Fonio is an underutilized cereal from West Africa, recognized for its exceptional nutritional profile and ability to thrive under climate stress. However, its processing, including drying, remains labor-intensive in rural areas. This study therefore aimed to design and evaluate the performance of a chamber-type solar dryer, considering the specific constraints of fonio drying in rural contexts. The dryer consists of a 10 m × 5 m concrete chamber with a sloped roof (3.0 m to 2.2 m). It is covered with translucent fiberglass sheets and powered by a photovoltaic system. Experimental trials were conducted in northern Benin during both rainy and dry seasons, to compare the dryer’s technical performance with traditional sun drying. Furthermore, economic and environmental assessments were carried out. Results showed that the new dryer significantly accelerated the drying process in both seasons. Fonio moisture content was reduced from 40% to below 9% (w.b.) in 4 hours during the dry season and 22 hours during the rainy season. This corresponds to a drying time reduction of 86% and 59%, respectively, compared to natural sun drying. Moreover, the dryer preserved nutrients and protected fonio from dust, insects, and other contaminants. The net annual profit was estimated at 54,224 USD (42.7% of revenue), with a payback period of 58 days. Over a 20-year lifespan, the system could mitigate 757.31 tons of CO₂ emissions, with an energy payback time of 1.27 years. The carbon credit value was estimated at 11,359.65 USD. This dryer offers a cost-effective and sustainable alternative to traditional fonio drying.
In this paper, we present a cost-effective and efficient design for a single-phase solar power inverter system aimed at converting solar energy into high-quality alternating current (AC) power. The primary objective of this design is to enable the reliable integration of renewable energy into residential and industrial electrical systems. A PIC16F876A micro-controller was employed to generate a sinusoidal pulse-width modulation (SPWM) signal, which precisely controls the operation of the H-bridge inverter circuit. This configuration allows the system to convert direct current (DC) from solar panels into a stable AC output. To further improve power quality, a lowpass LC filter was incorporated at the inverter output to eliminate high-frequency harmonics, producing a waveform that closely resembles a pure sine wave. Simulation and practical hardware testing confirm that the inverter delivers a consistent and stable AC output with minimal harmonic distortion. Due to its simplicity, scalability, and low cost, this design is highly suitable for renewable energy applications, including powering sensitive electronic equipment, off-grid systems, and energy-efficient smart homes or small industrial setups. It offers a promising solution for enhancing energy access and sustainability, particularly in regions where conventional grid connectivity is limited or unreliable.
Microbial Fuel Cells (MFCs) represent a promising biotechnology for green energy production and wastewater reclamation. These bio-electrochemical processes harness the metabolism of microbes to oxidize organic matter and generate electricity. A widespread energy crisis is the result of the current global situation, which is characterized by high energy demand and finite resources. Depletable resources are eroding rapidly, while clean energy sources remain overlooked. Thus, there is a pressing need for alternative energy-generating techniques. MFCs have received a lot of attention because of their advantageous working conditions and the availability of a wide range of environmentally safe fuel substrates. Through active breakdown of substrates by microorganisms, bioelectricity is produced, offering a durable solution to intensify energy challenges. Rigorous research has yielded new insights into fuel cells, revealing that various carbon sources, including numerous types of biomass, can be utilized proficiently. Consequently, in wastewater treatment, conversion of waste by microbes through cutting-edge bioremediation techniques, such as using MFCs, offers a potentially attractive substitute for traditional treatment processes, enabling the direct production of electricity. In addition to being in line with current technological developments, this lowers overall process costs. This review provides an overview of fundamental principles underlying MFC operation, discusses the various configurations and components, highlights their diverse applications, addresses current challenges, and outlines future research directions in the field.
