مقبول رملي - Academia.edu (original) (raw)

Papers by مقبول رملي

Electronics, 2020

Recently, off-grid renewable power generation systems have become good alternatives for providing... more Recently, off-grid renewable power generation systems have become good alternatives for providing reliable electricity at a low cost in remote areas. According to the International Renewable Energy Agency, more than half the population of Nigerian rural communities are outside the electricity coverage area. This research examines the potential application of hybrid solar photovoltaic (PV)/hydro/diesel/battery systems to provide off-grid electrification to a typical Nigerian rural village. The performance of four different hybrid systems was evaluated via techno-economic and environmental analysis, and the optimized solution was selected using the HOMER analysis tool. The simulation results revealed that a hybrid PV solar/hydro/diesel with battery storage was the optimized solution and most suitable with the least net present cost (NPC) of 963,431andacostofenergy(COE)of963,431 and a cost of energy (COE) of 963,431andacostofenergy(COE)of0.112/kWh. The results also revealed that the optimal system prevented about 77.1% of CO2 gas emission fro...

Sustainability, 2019

This paper studies in detail a systematic approach to offering a combination of conventional and ... more This paper studies in detail a systematic approach to offering a combination of conventional and renewable energy that is adaptable enough to operate in grid-connected and off- grid modes to provide power to a remote village located in Nigeria. To this aim, the HOMER pro software tool was used to model two scenarios from the on-and off-grid systems, evaluating in detail the techno-economic effects and operational behavior of the systems and their adverse impacts on the environment. The impacts of varying load demand, grid power and sellback prices, diesel prices, and solar irradiation levels on system performance were discussed. Results showed that, for both cases, the optimum design consists of a diesel generator rated at 12 kW, with a photovoltaic (PV) panel of 54 kW, a 70 battery group (484 kWh nominal capacity battery bank), and a 21 kW converter. The cost of electricity (COE) and net present cost (NPC) were in the range of 0.1/kWhto0.2180.1/kWh to 0.218 0.1/kWhto0.218/kWh and 117,598to117,598 to 117,598to273,185, respe...

Journal of Renewable and Sustainable Energy, 2017

This paper analyzes the optimum orientation and tilt angle effects on photovoltaic (PV) module pe... more This paper analyzes the optimum orientation and tilt angle effects on photovoltaic (PV) module performance in Jeddah, Saudi Arabia. This analysis will begin with the description of solar radiation and tilt angle concepts. These concepts are then used to investigate the performance of PV modules by determining the power output while varying the tilt and azimuth angles. Several azimuth and tilt angles have been analyzed for monthly, bimonthly, trimonthly, quarterly, six-monthly, and yearly periods to determine how much power output can be generated and to select the best orientation angles for the PV system. The results indicate that the highest monthly average output power of the PV is 0.225 kW, obtained in June at the 0 tilt surface of the 1-kW PV panel. However, bimonthly, trimonthly, quarterly, six-monthly, and yearly adjustments result in lower power output, with yearly adjustment giving the lowest power. Therefore, the tilt angle should always be adjusted in the shortest time possible in the interest of achieving the maximum possible power output and improving the efficiency of the PV system.

Journal of Renewable and Sustainable Energy, 2017

Energy Conversion and Management, 2015

In this paper, investigation of the performance of a support vector machine (SVM) and artificial ... more In this paper, investigation of the performance of a support vector machine (SVM) and artificial neural networks (ANN) in predicting solar radiation on PV panel surfaces with particular tilt angles was carried out on two sites in Saudi Arabia. The diffuse, direct, and global solar radiation data on a horizontal surface were used as the basis for predicting the radiation on a tilted surface. The amount of data used is equivalent to 360 days, averaged from the 5-min basis data. By solving the tilt angle equation, an optimum value of solar radiation was obtained using a tilt angle of 16°and 37.5°for Jeddah and Qassim locations, respectively. The evaluation of performance and comparison of results of ANN as well as SVM and the measured/calculated data are made on the basis of statistical measures including the root mean square error (RMSE), coefficient of correlation (CC), and magnitude of relative error (MRE). The speed of computation of the algorithms is also considered for comparison. Results indicate that for Jeddah, the CC for SVM is between 0.918 and 0.967 for training and in the range of 0.91981-0.97641 for testing while that of ANN is in the range of 0.517-0.9692 for training and 0.0361-0.0961 for testing. For Qassim, the results are even better with CC of 0.999 for training and 0.987 for testing ANN showed higher values of MRE ranging between 0.19 and 1.16 and SVM is between 0.33 and 0.51 for training and testing respectively. In terms of speed of computation, it is observed that SVM is faster than ANN in predicting solar radiation data with a lower speed of 2.15 s compared to 4.56 s for ANN during training. Moreover, SVM has lower values of RMSE indicating that it is robust and has the capability to minimize errors during computations. Therefore, SVM has significantly higher accuracy, robust during computation and is faster in predicting the radiation on the tilted surfaces in comparison with ANN.

