This paper proposes computational models to investigate the effects of dust and ambient temperature on the performance of a photovoltaic system built at the Hashemite University, Jordan. The system is connected on-grid with an azimuth angle of 0° and a tilt angle of 26°. The models have been developed employing optimized architectures of artificial neural network (ANN) and extreme learning machine (ELM) models to estimate conversion efficiency based on experimental data. The methodology of building the models is demonstrated and validated for its accuracy using different metrics. The effect of each parameter was found to be in agreement with the well-known relationship between each parameter and the predicted efficiency. It is found that the optimized ELM model predicts conversion efficiency with the best accuracy, yielding an R² of 91.4%. Moreover, a recommendation for cleaning frequency of every two weeks is proposed. Finally, different scenarios of electricity tariffs with their sensitivity analyses are illustrated.

In this study, the technical and economic feasibility of employing pumped hydroelectric energy storage (PHES) systems at potential locations in Jordan is investigated. In each location, a 1 MW_p off-grid photovoltaic (PV) system was installed near the dam reservoir to drive pumps that transfer water up to an upper reservoir at a certain distance and elevation. PVsyst (Version 7.3.4) is implemented to simulate the water flow rate pumped to the upper reservoir at each location. The water in the upper reservoir is presumed to flow back into the dam reservoir through a turbine during peak hours at night to power a 1 MW load. Based on the water volume in the upper reservoir, the power generated through the turbine was estimated using HOMER Pro^® (Version 3.15.3), and the power exported to the grid (when the power generated from the turbine is more than the power required by the driven load) was also determined. It is worth mentioning that scaling up the size of PV and hydropower systems is a straightforward approach considering the modular nature of such systems. However, the quantity of water in the dam reservoir that is allowed to be pumped is the main determinant for the size of a PHES system. The technical and economic results show that the potential of employing these locations to implement PHES systems is great. In addition, a study was conducted to estimate how much CO₂ emissions were reduced by generating renewable energy compared to generating the same amount of energy from fossil fuels. These systems increase renewable energy in the energy mix in Jordan, stabilize the grid, and balance the loads, especially during peak periods. More importantly, PHES systems contribute to making the energy sector in Jordan more sustainable.

An energy harvester composed of a microcantilever beam with a tip mass and a fixed electrode covered with an electret layer is investigated when subject to an external harmonic base excitation. The tip mass and fixed electrode form a variable capacitor connected to a load resistance. A single-degree-of-freedom model, derived based on Newton’s and Kirshoff’s laws, shows that the tip mass displacement and charge in the variable capacitor are nonlinearly coupled. Analysis of the eigenvalue problem indicates the influence of the electret surface voltage and electrical load resistance on the harvester linear characteristics, namely the harvester coupled frequency and electromechanical damping. Then, the frequency–response curves are obtained numerically for a range of load resistance, electret voltage and base excitation amplitudes. A softening nonlinear effect is observed as a result of decreasing the load resistance and increasing the electret voltage. It is found that there is an optimal electret voltage with the highest harvested electrical power. Below this optimal value, the bandwidth is very small, whereas the bandwidth is large when the electret voltage is above this optimal value. In addition, it is noted that for a certain excitation frequency, the harvested power decreases or increases as a function of electrical load resistance when the coupled frequency is closer to short- or open-circuit frequency, respectively. However, when the coupled frequency is between the short-circuit and open-circuit frequencies, the harvested power has an optimal resistance with the highest power. Increasing the excitation amplitude to raise the harvested power could be accompanied with dynamic pull-in instability and/or softening behavior depending on the electrical load resistance and electret voltage. However, large softening behavior would prevent the pull-in instability, increase the level of the harvested power, and broaden the bandwidth. These observations give a deeper insight into the behavior of such energy harvesters and are of great importance to the designers of electrostatic energy harvesters.

The nature of solar radiation and the high penetration of photovoltaic (PV) systems in the smart electrical grid necessitate the development of models driven by historical operational data capable of precisely estimating the performance of PV systems. In this work, six proposed models are applied to predict the conversion efficiency of a 7.98 kWp rooftop on-grid PV system in Jordan. The dataset comprises 179 daily samples obtained during a single spring–summer period (17 March–24 September 2014). The efficiency modeled is the combined efficiency of the modules and inverter as a system. The proposed models are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), Gaussian Process Regression (GPR), and Elastic Net (EN). The effectiveness of these proposed models is assessed by calculating four performance metrics, namely, the Mean Square Error, prediction accuracy, Coefficient of Determination (R²), and adjusted R², and benchmarking the results with those of six prediction models discussed in our previous work. The results from the unweighted Decision-Making Matrix show that RF showed the best overall performance among the proposed and benchmark models considered. By contrast, the SVM, DT, and GB models exhibited moderate predictive behavior. However, Elastic Net is the worst-performing model among the 12 proposed and benchmark models discussed in this work. Moreover, the RF model’s consistently low prediction error supports its practical utility for PV system performance estimation, despite a slightly higher training cost than simpler models.

Hydrogen Fuel Cell Electric Buses (FCEBs) are a promising solution for decarbonizing public transport. Their operational feasibility in developing countries depends on hydrogen-supply costs and infrastructure readiness. The Ministry of Energy has reported a 400% increase in imported natural-gas costs due to current geopolitical tensions between the United States and Iran, exposing the strategic vulnerability of relying on imported diesel for public transport. This study assesses the operational hydrogen demand, fuel-cost implications, and avoided diesel tailpipe carbon dioxide (CO₂) emissions associated with gradually integrating FCEBs into Jordan’s Bus Rapid Transit (BRT) project. The baseline system consists of 64 diesel buses covering about 11 million kilometers annually, consuming around 3 million liters of diesel and emitting roughly 8108.3 tons of CO₂. Fleet transition scenarios are assessed for 2030, 2035, and 2040, with FCEB-integration ratios of 10%, 15%, and 25%. Under the reference assumption of equivalent-duty replacement, avoided diesel tailpipe CO₂ emissions increase with the diesel service displaced by FCEBs, reaching 25.7% at the highest penetration level. At the $6/kg reference hydrogen-price input, FCEB integration results in higher operational fuel costs than diesel-only operation. Under the constant diesel-price reference, mixed-fleet operation becomes cost-competitive at hydrogen prices of $1/kg and $2/kg, with operational fuel-cost savings of up to 16.97%. Within the operational fuel-cost boundary of this study, mixed-fleet competitiveness is influenced by both the assumed hydrogen fuel price and diesel-price trajectory. The results support gradual deployment, subject to the greenhouse-gas intensity of the hydrogen-production and delivery pathway and the readiness of hydrogen supply and depot infrastructure.