This study investigates the use of lime as a buffer solution to optimize pH and temperature for the fermentation of bioethanol from elephant grass (Pennisetum purpureum), a non-edible biomass. Bioethanol is an environmentally friendly alternative to fossil fuels, produced through the fermentation of starches, sugars, or cellulose. This study emphasizes the importance of controlling pH and temperature during fermentation, noting that enzymatic hydrolysis typically requires higher temperatures than fermentation. Samples of elephant grass were incubated at different pH levels (4.0, 4.5, 5.0, 5.5, and 6.0) and temperatures (25, 35, and 40 ℃) for one week. Following acid hydrolysis using lime—which also serves as a mildly acidic substance—as the nutrient medium, fermentation was conducted using baker’s yeast, selected for its cost-effectiveness and wide availability. The ethanol yields were measured and recorded using a digital refractometer. The results were analyzed using the Gompertz method to verify the accuracy of the experimental findings. The results showed that lime not only increased ethanol production efficiency but also affected the enzymes by maximizing yeast growth at temperatures above 30 ℃, specifically 35 ℃ in this study, with a total yield of 8 % (v/v) ethanol concentration and 12 % (v/v) ethanol concentration on the peak day (day 3) at a pH of 4. These findings indicate that temperature enhances ethanol concentration, whereas higher pH levels inhibit it.
The physical characteristics of a school building have a significant impact on student learning. Daylight is one of the most important elements in architecture. Adequate lighting has a significant effect on the physical and physiological health, comfort, and performance of students. Therefore, a considerable proportion of the visual quality of educational environments depends on the quality of lighting within their spaces. Nowadays, due to population growth and the extensive demand for schools, the construction of school buildings often proceeds with minimal standards, frequently without adequate consideration of daylighting. Therefore, it is crucial to design schools with sufficient and high-quality lighting. The quality of daylight within a building is directly related to windows, which serve as the primary gateways for daylight entry. Consequently, it is essential for designers to recognize the various window parameters and the impact of each on daylight metrics, and to optimize window configurations during the early design stages. The aim of this research is to identify the physical parameters of windows and the metrics used for daylight calculation, to evaluate the interaction between these components, and ultimately to present a general template for the design of windows without shading control strategies in schools in Tehran, considering the LEED v4.1 standard. In this study, the Rhinoceros-Grasshopper software was used to simulate a total of 132 classroom models located in Tehran city (BSk Köppen climate). Subsequently, the Ladybug and Honeybee plugins were employed to investigate static and dynamic daylight metrics (DF, DA, SDA, CDA, UDI, and ASE) by modifying five main physical properties of windows (orientation, position, window-to-wall ratio, shape, and number) and analyzing their mutual impacts. In general, when the use of shading control elements is not feasible, placing windows on the north facade is more advantageous due to very low annual sunlight exposure (ASE). For both north- and south-facing windows, window position has the greatest influence on daylight performance, improving UDI by up to 62.9%. After window position, the number and shape of windows have a significant impact on the ASE and SDA metrics, which are among the most commonly evaluated daylight metrics in standards. The required window size to achieve the minimum daylight level on the south facade is nearly 50% smaller than that on the north facade. Ultimately, this paper presents 18 optimal window types for schools based on the LEED v4.1 rating system.
