays, the real life constraints necessitatescontrolling modern machines using human interventionby means of sensorial organs. The voice is one of the humansenses that can control/monitor modern interfaces.In this context, Automatic Speech Recognition is principallyused to convert natural voice into computer text aswell as to perform an action based on the instructionsgiven by the human. In this paper, we propose a generalframework for Arabic speech recognition that uses LongShort-Term Memory (LSTM) and Neural Network (Multi-Layer Perceptron: MLP) classifier to cope with the nonuniformsequence length of the speech utterances issuedfromboth feature extraction techniques, (1)Mel FrequencyCepstral Coefficients MFCC (static and dynamic features),(2) the Filter Banks (FB) coefficients. The neural architecturecan recognize the isolated Arabic speech via classificationtechnique. The proposed system involves, first, extractingpertinent features from the natural speech signalusing MFCC (static and dynamic features) and FB. Next,the extracted features are padded in order to deal with thenon-uniformity of the sequences length. Then, a deep architecturerepresented by a recurrent LSTM or GRU (GatedRecurrent Unit) architectures are used to encode the sequencesofMFCC/FB features as a fixed size vector that willbe introduced to a Multi-Layer Perceptron network (MLP)to perform the classification (recognition). The proposedsystem is assessed using two different databases, the firstone concerns the spoken digit recognition where a comparisonwith other related works in the literature is performed,whereas the second one contains the spoken TVcommands. The obtained results show the superiority ofthe proposed approach.},
	url = {https://www.degruyter.com/view/j/comp.2019.9.issue-1/comp-2019-0004/comp-2019-0004.xml},
	author = {Naima Zerari and Samir Abdelhamid and Hassen Bouzgou and Christian Raymond}
}
@article {6587,
	title = {Fast Short-Term Global Solar Irradiance Forecasting with Wrapper Mutual Information},
	journal = {Renewable Energy (Elsevier)},
	volume = {133},
	year = {2019},
	pages = {1055-1065},
	abstract = {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{\textendash}10.77] than for PCA [8.4{\textendash}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{\textendash}0.128] than for PCA [0.105{\textendash}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.},
	url = {https://www.sciencedirect.com/science/article/pii/S0960148118313028},
	author = {Hassen Bouzgou and Christian A. Gueymard}
}
@conference {7560,
	title = {Auto-Encoder with Neural Networks for Wind Speed Forecasting},
	booktitle = {International Conference on Communications and Electrical Engineering (ICCEE) },
	year = {2018},
	pages = {1-5},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {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.},
	url = {https://ieeexplore.ieee.org/abstract/document/8634551},
	author = {Hatem Mezaache and Hassen Bouzgou}
}
@proceedings {3775,
	title = {Bi-directional recurrent end-to-end neural network classifier for spoken Arab digit recognition},
	journal = {2nd International Conference on Natural Language and Speech Processing (ICNLSP) IEEE},
	year = {2018},
	pages = {1-6},
	publisher = {IEEE},
	address = {Algiers},
	abstract = {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.},
	url = {https://ieeexplore.ieee.org/abstract/document/8374374/},
	author = {Naima Zerari and Samir Abdelhamid and Hassen Bouzgou and Christian Raymond}
}
@proceedings {3784,
	title = {Ensemble of Support Vector Methods to Estimate Global Solar Radiation In Algeria},
	journal = {International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES2017 (Springer)},
	year = {2017},
	pages = {1-5},
	publisher = {Springer},
	address = {Tipasa, Algeria},
	abstract = {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.},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73192-6_16},
	author = {Nahed zemouri and Hassen Bouzgou}
}
@proceedings {3778,
	title = {Intra-hour Forecasting of Direct Normal Solar Irradiance using Variable Selection with Artificial Neural Networks},
