Energy Science & Engineering最新文献

Predicting energy consumption helps countries make strategic decisions in many critical areas such as energy management, economic development, energy security, environmental sustainability and infrastructure investments. Therefore, accurate and reliable energy consumption predictions are vital to ensure the sustainability and prosperity of countries. This study aims to contribute to the proper planning of transportation policies and energy management by successfully predicting Türkiye's railway energy consumption. In this direction, energy prediction values were obtained from 18 different machine learning methods using the country's railway line length, number of passengers, freight amount and energy consumption values from 1977 to 2024. To further strengthen the results obtained with these methods, bagging, boosting, stacking and blending ensemble learning methods were utilized. With the improvements, the R-squared value was increased up to 0.9667 and energy predicting was achieved with very high accuracy. Based on the results obtained from this study, it is possible to provide investment planning more efficiently. In addition, the implementation of energy management strategies, infrastructure planning and sustainable energy policies will be provided more efficiently as a result of obtaining more successful results by using ensemble machine learning methods instead of traditional machine learning methods for energy consumption predictions in different sectors.

This comprehensive review evaluates Iraq's energy landscape, examining the spatial distribution of renewable and conventional resources, carbon emissions from power generation, and the technoeconomic viability of energy projects. Iraq's electricity generation is overwhelmingly dominated by thermal power plants, accounting for 96.6% of total production, while hydropower contributes 3.39% and solar only 0.059% of Iraq's overall electricity. Despite vast oil and gas reserves, the country faces chronic electricity shortages due to aging infrastructure, reliance on imports, and limited renewable adoption. Iraq possesses significant but underexploited renewable potential across hydropower, solar, wind, biomass, geothermal, wave, and blue energy. Hydropower remains dominant but is constrained by water scarcity and outdated infrastructure. On the other hand, solar and wind demonstrate strong technical and economic feasibility but face grid and financial barriers, while biomass and geothermal resources remain largely untapped. The energy transition is uneven, with CO₂ reductions in governorates such as Al-Muthanna and Kirkuk achieved through partial fuel switching, whereas others continue to experience rising emissions from high-carbon generation. Technoeconomic assessments underscore the competitiveness of renewables, with solar photovoltaic in Al-Nasiriyah and Al-Rutba yielding low-levelized cost of energy values of 0.033–0.035 $/kWh and high-capacity factors, and wind projects in Al-Qaim and Rawa achieving 0.025–0.05 $/kWh. By integrating Iraq's energy challenges, renewable potential, environmental trends, and technoeconomic insights, this review provides policymakers, researchers, and investors with evidence-based guidance to support strategic planning, targeted investments, and the adoption of technologies for a resilient, low-carbon, and economically sustainable energy future.

Partial shading is a significant concern that causes a current mismatch between rows, resulting in local power peaks. Dynamic reconfiguration methods may not completely eradicate the current mismatch. Hence, a battery of similar capacity injects a compensation current to nullify the current mismatch. The main limitation of this approach is the selection of a battery with a similar capacity for all the rows. To address this shortcoming, the proposed study introduces the experimental verification of the optimal section of the battery-based adaptive reconfiguration (OBAR) technique is verified on 4 × 4 total-cross-tied PV array to reduce the current mismatch. The OBAR is implemented in two steps: initially, the adaptive reconfiguration technique is performed by switching circuit 1 to reduce the current mismatch. The OBAR algorithm monitors the existence of a current mismatch; if the mismatch persists, the switching circuit 2 selects the battery of suitable capacity from a battery bank of three ranges: 0.5 Ah and 18 V, 1 Ah and 18 V, and 1.5 Ah and 18 V based on the current mismatch. The OBAR is tested experimentally, and its performance is related to that of the total cross-tied array, adaptive reconfiguration, and battery-based current mismatch reduction technique. The experimental results reveal that the battery of 0.50 Ah is the optimal selection with a power enhancement of 67% to nullify the current mismatch. The economic analysis of the OBAR indicates its viability and it can be prolonged to PV array of any size.