Nearshore wave energy is increasingly recognized as a viable source of clean power for small tropical islands. However, its practical implementation requires accurate characterization of wave conditions in shallow water. A primary challenge is translating deep-water reanalysis data into site-specific parameters suitable for engineering applications. This study introduces a physics-based approach to convert multi-year ERA5 wave data (2019–2025) into depth-adjusted conditions using a nonlinear dispersion solver at a water depth of 2.5 m. Seasonal analysis was conducted using the standard DJF, MAM, JJA, and SON groupings to capture intra-annual variability, and the framework employs standard statistical techniques, including percentile ranges and circular directional statistics, to quantify both variability and directional trends. When applied to the west side of Kodingareng Keke Island, Indonesia, the method indicates that the site is characterized by predominantly low-to-moderate sea states, with a mean wave power of 0.66 kW/m and a consistent wave direction near 205°. The depth-adjusted wavelength (19–23 m) and modal sea state (Hs = 0.3–0.7 m, Te = 3.8–4.8 s) offer clear parameters for device spacing, orientation, and preliminary power take-off (PTO) tuning. The seasonal evaluation shows that DJF and SON produce the highest energy levels, whereas the calmer MAM season provides a natural window for planned maintenance. The 90th-percentile wave height (Hs,90 = 0.86 m) serves as a practical benchmark for establishing preliminary operational limits. In summary, the proposed method delivers depth-adjusted wave information that supports early-stage planning and design of wave energy converter (WEC) systems in shallow-water tropical environments.When applied to the west side of Kodingareng Keke Island, Indonesia, the method indicates that the site is characterized by predominantly low-to-moderate sea states, with a mean wave power of 0.66 kW/m and a consistent wave direction near 205°. The depth-adjusted wavelength (19–23 m) and modal sea state (Hs = 0.3–0.7 m, Te = 3.8–4.8 s) offer clear parameters for device spacing, orientation, and preliminary power take-off (PTO) tuning. The seasonal evaluation shows that DJF and SON produce the highest energy levels, whereas the calmer MAM season provides a natural window for planned maintenance. The 90th-percentile wave height (Hs,90 = 0.86 m) serves as a practical benchmark for establishing preliminary operational limits. In summary, the proposed method delivers depth-adjusted wave information that supports early-stage planning and design of wave energy converter (WEC) systems in shallow-water tropical environments.
As demand for clean energy grows, improving PV efficiency in residential systems becomes increasingly important. While dual-axis solar tracking effectively enhances energy capture, its adoption in residential applications remains limited due to high costs, mechanical design complexity, and space constraints. This study addresses these challenges by investigating a cost-effective and compact dual-axis tracking solution suitable for residential use. In this study, an automatic, cost-effective residential dual-axis solar tracking system was designed, fabricated, and tested under two distinct climatic conditions in New Zealand. DC gearmotors and an Arduino MEGA 2560 R3-based control system, integrated with four light-dependent resistors (LDRs) for real-time sun tracking, were implemented. To enhance safety and motion control, limit switches were installed at motion boundaries, and an H-bridge motor driver enabled bidirectional movement, allowing precise tracking in both east-west and elevation directions. To assess its performance, the system was experimentally compared with a fixed PV panel on a typical sunny day in April and a semi-cloudy day in June. On a typical sunny day in April, the automatic solar tracking system produced a total power output of 49.37 W, compared to 30.97 W produced by the fixed PV panel, while on a typical semi-cloudy day in June, the total power output from the automatic solar tracking system was 30.4 W, compared to 19.28 W generated by the fixed PV panel. The results reveal that the improved tracking system demonstrated a significant energy gain of approximately 59.4% on a typical sunny day in April and 57.7% on a typical semi-cloudy day in June, compared to the fixed PV panel. The system features an energy-efficient actuation mechanism, requiring limited motor operation per day, which extends its operational lifetime and minimizes energy losses. These findings confirm that the proposed dual-axis solar tracking system is a practical, durable, and scalable solution for enhancing residential PV performance under diverse New Zealand climatic conditions. The analysis indicates that the system is both durable and economically practical, particularly when integrated into multi-panel installations.
This study explores the influence of enclosed interface spaces on energy consumption in educational buildings situated in hot-arid climates, with a particular focus on Isfahan, Iran. A comprehensive sensitivity and uncertainty analysis was conducted using Monte Carlo Simulation and Latin Hypercube Sampling, applied to critical building parameters including orientation, layout, glazing types, natural ventilation, insulation, infiltration, shading elements, construction materials, and HVAC set-point temperatures. Simulation tools such as DesignBuilder and EnergyPlus were employed to examine how these parameters affect annual heating and cooling loads. The results revealed that air infiltration rate, thermostat settings, and shading design tailored to orientation significantly impact thermal loads, with distinct seasonal variations. However, limitations emerged due to low adjusted R² values in cooling load analysis and the absence of experimental validation. Despite these limitations, the study offers a valuable framework for early-stage decision-making in sustainable building design. Further incorporation of real-world energy data and comprehensive statistical validation is recommended to enhance the reliability and applicability of the findings.
