Original Article
Electricity Generation by Green Energy Sources
SHUBHAM GUPTA; Amit kumar singh
Abstract
This study is performed to increase the bankability and assess the future performance of solar-energy-based applications in hilly areas of India. A solar energy database for these places is necessary to achieve this. There are many obstacles to building measurement stations at these places. Significant ...
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This study is performed to increase the bankability and assess the future performance of solar-energy-based applications in hilly areas of India. A solar energy database for these places is necessary to achieve this. There are many obstacles to building measurement stations at these places. Significant problems include expensive measuring instruments, transportation, periodic calibration, and maintenance of the instruments. Utilizing satellite sources can overcome these challenges. This study uses the NASA and ISRO solar radiation databases as satellite sources. The ground station data was obtained from the Center of Energy and Environment (CEE), NIT, Hamirpur. Both overall and seasonal data analysis are performed. The concise mathematical relationship between ground station measured value and satellite radiation has a higher prediction accuracy than the other studies performed for the same study area (site). The lowest mean absolute percentage error (MAPE) obtained in this study was 7.3688%. The present study reveals that satellite solar radiation is a good alternative to developing a long-duration global solar radiation database in the hilly region of India.
Review Paper
Applications of Artificial Intelligence Studies in Renewable Energies
Hicham Ait Lhaj; Khalid Amechnoue; Rachid Herbazi
Abstract
The growing emphasis on renewable energy has led to a notable rise in photovoltaic (PV) installations, highlighting the need for advanced diagnostic methods to enable efficient maintenance and performance optimisation. This paper offers a comprehensive overview of recent developments in the use of artificial ...
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The growing emphasis on renewable energy has led to a notable rise in photovoltaic (PV) installations, highlighting the need for advanced diagnostic methods to enable efficient maintenance and performance optimisation. This paper offers a comprehensive overview of recent developments in the use of artificial intelligence (AI) for image-based fault detection and diagnosis in PV systems with a particular focus on the influence of modality and task design on the performance and real-world deployability. First, a bibliometric analysis was performed to trace the field’s evolution and structural trends, followed by an in-depth technical synthesis organized by fault type, learning paradigm, and application goal. The bibliometric data were sourced from Scopus and Web of Science (WoS). Using Bibliometrix/Biblioshiny, we observe rapid growth in publication activity, increasing collaboration, and a thematic evolution from early classification-oriented studies toward more deployment-relevant pipelines. Consequently, this review highlights persistent technical challenges as well as emerging research directions providing a structured reference for both researchers and practitioners and outlines actionable priorities for advancing reliable field-deployable inspection systems.
Original Article
Biomass Energy Sources
Charity Aghariagbonse Osagiede; Favour Uchechukwu Ugbodu
Abstract
High production costs, environmental concerns, and optimization complexities limit conventional biodiesel viability. To address this, a novel bio-based heterogeneous catalyst was synthesized by impregnating Coconut Shell BioChar (CS-BC) with copper-doped Calcined Periwinkle Shell (CPS). The morphology ...
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High production costs, environmental concerns, and optimization complexities limit conventional biodiesel viability. To address this, a novel bio-based heterogeneous catalyst was synthesized by impregnating Coconut Shell BioChar (CS-BC) with copper-doped Calcined Periwinkle Shell (CPS). The morphology and stability of the catalyst was assessed using SEM, EDX, FTIR, XRD, and BET. The catalyst was applied to transesterify an optimized blend of Waste Cooking Oil (WCO) and Animal Fat (AF). The process was modeled using Support Vector Regression (SVR), Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), then globally optimized via the Manta Ray Foraging Optimization (MRFO) algorithm. XGBoost outperformed the other models, achieving a coefficient of determination (R2) of 0.899 with the lowest error metrics. SHAP analysis identified reaction temperature as the most influential feature. The MRFO-optimized conditions were a reaction temperature of 66.3°C, catalyst loading of 6.37 wt.%, reaction time of 104.6 minutes, and a methanol-to-oil ratio of 8.84:1. Under these optimized conditions, a maximum predicted biodiesel yield of 81.9% was achieved, which closely matched the experimentally validated yield of 82.3 ± 0.8%. The synthesized catalyst demonstrated excellent stability, maintaining a 97.1% biodiesel yield after five consecutive reaction cycles, highlighting its potential for sustainable, large-scale application. Physicochemical analysis confirmed that the produced biodiesel complies with ASTM D6751 and EN 14214 standards. This integrated framework of waste-derived feedstocks, green catalysis, and metaheuristic optimization provides a sustainable, data-driven approach to biofuel production.