	journal = {International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES2017 (Springer)},
	year = {2017},
	pages = {1-5},
	publisher = {Springer},
	address = {Tipasa Algeria},
	abstract = {Renewable Energy Sources (RES) are one of the key solutions to handle the world{\textquoteright}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.},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73192-6_29},
	author = {Hanane Atmani and Hassen Bouzgou and Christian A. Gueymard}
}
@proceedings {3776,
	title = {Gaussian Process with Linear Discriminant Analysis for Predicting Hourly Global Horizontal Irradiance in Tamanrasset, Algeria},
	journal = {The 5th International Conference on Electrical Engineering (ICEE-B) (IEEE)},
	year = {2017},
	pages = {1-5},
	publisher = {IEEE},
	address = {Boumerdes, Algeria},
	abstract = {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.},
	url = {https://ieeexplore.ieee.org/abstract/document/8192223/},
	author = {Nahed zemouri and Hassen Bouzgou and Christian A. Gueymard}
}
@article {3485,
	title = {Minimum redundancy \&ndash; Maximum relevance with extreme learning machines for global solar radiation forecasting: Toward an optimized dimensionality reduction for solar time series},
	journal = {Solar Energy (Elsevier)},
	volume = {158},
	year = {2017},
	pages = {559-609},
	abstract = {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 {\textendash} 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.},
	url = {https://www.sciencedirect.com/science/article/pii/S0038092X17309052},
	author = {Hassen Bouzgou and Christian A. Gueymard}
}
@conference {3789,
	title = {Kernel Principal Components Analysis with Extreme Learning Machines for Wind Speed Prediction},
	booktitle = {Seventh International Renewable Energy Congress "IREC2016"},
	year = {2016},
	pages = {1-5},
	address = {Hammamet, Tunisia},
	abstract = {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.},
	url = {https://hal.inria.fr/hal-01394000/document},
	author = {Hatem Mezaache and Hassen Bouzgou and Christian Raymond}
}
@proceedings {3790,
	title = {Use of the artificial neural network and meteorological data for predicting daily global solar radiation in Djelfa, Algeria},
	journal = {International Conference on Composite Materials \& Renewable Energy Applications (ICCMREA), (IEEE) },
	year = {2014},
	pages = {1-5},
	publisher = {IEEE},
	abstract = {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.},
	url = {https://ieeexplore.ieee.org/abstract/document/6843807/},
	author = {Ouarda Assas and Hassen Bouzgou and Sabah Fetah and Mohamed Salmi and Abdelhakim  Boursas}
}
@article {3487,
	title = {Evaluation of the Hourly Global Solar Radiation on a Horizontal Plane for Two Sites in Algeria},
	journal = {Advanced Materials Research},
	volume = {925},
	year = {2014},
	pages = {641-645},
	abstract = {We present three models for the estimation of hourly global solar radiation for two sites in Algeria, namely: Djelfa (Latitude 34.68{\textdegree}N, Longitude 3.25{\textdegree}E, Altitude 1126 (m)) and Ain Bessem (Latitude 36.31{\textdegree}N, Longitude 3.67{\textdegree}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.},
	url = {https://www.scientific.net/AMR.925.641},
	author = {Mohamed Salmi and Hassen Bouzgou and Yaarub  Al-Douri and Abdelhakim  Boursas}
}
@article {3486,
	title = {A fast and accurate model for forecasting wind speed and solar radiation time series based on extreme learning machines and principal components analysis},
	journal = {Journal of Renewable and Sustainable Energy (AIP)},
	volume = {6},
	number = {1},
	year = {2014},
	pages = {013114},
	abstract = {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{\textquoteright}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.},
	url = {https://aip.scitation.org/doi/abs/10.1063/1.4862488},
	author = {Hassen Bouzgou}
}
@book {3792,
	title = {Regression with Hyperdimensional Features: Application to Chemometric Calibration},
	year = {2013},
	pages = {110},
	publisher = {LAP Lambert Academic Publishing},