Wind is a renewable energy source and has potential for developing the Savonius wind turbine. However, the Savonius turbine typically exhibits low performance and requires further optimization. One approach to enhance its performance is through overlap modification, which improves turbine rotation efficiency. Additionally, adding a cylinder in front of the returning blade can further increase performance by reducing drag force. In this study, the turbine’s performance was enhanced using both overlap modification and an additional disturbance cylinder. The experimental setup employed an original Savonius wind turbine with a diameter and height of 0.4 m, an overlap ratio of 0.3, and a cylinder with a diameter ratio of ds/d=0.4. Various distance ratios (S/d) of 1.4, 1.7, 2.0, and 2.3 were tested under wind velocities of 5 m/s, 6 m/s, and 7 m/s. The resulting torque and power coefficients were measured. The best performance was achieved at a distance ratio of 1.7 and a wind velocity of 5 m/s, with the power coefficient (Cp) increasing by approximately 21.13% compared to the original Savonius turbine.
The growing demand for sustainable energy sources, the urgent need to mitigate global warming, and the pursuit of more cost-effective solutions have motivated the development of modern smart grids (SGs) by integrating renewable energy (RE) systems into grids and forming microgrids (MGs). The intermittent nature of RE systems demands optimization strategies. Past surveys have focused on specific parameters of an SG without considering the impact of the design on each parameter of the network. Nevertheless, this paper aims to provide a bibliometric and review analysis of past work, highlighting the integration of SG technology and RE sources. One of the key challenges is implementing SGs with RE usage and control. This review provides insights into the most effective methods and technologies for maximizing the use of RE sources in SGs. In addition, a comprehensive review of the state-of-the-art algorithms and methodologies for using RE systems in SGs is provided. Due to the issue that existing SGs depend on instant management techniques, which shows a lack of AI role in forecasting and control in this field, this work explores various methods of energy forecasting, load balancing, demand response (DR), and optimization techniques for RE sources, with a particular emphasis on artificial intelligence (AI), machine learning (ML), and advanced computational algorithms. A smart grid can be optimized by using algorithms as software controllers and communication systems as hardware optimizers. Furthermore, the results obtained from this work provide insight into cleaner, more resilient, and sustainable energy systems.
Lithium-ion batteries are essential for electric vehicles and renewable energy systems; yet, accurate battery state estimation remains critical for effective battery management systems. Although deep learning models have advanced state of health estimation, their comparative performance in accuracy, computational efficiency, and sustainability is underexplored and not discussed in detail. In this research, the proposed model achieves an optimal balance for real-time, resource-constrained BMS applications, resulting in high accuracy and superior efficiency compared to traditional models. Across four datasets, CS2_35–CS2_38, the proposed deep learning models were evaluated for predictive accuracy using evaluation metrics, including training time for efficiency and RMSE variability for generalizability. The proposed model outperformed the others, reducing RMSE by up to 26% compared to the traditional model, which exhibited consistent performance across all datasets. Furthermore, it trained 45–55% faster, reduced computational overhead by nearly half, and showed the lowest RMSE variability, indicating robust generalizability. These results highlight the proposed model as an ideal choice for resource-limited applications. By leveraging efficient models like CNN, this research advances state of health estimation while encouraging sustainable, eco-friendly BMS practices that minimize computational energy demands, aligning with Green AI principles for environmentally conscious battery management.
Solar radiation can be effectively converted into thermal energy and transferred to working fluids such as water or air for various residential and industrial applications. This study presents an enhanced approach for improving the performance of flat-plate solar thermal collectors in Kirkuk, Iraq, by integrating two adjustable reflective mirrors. These reflectors were used to concentrate both direct and diffuse solar radiation onto the collector surface, thereby increasing the overall absorbed energy. Experimental evaluations were conducted over four months: December 2024 and January, February, and March 2025. During December, the upper reflector angle was varied throughout the day to maximize incident radiation, while the lower reflector remained fixed at θ = 5°. The optimal monthly upper mirror angles were determined to be 25°, 22°, 20°, and 18° for December through March, respectively. The results demonstrated significant improvements in thermal efficiency: 9.21% in December, 32.05% in January, 8.02% in February, and 66.66% in March compared with the conventional collector. The relatively lower efficiency gains observed in December and February were attributed to adverse weather and reduced solar intensity during those periods.
