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Abstract 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.

This paper analyzes the electricity production potential and economic viability of grid-connected wind/photovoltaic (PV) energy systems at two coastal cities, Yanbu and Dhahran in Saudi Arabia. First, wind energy is assessed based on the hourly wind speed observation data recorded over the entire year 2013 in the selected locations. Electricity generation potential is estimated using two wind turbines: Vestas V82 and V90 models. The results indicate that both locations have sufficient wind resources for wind turbine operation. Strong wind resources are more common at Dhahran than at Yanbu with wind speeds above 3.5 m/s, accounting for 60.12% of the wind data at Dhahran, which is higher than 51.2% of Yanbu. Grid-connected hybrid systems using Vestas V90 wind turbines had the highest net present cost (NPC) compared with other configurations. The inclusion of battery storage units slightly increases the NPC. Surprisingly, systems with the highest NPC produced the least electricity. In contrast, cheaper V82-based systems had the lowest NPC and levelized cost of energy and produced the most electricity. Hence, a grid-connected wind/PV system using V82 turbines is most economically viable. Incorporating a small battery storage unit in the systems minimizes capacity shortages and improves reliability at minimal extra cost. Using different wind turbines with a lower cut-in speed of 3 m/s could increase the electricity production, as 9.1% and 10.3% of wind observations at Yanbu and Dhahran, respectively, had a wind speed of 3 m/s.

Abstract Uganda is gifted by nature with abundant energy resources, mainly renewables, which can potentially provide the country with sufficient capacity to meet future growth in energy demand. Surprisingly, Uganda has one of the lowest electricity penetration levels, with only 9–12% of the total population having electricity access; 2–3% of them living in rural communities. There is multitude of challenges facing the energy sector of Uganda, forcing energy demand to always exceed the supply. For decades, hydropower has been and is still the base electricity supply of the country with a supplement of limited biomass, diesel based thermal and solar electricity. The objective of this paper is to review the potential and progress of renewable based distributed generation in Uganda. The potential of the country’s natural renewable resources and existing distributed generation is described and existing government policies are assessed. The challenges facing the energy sector and the suggested remedies are discussed. Various distributed generation systems that could be incorporated into the energy system of the country to improve renewable energy (RE) utilization and possibly contribute to the electricity needs of the population are also proposed and discussed. Exploitation of abundant RE resources through distributed energy generation around the region will not only improve the electricity needs of the country but also increase the economic welfare of the growing population.

Abstract In this paper, the potential of solar and wind energy-based distributed generation (DG) in Saudi Arabia is simultaneously analyzed with the aim of maximizing the utilization of available resources. It begins with an analysis of DG application potential for wind and solar energy resources in various regions of Saudi Arabia. The progress of DG applications in terms of research, planning, and exploitation of wind and solar energy resources is then presented. An assessment of the DG contribution to the energy sector of Saudi Arabia has been conducted and the barriers and challenges for the implementation of DG systems in the country are discussed with suggested measures to overcome the challenges. The main findings are that with the huge potential of wind and solar resources for DG applications the country has targeted 50 GW of wind and solar capacity by the year 2040. The Saudi government is expected to provide full support in the form of financial incentives for solar and wind energy projects in order to boost renewable energy development.

Abstract Distributed generation is a collective term that covers the generation of energy at micro level, distributed in a location near the end user by using renewable and nonrenewable distributed energy generation (DEG) resources including among others, solar, wind, hydro, geothermal and diesel generators. This paper presents a review on the optimization approaches for hybrid DEG systems, considering both stand-alone and grid-connected systems. There are several optimization techniques used on DEG systems, comprising of analytical and artificial intelligent (AI) and hybrid techniques. This work encompasses the selected journal papers published especially in the last five years. A brief background of the optimization approaches been highlighted, particularly identifying the most common techniques to give the basis for analysis of the approaches currently applied on hybrid DEG systems. The analysis shows that AI techniques are still dominating the techniques used for optimization of DEG systems, with particle swarm optimization (PSO) recognized as the most used AI method. The objective functions in the optimization of hybrid DEG systems are currently defined to maximize the reliability, to minimize the expected interruption cost, and to optimize operation schedule of DEG resources.

This paper discusses maximum power point tracking (MPPT) methods of PV system for normal and partial shading conditions (PSC). The selected MPPT methods were classified as artificial intelligent, hybrid, and other MPPT methods. The comparison of researches on MPPT methods under normal condition and PSC reveals that researchers have concentrated more on shading conditions since the last few years mainly due to the need of power output and efficiency improvements. It is believed that the information contained in this piece of work will be of great use for the researchers in the field under consideration.