	organization = {LAP Lambert Academic Publishing},
	address = {Germany},
	abstract = {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.},
	url = {https://www.amazon.fr/Regression-Hyperdimensional-Features-Application-Chemometric/dp/3659349429/ref=sr_1_1?s=english-books\&ie=UTF8\&qid=1528882981\&sr=1-1},
	author = {Hassen Bouzgou}
}
@conference {3809,
	title = {Modelling of global solar radiation in Algeria based on geographical and all climatic parameters},
	booktitle = {Deuxi{\`e}me S{\'e}minaire International sur les Energies Nouvelles et Renouvelables},
	year = {2012},
	pages = {1-5},
	address = {Ghardaia, Algeria},
	author = {Sabah Fetah and Ouarda Assas and Mohamed Salmi and Hassen Bouzgou and Abdelhakim  Boursas}
}
@mastersthesis {3793,
	title = {Advanced Methods for the Processing and Analysis of Multidimensional Signals: Application to Wind Speed},
	year = {2012},
	type = {PhD Thesis},
	url = {https://www.researchgate.net/profile/Hassen_Bouzgou/publication/316351306_Advanced_Methods_for_the_Processing_and_Analysis_of_Multidimensional_Signals_Application_to_Wind_Speed/links/58fb242da6fdccde98948c32/Advanced-Methods-for-the-Processing-and-Analysi},
	author = {Hassen Bouzgou}
}
@book {3791,
	title = {Automatic Analysis of Highdimensional Signals: Advanced Wind Speed Forecasting Techniques},
	year = {2012},
	pages = {115},
	publisher = {LAP Lambert Academic Publishing},
	organization = {LAP Lambert Academic Publishing},
	address = {Germany},
	abstract = {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.},
	url = {https://www.amazon.fr/Automatic-Analysis-Highdimensional-Signals-Forecasting/dp/3659153699},
	author = {Hassen Bouzgou}
}
@article {3488,
	title = {Multiple architecture system for wind speed prediction},
	journal = {Applied Energy (Elsevier)},
	volume = {88},
	number = {7},
	year = {2011},
	pages = {2463-2471},
	abstract = {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\&nbsp;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.},
	url = {https://www.sciencedirect.com/science/article/pii/S0306261911000559},
	author = {Hassen Bouzgou and Nabil Benoudjit}
}
@conference {3810,
	title = {Estimation de l\&rsquo;irradiation Solaire Globale dans la ville de M\&rsquo;sila (Alg{\'e}rie)},
	booktitle = {1{\`e}re Conf{\'e}rence Maghr{\'e}bine sur les Mat{\'e}riaux et l{\textquoteright}{\'e}nergie, University of Gafsa, Tunisia},
	year = {2010},
	pages = {1-4},
	address = {Gafsa, Tunisia},
	author = {Mohamed Salmi and Hassen Bouzgou and Lyamani Laissaoui}
}
@conference {3811,
	title = {A Comparison Study of Solar Energy : Application to Arabic Countries},
	booktitle = {3rd Conference of Basic Science, University of Aljabal Algharibi, Lybia},
	year = {2009},
	pages = {1-4},
	address = {Aljabal Algharibi, Lybia},
	author = {Mohamed Salmi and Hassen Bouzgou and Abdelhakim  Boursas}
}
@article {3489,
	title = {Multiple regression systems for spectrophotometric data analysis},
	journal = {Chemometrics and Intelligent Laboratory Systems (Elsevier)},
	volume = {95},
	number = {2},
	year = {2009},
	pages = {144-149},
	abstract = {
	
		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.
	

},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0169743908001937},
	author = {Nabil Benoudjit and Farid Melgani and Hassen Bouzgou}
}
@conference {3813,
	title = {Combination of Multiple Estimators for Hyperdimensional Data Analysis},
	booktitle = {Proceedings of: International Conference on Electrical Engineering Design \& Technologies, Tunisia: ICEEDT},
	year = {2007},
	pages = {1-5},
	address = { Hammamet, Tunisia},
	author = {Nabil Benoudjit and Hassen Bouzgou and Farid Melgani}
}
@mastersthesis {3814,
	title = {Development of Multiple Regression Systems for Hyperdimensional Spectral Spaces},
	year = {2006},
	type = {Magister Thesis},
	url = {https://www.researchgate.net/profile/Hassen_Bouzgou/publication/316455274_Developement_of_Multiple_Regression_Systems_for_Hyperdimensional_Spectral_Spaces/links/58ff1b9c0f7e9bcf654516ec/Developement-of-Multiple-Regression-Systems-for-Hyperdimensional-Spec},
	author = {Hassen Bouzgou}
}