Review Paper
Desalinations Systems with Renewable Energies
Azizbek Rakhmonov; Saidislomkhon Tukhtasinov; Afzaljon Qodirov; Sokhibjon Komolddinov; Ikboljon Zokhidov; Ilkhombek Kholiddinov; Bekzod Boynazarov
Abstract
Water scarcity has become a major global challenge, particularly in arid and semi-arid regions where freshwater resources are limited and demand continues to increase. Renewable energy-driven desalination technologies have emerged as promising solutions for sustainable freshwater production while reducing ...
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Water scarcity has become a major global challenge, particularly in arid and semi-arid regions where freshwater resources are limited and demand continues to increase. Renewable energy-driven desalination technologies have emerged as promising solutions for sustainable freshwater production while reducing dependence on fossil fuels and minimizing environmental impacts. This review presents a comprehensive analysis of recent developments in major desalination technologies powered by renewable energy sources, including solar still systems, reverse osmosis, membrane distillation, humidification–dehumidification, multi-effect distillation, and concentrated solar desalination systems. Their operating principles, advantages, limitations, and recent technological advancements are critically discussed. Furthermore, the performance characteristics and application potential of these technologies in water-scarce regions are comparatively evaluated. Major challenges associated with large-scale implementation, such as high capital costs, membrane fouling, heat losses, and energy storage limitations, are also highlighted. In addition, emerging trends involving advanced materials, artificial intelligence, Internet of Things technologies, and hybrid desalination configurations are discussed as promising approaches for improving system efficiency and sustainability. The findings indicate that renewable energy-based desalination technologies offer significant potential for achieving long-term water security and sustainable water management in arid regions
Original Article
Tide, Wave and Hydro Power
Taitiya Kenneth Yuguda; Olugbenga Abiola Fakayode; Isaac Mashingil Mankilik
Abstract
A largely overlooked approach to expanding electricity access in rural regions while minimizing environmental harm involves retrofitting existing dams. Although upgrading these structures presents a promising alternative for power generation with reduced ecological consequences, the economic viability ...
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A largely overlooked approach to expanding electricity access in rural regions while minimizing environmental harm involves retrofitting existing dams. Although upgrading these structures presents a promising alternative for power generation with reduced ecological consequences, the economic viability of various financing models for such hydropower retrofits remains poorly understood. This study addresses that gap by modifying the Hydropower Environmental Costs Analysis Model (HECAM) to evaluate the long-term economic feasibility of dam retrofits, considering the interplay between environmental, economic, and social factors. A cost-benefit analysis of upgrading Kiri Dam for hydroelectric power generation evaluated three potential financing and ownership models: a 20 MW public-private partnership (PPP) over 20 years, a 30 MW PPP over 20 years, and a 30 MW PPP over 30 years. Among these, the 30 MW 20-year PPP option yielded the highest annual revenue from electricity sales when compared to the 20 MW 20-year and 30 MW 30-year alternatives. However, the 30 MW 30-year PPP model generated greater total annual revenue from additional reservoir services than the other two options. Sensitivity analysis suggests that, at this early phase of project development, it makes sense to explore multiple equity tranches with different payment structures. The results highlight that factors like contract duration, water flow, capacity factor, capital expenses, debt term, interest rates, and other variables should all be weighed when evaluating the costs and benefits of retrofitting existing dams. Given that hydropower projects can have long operational lifespans under favorable conditions, project developers and equity investors should prioritize longer-term contracts.