The growing penetration of variable wind and solar generation poses operational challenges for power grids, while the rapid adoption of electric vehicles (EV) further intensifies system load stress. This study develops an optimized time-of-use (TOU) pricing strategy that incentivizes EV owners to shift charging to off-peak periods and curtail charging during peak demand, jointly minimizing system load volatility and user charging costs. A Wasserstein GAN with gradient penalty (WGAN-GP), an enhancement over the original WGAN, is employed to synthesize high-fidelity wind–solar generation scenarios that serve as reliable inputs for tariff optimization. A moving boundary method is applied to segment EV charging demand into dynamic time periods. Building on these components, we formulate a bi-objective TOU pricing model that explicitly incorporates EV users' price responsiveness. Case studies demonstrate the superior scenario generation performance of WGAN-GP and the tangible benefits of the proposed TOU strategy. The optimized pricing achieves a 9.09% reduction in average user charging cost and a 24.11% reduction in the peak–valley load gap, thereby mitigating system stress, enhancing the operational flexibility of the virtual power plant (VPP), improving renewable (wind–solar) utilization, reducing reliance on energy storage systems (ESS), and increasing the wind–solar absorption ratio.

The research considers an hourly residential load demand with a daily average of 988 kWh/day and investigates possible standalone systems, including solar panels (photovoltaic [PV]), wind turbines (WTs), diesel generator (DG), biogenerator (BG), and battery bank (Bat), to provide the load demand, for a case study located in Tabuk, Saudi Arabia, where the monthly solar radiation and wind speed are 5.74 kWh/m²/day and 5.33 m/s, respectively. In this study, enviroeconomic factors, including inflation and discount rates, capacity shortage and load demand, CO₂ and SO₂ penalties, diesel and biomass prices are considered, while they were not considered in the previous studies in Saudi Arabia. The results show that the net present cost and cost of energy of the optimized system are $1.03 M and 0.178 $/kWh, respectively. Additionally, the prices of diesel fuel and biomass have a significant impact on the CO₂ emissions of the system, even with a 10% increase in the renewable fraction. The results of sensitivity analyses show that increasing the CO₂ emission penalty from 20 to 80 $/ton leads to a decrease in CO₂ emissions by 50%. The effect of the initial cost of WT on the configuration of the optimal system is higher than that of PV, and increasing both prices significantly leads to an increase in CO₂ emissions.

This study investigated the combustion characterization of Petroleum Motor Spirit (PMS) and Liquefied Petroleum Gas (LPG) due to fuel tanker explosions in Nigeria using Aspen Plus software. The simulation characterized and determined the emission factors (EFs) of the associated emissions from explosions. The results showed that the associated air emissions are: NO₂, NO, CO₂, CO, SO₂, H₂O_(g), sulfur particulates (S_(s)), and carbon particulate/soot (C_(s)). On average, for PMS at any tanker explosion, the EFs are: 0.00041 kg/kg (S_(s)), 9.19E–06 kg/kg (SO₂), 0.01930 (CO₂), 8.02E–11 kg/kg (C_(s)), 0.98111 kg/kg for H₂O, to 8.02E–11 kg/kg for C_(s), 5.79E–11 kg/kg (NO₂), 2.06279 kg/kg (CO) 0.98111 kg/kg (H₂O_(g) and 6.05E–05 kg/kg (NO). For LPG at any tanker explosion, the EFs are: 2.9183E–14 kg/kg for (C_(s)), 1.57505 kg/kg (H₂O_(g)), 1.10006 kg/kg (CO₂), 3.6534E-08 kg/kg (NO₂), 0.00212 kg/kg (NO), 1.22086 kg/kg (CO), and 3.6337E-05 kg/kg (SO₂). EFs of the emission would be effective tools for the stakeholders and regulatory agencies of governments for proactive actions to quantify the arrest the negative impacts of emissions that are associated with PMS and LPG tanker explosions.