The environmental degradation resulting from the use of non-renewable fuels requires immediate remedies; therefore, sustainable energy sources must be employed. Hydrogen, with net zero carbon emissions, is a crucial sustainable energy resource. It is flexible for use in both portable and stationary systems, providing an advantage over other sustainable fuels. This work presents current knowledge regarding hydrogen, including its classifications, life cycle, production methods, electrolyser technologies, and associated costs. Green hydrogen faces numerous economic and technological obstacles that impede its viable widespread adoption. The cost of green hydrogen significantly exceeds that of grey hydrogen, necessitating a reduction in price to enable competition with grey hydrogen for substitution purposes. Estimates indicate future costs of green hydrogen at $1–2/kg and electrolysers at $400–500/kW. This study addresses the issues associated with the extensive utilization of green hydrogen, including the need for inexpensive renewable electricity and reduced electrolyser prices. The cost of renewable electricity has decreased over the years, with wind energy being less expensive than solar energy. The cost of hydrogen should be assessed at the point of production due to its significant dependence on feedstock pricing. Proton exchange membranes (PEMs) appear to be the forthcoming technology for green hydrogen production. Green hydrogen also encounters significant obstacles in storage, transportation, and utilization, as the supply chain lacks adequate infrastructure. This paper focuses on the current state and challenges facing hydrogen for its widespread commercial use. Future research should include substantial reductions in hydrogen cost, advanced electrolyser technologies, hydrogen utilization in engines and NOx reduction during combustion, advanced fuel cells, biological production of hydrogen, and overcoming all hazards associated with hydrogen use due to its properties. These developments would be favorable for all stakeholders, particularly researchers confronting numerous complex subjects. Policymakers should also be actively engaged.
Household organic waste is often improperly discarded, leading to unpleasant odors and negative environmental and aesthetic impacts. Furthermore, its decomposition releases greenhouse gases into the atmosphere, exacerbating climate-related concerns. This study focused on developing a fixed-dome digester system for biogas production from household organic waste, particularly kitchen food waste, as a sustainable waste management solution. The digester is a cylindrical vessel with a diameter of 0.8 m and a height of 1.8 m. The fermentation process utilized fresh cow manure as the initial feedstock. An initial batch of approximately 190 kg of cow manure was added at the beginning of the experiment. Following a 5-day initial period, 3 kg of food waste was added every 3 days, and both stirring and non-stirring methods were demonstrated. The experiment found that the stirred process produced biogas with approximately 13.92% more methane (CH₄) compared to the unstirred process. Over the entire 64-day fermentation period, the biogas from both stirred and unstirred processes contained CH₄ at concentrations ranging from 64.92% to 72.06%, while carbon dioxide (CO₂), oxygen (O₂), and hydrogen sulfide (H₂S) concentrations ranged from 29.70% to 28.26%, 1.53% to 1.89%, and 3.73 to 18.3 ppm, respectively. Additionally, the biogas produced had the potential to reduce cooking energy costs by approximately 0.19 USD (6.37 THB) per day. Furthermore, investing in a fixed-dome biogas digester yielded a return on investment (ROI) of 58.0% and achieved a break-even point within approximately 8 months.