Renewable Energy, 2015

This paper analyzes a hybrid energy system performance with photovoltaic (PV) and diesel systems ... more This paper analyzes a hybrid energy system performance with photovoltaic (PV) and diesel systems as the energy sources. The hybrid energy system is equipped with flywheel to store excess energy from the PV. HOMER software was employed to study the economic and environmental benefits of the system with flywheels energy storage for Makkah, Saudi Arabia. The analysis focused on the impact of utilizing flywheel on power generation, energy cost, and net present cost for certain configurations of hybrid system. Analyses on fuel consumption and carbon emission reductions for the system configurations were also presented in this paper.

In this paper, investigation of the performance of a support vector machine (SVM) and artificial ... more In this paper, investigation of the performance of a support vector machine (SVM) and artificial neural networks (ANN) in predicting solar radiation on PV panel surfaces with particular tilt angles was carried out on two sites in Saudi Arabia. The diffuse, direct, and global solar radiation data on a horizontal surface were used as the basis for predicting the radiation on a tilted surface. The amount of data used is equivalent to 360 days, averaged from the 5-min basis data. By solving the tilt angle equation, an optimum value of solar radiation was obtained using a tilt angle of 16° and 37.5° for Jeddah and Qassim locations, respectively. The evaluation of performance and comparison of results of ANN as well as SVM and the measured/calculated data are made on the basis of statistical measures including the root mean square error (RMSE), coefficient of correlation (CC), and magnitude of relative error (MRE). The speed of computation of the algorithms is also considered for comparison. Results indicate that for Jeddah, the CC for SVM is between 0.918 and 0.967 for training and in the range of 0.91981–0.97641 for testing while that of ANN is in the range of 0.517–0.9692 for training and 0.0361–0.0961 for testing. For Qassim, the results are even better with CC of 0.999 for training and 0.987 for testing ANN showed higher values of MRE ranging between 0.19 and 1.16 and SVM is between 0.33 and 0.51 for training and testing respectively. In terms of speed of computation, it is observed that SVM is faster than ANN in predicting solar radiation data with a lower speed of 2.15 s compared to 4.56 s for ANN during training. Moreover, SVM has lower values of RMSE indicating that it is robust and has the capability to minimize errors during computations. Therefore, SVM has significantly higher accuracy, robust during computation and is faster in predicting the radiation on the tilted surfaces in comparison with ANN.

Electronics, 2020

Sustainability, 2019

Journal of Renewable and Sustainable Energy, 2017

Energy Conversion and Management, 2015

Renewable Energy, 2015

In this paper, investigation of the performance of a support vector machine (SVM) and artificial ... more In this paper, investigation of the performance of a support vector machine (SVM) and artificial neural networks (ANN) in predicting solar radiation on PV panel surfaces with particular tilt angles was carried out on two sites in Saudi Arabia. The diffuse, direct, and global solar radiation data on a horizontal surface were used as the basis for predicting the radiation on a tilted surface. The amount of data used is equivalent to 360 days, averaged from the 5-min basis data. By solving the tilt angle equation, an optimum value of solar radiation was obtained using a tilt angle of 16° and 37.5° for Jeddah and Qassim locations, respectively. The evaluation of performance and comparison of results of ANN as well as SVM and the measured/calculated data are made on the basis of statistical measures including the root mean square error (RMSE), coefficient of correlation (CC), and magnitude of relative error (MRE). The speed of computation of the algorithms is also considered for comparison. Results indicate that for Jeddah, the CC for SVM is between 0.918 and 0.967 for training and in the range of 0.91981–0.97641 for testing while that of ANN is in the range of 0.517–0.9692 for training and 0.0361–0.0961 for testing. For Qassim, the results are even better with CC of 0.999 for training and 0.987 for testing ANN showed higher values of MRE ranging between 0.19 and 1.16 and SVM is between 0.33 and 0.51 for training and testing respectively. In terms of speed of computation, it is observed that SVM is faster than ANN in predicting solar radiation data with a lower speed of 2.15 s compared to 4.56 s for ANN during training. Moreover, SVM has lower values of RMSE indicating that it is robust and has the capability to minimize errors during computations. Therefore, SVM has significantly higher accuracy, robust during computation and is faster in predicting the radiation on the tilted surfaces in comparison with ANN.