This study employs a hybrid technique based on the Decision-Making Trial Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP), and Multiple Criteria Decision methods to investigate the causation and mutual influence strength among the barriers to the growth of electric vehicles in India. DEMATEL is used to discern between cause-and-effect barriers, while ANP ranks and prioritizes the various obstacles. This study gives critical insights into the linkages between these hurdles that will aid in the development of measures to promote the rise of electric cars. According to the findings, the barriers to electric car adoption include a lack of charging infrastructure, issues of fire safety, supply chain hurdles, range anxiety, and high cost of ownership. Generally, this study leads to a better understanding of the multidimensional nature of electric cars’ barriers and their interdependencies.

This study numerically investigates passive solar chimney design with emphasis on the coupled influence of geometric and thermal parameters on air inlet flow. While prior studies have examined individual factors such as window height or chimney height, their combined effects have not been sufficiently explored. A validated CFD model was employed to analyze the roles of absorber wall temperature, window area, air inlet height, and cavity width in driving the chimney effect. Results indicate that increasing the absorber wall temperature enhances the air inlet flow by up to a 1.9-fold increase, whereas enlarging the window area produces the strongest effect, with air inlet flow up to 9.2 times higher. In contrast, greater air inlet height and wider cavity width reduce air inlet flow by 2.2% to 6.1% and 29.5% to 35.2%, respectively. The optimal configuration, consisting of a 60°C absorber wall as the primary thermal parameter, 0.9 m² window area, 0.1 m air inlet height, and 0.1 m cavity width, achieves a maximum air inlet flow of 0.2785 kg/m·s. The novelty of this study lies in being the first to systematically simulate different window areas in combination with air inlet height, cavity width, and absorber wall temperature, thereby revealing the interactive effects among these parameters on air inlet flow performance. These findings provide actionable design strategies to enhance passive ventilation, reduce reliance on mechanical ventilation, and further improve building energy efficiency.

The impact of partial shading conditions on photovoltaic modules is investigated here, and a novel Master-Slave configuration is proposed to mitigate the associated performance losses. The modified method achieves the global maximum at an enhanced PV voltage and current, employing a Master-Slave setup that supplements the under-generated power of the slaves, resulting in a maximum increase of 51.9% in power output when tested at a 20% fixed partially shaded condition. Unlike conventional bypass diode (BPD) or reconfiguration methods, the proposed system ensures better voltage stability and reduces reverse bias conditions and thermal stress. Simulation results, mathematical modeling, and experimental validation are provided in this paper.

This study investigates the public acceptance of hydrogen technologies in the United Kingdom's domestic energy sector, with a focus on green and low-carbon hydrogen as a pathway to decarbonisation. The purpose is to evaluate the social, economic and perceptual factors shaping willingness to adopt hydrogen-based appliances such as boilers, hobs and complete home systems. A mixed-methods framework was employed, combining quantitative analysis including descriptive statistics, correlation matrices and regression modelling with qualitative approaches such as sentiment and thematic analysis of survey responses (n = 1213). Sentiment analysis revealed three dominant orientations: optimistic (28%), cautious (17.7%) and hopeful (16.8%). Thematic coding highlighted five central drivers and barriers: affordability, environmental impact, technological reliability, trust and broader public opinion. Regression analysis confirmed that knowledge of hydrogen strongly predicts acceptance (β = 0.28, p < 0.001 for boilers; β = 0.26, p < 0.001 for hobs), while demographic factors such as age (β = −0.099, p < 0.05) and income (β = 0.045, p < 0.05) exert smaller yet significant influences. Standard error clustering and robustness checks were applied to validate these results. The findings demonstrate that acceptance is more closely tied to attitudinal and informational factors than to demographics alone. Based on these insights, the study proposes evidence-based strategies for policymakers, including targeted public education, financial incentives and transparency-driven pilot projects. By integrating both methodological rigour and policy relevance, the paper contributes to the literature on sustainable energy transitions and outlines practical pathways for accelerating hydrogen adoption in domestic contexts.