Original Article
Applications of Artificial Intelligence Studies in Renewable Energies
Hussein M. Hussein; Bahaa T. Shawky; Hamed Mohamed Abdel Barry; Ibrahim El Tantawy El Sayed
Abstract
Magnetic iron oxide nanoparticles are a promising support for immobilization of enzymatic and acid catalysts for the hydrolysis of cellulose due to their large surface area, modifiable surface, and easy magnetic separation. Here, Fe₃O₄ nanoparticles were synthesized using coprecipitation yielding ...
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Magnetic iron oxide nanoparticles are a promising support for immobilization of enzymatic and acid catalysts for the hydrolysis of cellulose due to their large surface area, modifiable surface, and easy magnetic separation. Here, Fe₃O₄ nanoparticles were synthesized using coprecipitation yielding quasi-spherical particles of 15.3±2.7 nm diameter, PDI 0.311, and zeta potential -18.7 mV, confirming moderate monodispersity and colloidal stability, and employed as support for cellulase immobilization (biocatalyst) and a core for carbon coating then sulfonation to produce a magnetically separable solid acid (Fe₃O₄@C-SO₃H). Fe₃O₄ nanoparticles were prepared by precipitation from FeCl₃.6H₂O and FeCl₂.4H₂O in a basic solution, tested for basic magnetic, color and dispersion characteristics, and then characterized using spectroscopy and microscopy before being used for the enzyme adsorption and the carbon coating/sulfonation. The immobilized cellulase on Fe₃O₄ nanoparticles was applied to the hydrolysis of carboxymethyl cellulose, while Fe₃O₄@C-SO₃H was applied to the hydrolysis of pretreated cellulose. In all cases, glucose was detected by the glucose oxidase-peroxidase method, and the reusability of the magnetic materials was tested by multiple recycling runs retaining>80% activity after 5 cycles, which demonstrated that they maintained high activity after at least five runs. Response surface methodology optimization revealed peak glucose yields of 84.93% from cellulose at pH 5, 60°C, 2.5% substrate, and 17.05 mg/g catalyst loading (R²=0.9732), approaching 91.6% of commercial enzyme performance (92.7%) with superior pH/temperature stability and recyclabilityThis study shows that straightforward Fe₃O₄-based materials can be used to prepare magnetically recoverable and reusable catalysts for the conversion of cellulose into fermentable sugars.
Original Article
Photovoltaic Systems
Muhammad Fawad Saleem; Majid Baseer; Ahmad Khan; Sardar Ahmed Imtiaz
Abstract
Net-Zero Energy Buildings (NZEBs) designed for present climatic conditions face a dual mid-century challenge: rising cooling demand increases electricity consumption, while climate-related photovoltaic (PV) losses reduce on-site generation. This study introduces the Net-Zero Divergence Factor (DF), a ...
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Net-Zero Energy Buildings (NZEBs) designed for present climatic conditions face a dual mid-century challenge: rising cooling demand increases electricity consumption, while climate-related photovoltaic (PV) losses reduce on-site generation. This study introduces the Net-Zero Divergence Factor (DF), a normalized, additive metric quantifying the combined erosion of net-zero performance over time. The framework is applied to a 511 m² office building in Karachi, Pakistan, from 2025 to 2050 under the HadCM3 A2 scenario. Climate-adjusted weather files are generated using CCWorldWeatherGen, building loads simulated in EnergyPlus v25.2, and PV generation modelled in PVSyst v7.4.7. Mid-century climate drift erodes 5.13% ± 1.50% of baseline annual load, comprising a 10.02% increase in cooling electricity (demand pressure: +2.58%) and a 2.45% reduction in PV generation (supply pressure: +2.55%). At the simulated thermal-loss coefficient (Uc = 20 W/m²K), the annual balance changes from a 3,266 kWh surplus in 2025 to a 778 kWh deficit in 2050 (DF = −0.99% ± 1.50%). Across Uc = 15–20 W/m²K, the mounting-dependent divergence ranges from −0.99% to −4.59%, while a propagated flush-mounted-plus-urban-heat-island case reaches −5.25%. Seasonal DF ranges from −29.60% in summer to +29.93% in winter, and grid-dependent operation increases from four to five months. These seasonal patterns are pronounced, although season-specific uncertainty was not propagated. The study demonstrates that annual balance alone can conceal substantial intra-annual grid dependence and supports incorporating climate-adjusted, seasonal performance indicators into NZEB design and certification. The DF is scenario-agnostic; CMIP6 SSP validation remains a priority for wider application and validation.
