In this work, sol—gel dip-coating technique was used to elaborate ZnO pure and ZnO/Al films. The impact of Al-doped concentration on the structural, optical, surface morphological and electrical properties of the elaborated samples was investigated. It was found that better electrical and optical performances have been obtained for an Al concentration equal to 5%, where the ZnO thin films exhibit a resistivity value equal to 1.64104 Ωcm. Moreover, highest transparency has been recorded for the same Al concentration value. The obtained results from this investigation make the developed thin film structure a potential candidate for high optoelectronic performance applications.

In this work, sol—gel dip-coating technique was used to elaborate ZnO pure and ZnO/Al films. The impact of Al-doped concentration on the structural, optical, surface morphological and electrical properties of the elaborated samples was investigated. It was found that better electrical and optical performances have been obtained for an Al concentration equal to 5%, where the ZnO thin films exhibit a resistivity value equal to 1.64104 Ωcm. Moreover, highest transparency has been recorded for the same Al concentration value. The obtained results from this investigation make the developed thin film structure a potential candidate for high optoelectronic performance applications.

In this work, sol—gel dip-coating technique was used to elaborate ZnO pure and ZnO/Al films. The impact of Al-doped concentration on the structural, optical, surface morphological and electrical properties of the elaborated samples was investigated. It was found that better electrical and optical performances have been obtained for an Al concentration equal to 5%, where the ZnO thin films exhibit a resistivity value equal to 1.64104 Ωcm. Moreover, highest transparency has been recorded for the same Al concentration value. The obtained results from this investigation make the developed thin film structure a potential candidate for high optoelectronic performance applications.

Nowadays, wind power andprecise forecasting are of great importancefor the development ofmodern electricalgrids.In this paperwepropose aprediction systemfor time seriesbased onKernelPrincipalComponent Analysis(KPCA) andExtremeLearningMachine(ELM). To compare the proposed approach, threedimensionality 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 absoluteerror (MAE), root mean squareerror (RMSE), and normalizedmean square error (NMSE). The results show that the reduction of the original input space affectspositively 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.

Nowadays, wind power andprecise forecasting are of great importancefor the development ofmodern electricalgrids.In this paperwepropose aprediction systemfor time seriesbased onKernelPrincipalComponent Analysis(KPCA) andExtremeLearningMachine(ELM). To compare the proposed approach, threedimensionality 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 absoluteerror (MAE), root mean squareerror (RMSE), and normalizedmean square error (NMSE). The results show that the reduction of the original input space affectspositively 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.

Nowadays, wind power andprecise forecasting are of great importancefor the development ofmodern electricalgrids.In this paperwepropose aprediction systemfor time seriesbased onKernelPrincipalComponent Analysis(KPCA) andExtremeLearningMachine(ELM). To compare the proposed approach, threedimensionality 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 absoluteerror (MAE), root mean squareerror (RMSE), and normalizedmean square error (NMSE). The results show that the reduction of the original input space affectspositively 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.