Original Article
Solar Thermal Engineering
Fatemeh - Ahani; Elham - Sarkardehi; Maryam - Ansarimanesh; Hamed - Moslehi
Abstract
Approximately 40% of fossil energy consumption in Iran is attributed to office and residential buildings. Therefore, optimizing energy consumption and managing energy demand in these buildings is essential. A key aspect of this optimization is the building's outer shell, which plays a vital role in heat ...
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Approximately 40% of fossil energy consumption in Iran is attributed to office and residential buildings. Therefore, optimizing energy consumption and managing energy demand in these buildings is essential. A key aspect of this optimization is the building's outer shell, which plays a vital role in heat control. One effective method for reducing energy consumption in buildings is the implementation of double-skin facades (DSFs), which have demonstrated superior performance compared to single-skin facades. Considering the high energy consumption of office buildings in Iran, this research aimed to minimize energy use in the computer center building at Shahrood University of Technology. The research methodology consisted of a literature review, field measurements, and simulation. The validity of the research data was evaluated against field measurements, and its accuracy was confirmed. Using Design Builder version 7 simulation software, twelve models of double-skin facades were analyzed and categorized into box window facades and shaft boxes, both located on the southern elevation of the computer center building. The results indicate that the double-skin facade featuring a box window reduces the building's energy consumption by 6.4% to 9.7% compared to the single-skin facade. Additionally, the double-skin facade of the shaft box also decreases energy consumption, ranging from 5.5% to 9.3% compared to the single-skin facade.
Original Article
Mojtaba Ghiyasi
Abstract
Data envelopment analysis (DEA) is a mathematical programming approach for the performance basement of the production units from a production economic view. In this paper, we analyze the performance of Iranian wind power plants (IWPP) using DEA. In the second phase of the study, we utilize the inverse ...
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Data envelopment analysis (DEA) is a mathematical programming approach for the performance basement of the production units from a production economic view. In this paper, we analyze the performance of Iranian wind power plants (IWPP) using DEA. In the second phase of the study, we utilize the inverse DEA approach as a valuable method for the future planning of the IWPPs. In this step, we considered five different scenarios from a short-run up to a long-run planning for electricity production by renewable energy. In the short-run we just need more capacity factor rather than capital. Although in the mid-run we did not presume capital extension, the result suggested this. In the end, in the long-run planning extension in not only capacity factor but also more investment in the higher technologies is a requirement for reaching the desired electricity production.
Original Article
Tide, Wave and Hydro Power
Amirhossein Farhoudi; Rouzbeh Shafaghat; Behrad Alizadeh Kharkeshi
Abstract
The performance of tidal turbines hinges on geometric characteristics and wave-current properties. This study investigates the performance of a three-bladed, 1:6 scale horizontal axis tidal turbine (HATT), with a particular focus on the underexplored influence of flow direction—a critical factor ...
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The performance of tidal turbines hinges on geometric characteristics and wave-current properties. This study investigates the performance of a three-bladed, 1:6 scale horizontal axis tidal turbine (HATT), with a particular focus on the underexplored influence of flow direction—a critical factor for bi-directional tidal sites. The effects of various variables, including pitch angle, flow direction, electrical load, and wave-current interaction under different tip speed ratios, were systematically studied. Power and moment of force coefficients (Cp and Cm) alongside their TSRs were considered as the objective functions. The wave characteristics of the Khoran Strait were scaled using Froude methodology. The Cp and Cm at the optimal pitch angle range for downstream flow do not exceed a maximum increase of 6.5% compared to the no-wave modes. When the HATT operates at its optimum variables, the power coefficient increased by 35% compared to the lowest level. However, due to the generator's characteristics, the increase in power and moment of force follows a non-linear trend. The variations in Cp and Cm at different TSRs also form a parabolic pattern. A key novel contribution of this study is the comprehensive comparison of upstream versus downstream flow configurations, revealing that the turbine's power and moment of force coefficients in the upstream flow state increase by 36.1% and 18.1%, respectively, compared to the same pitch angles in the downstream flow state, due to the absence of the nacelle impeding the current. This finding provides crucial design guidance for bi-directional tidal turbine installations.
Original Article
Wind Energy
Omid Rahaei; Mehrnam Sabbagh; Hossein Kavari Zadeh; Nahid Mahmoudi Abed
Abstract
In hot and semi-humid climates, natural ventilation plays a crucial role in enhancing indoor environmental quality. Atriums are recognized as architectural elements that facilitate daylight penetration and airflow, yet they remain underutilized in such climatic conditions. Inefficient air circulation ...
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In hot and semi-humid climates, natural ventilation plays a crucial role in enhancing indoor environmental quality. Atriums are recognized as architectural elements that facilitate daylight penetration and airflow, yet they remain underutilized in such climatic conditions. Inefficient air circulation in upper floors and uneven airflow distribution in surrounding corridors highlight the need to reconsider ventilation design strategies. This study aims to improve natural ventilation performance in atrium-adjacent corridors by exploring architectural interventions that enhance airflow patterns and thermal comfort. Independent variables included the number and arrangement of spatial elements, while dependent variables comprised airflow velocity, movement patterns, and indoor temperature. Empirical data were collected from the Central Library of Khuzestan Province between October 25 and November 25. Geometric modeling was conducted in SpaceClaim, and computational fluid dynamics (CFD) simulations were performed using ANSYS Fluent R3 2019. Among eleven design scenarios, Model 11 featuring inlet windows in stair cores and outlet vents on the second-floor ceiling demonstrated superior performance. Airflow velocity increased to 1.907 m/s, indoor temperature decreased to 302.57 K, and airflow uniformity was significantly improved. Significant improvements were also observed in outlet flux and turbulence intensity, while thermal comfort indices (PMV, PPD) confirmed acceptable comfort range according to ASHRAE standards. These interventions are cost-effective, easy to install, and applicable to existing buildings.
Research Note
Solar Thermal Engineering
Abuzar Shakeri; Farshad Farahbod; seyedeh nasrin hosseinimotlagh; Mohammad Sadegh Azadi Chegini; Abuzar Shakeri
Abstract
The escalating global demand for freshwater and the depletion of natural resources necessitate the development of sustainable desalination technologies. In this study, an improved solar desalination pond incorporating nano-ferric oxide (Fe₂O₃) plates is experimentally and theoretically investigated ...
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The escalating global demand for freshwater and the depletion of natural resources necessitate the development of sustainable desalination technologies. In this study, an improved solar desalination pond incorporating nano-ferric oxide (Fe₂O₃) plates is experimentally and theoretically investigated and compared with a conventional steel-based system. The incorporation of Fe₂O₃ nanoplates significantly enhanced solar energy absorption, thermal conductivity, and overall system efficiency. Results indicated that the modified pond achieved approximately 27% higher daily freshwater productivity under the tested operating conditions, with an average improvement of about 21% compared with the conventional configuration. Experimental performance was evaluated under representative summer operating conditions. The maximum energy and exergy efficiencies were achieved in July, whereas the minimum values were observed in January, highlighting a pronounced seasonal dependency. The maximum thermal and exergy efficiencies reached 0.53 and 0.078, respectively. The exergy efficiency was therefore approximately 85% lower than the thermal efficiency, reflecting the substantial irreversibilities inherent in low-temperature solar desalination processes. The findings highlight nano-ferric oxide as a low-cost, environmentally benign, and scalable enhancement material for next-generation solar desalination systems. This research contributes to advancing sustainable water production technologies suitable for arid and semi-arid regions.