Optimization of the in situ esterification of rubber seed was investigated in a batch process. Rubber seed is a non-edible seed containing a high vegetable oil content (54.64 ± 1.80%), and therefore, it does not compete with the food sector. The oil in rubber seeds, however, contains a substantial level of free fatty acids, approximately 10.4%. The oil recovery processing step can be reduced by applying in situ esterification. The use of n-hexane as a cosolvent can facilitate oil extraction. Consequently, in situ methanolysis-esterification of rubber seed with n-hexane as a cosolvent was employed to produce a high-yield, low-acid-number methyl ester, which was optimized using a central composite design of the response surface method. The acid number was determined by AOCS Cd 3d-63 titration, and the methyl ester composition was verified using Gas Chromatography-Mass Spectrometry analysis. Optimization of rubber seed in situ esterification resulted in a maximum yield of 89.92 ± 0.99% and a low acid number of 0.45 mg KOH/g at a solution volume of methanol and n-hexane to rubber seed mass ratio of 7:1 mL/g, a methanol volume fraction of 0.44 in the blended methanol-n-hexane solution, and 12.37 wt.% H2SO4 for a reaction time of 5 hours. The methyl ester yield increased and the acid number decreased with increasing reaction time up to 5 hours. Beyond this duration, extending the reaction time did not significantly enhance the methyl ester yield or reduce the acid number. The obtained methyl ester properties complied with the SNI-7182 standard.
Numerous green energy resources, including solar, wind, bio, and hydropower, have garnered significant attention as effective alternative energy sources. Particularly beneficial to society and the economy, solar photovoltaic systems (SPVS) are the most preferred resource. Unfortunately, because of shadowing situations and fluctuating loads, these systems are unable to maximize power extraction under changeable irradiance. Many Lower Peak Power Points (LPPPs) and Global Peak Power Points (GPPPs) on their power voltage characteristics (P-VC) arise as a result of PSC. Therefore, these systems employ Maximum Power Point Tracking (MPPT) approaches. This work implements and experimentally evaluates two supervised learning MPPT schemes, Support Vector Regression (SVRT) and Linear Regression Based Technique (LRBT), for stand-alone photovoltaic systems under partial shading, using an inverse SEPIC converter. The main novelty is a hardware-aware, real-time evaluation of a computationally light LRBT MPPT on an inverse SEPIC topology, and a comparative analysis against SVRT on metrics relevant to practical deployment, including computational complexity, tracking time, output power or current, and tracking efficiency, under realistic partial shading conditions. Unlike prior ML studies that rely on simulation or heavy models, LRBT demonstrates fast convergence and very low computational cost suitable for microcontroller implementation. In MATLAB/Simulink experiments on a 2×2 PV array and inverse SEPIC converter, LRBT achieves a mean tracking efficiency of 98.3% (±0.25%), reduces tracking time to approximately 0.10 s (variance 0.0008 s), and improves delivered power by about 2.0–3.0% relative to SVRT under the tested shading patterns. LRBT’s model size and prediction speed make it significantly more suitable for low-cost real-time hardware compared to SVRT.
In this research, ZIF-67 was used for CO2 capture and its separation from flue gas. To improve the CO2 adsorption capacity, the synthesis factors of ZIF-67 were optimized. For this purpose, the molar ratios of salt to ligand, salt to solvent, and the synthesis temperature were investigated. The optimum sample, synthesized at room temperature, was prepared with a 1:4:741 molar ratio corresponding to salt, ligand, and solvent, respectively. After comparing the textural properties of the samples, the highest surface area (2285 m2/g) and the lowest pore diameter (1.19 nm) were obtained for the optimum sample, showing good performance for CO2 adsorption. The optimized ZIF-67 adsorbed 0.94 mmol/g of CO2 at 293 K and 1 bar. Furthermore, the sample was used for N2 adsorption at different temperatures, and the highest CO2/N2 selectivity was measured as 5.86 at 313 K. The synthesized ZIF-67 retained more than 93% of its efficiency after five adsorption–desorption cycles, which is a suitable feature for industrial applications in CO2 capture.
The solar chimney power plant (SCPP) is a thermal power source that utilizes solar energy, which can be converted into thermal energy in the solar collector. It uses a turbine generator to convert the generated thermal and kinetic energy into electrical energy. Business Intelligence (BI) tools have increasingly been applied to improve the operation and efficiency of SCPP systems. This study examines the feasibility of solar chimneys that operate on the basis of renewable energy resources to provide sustainable energy alternatives. For the analysis, load data are examined, solar radiation is estimated, the minimum and maximum power generation are determined, and the relationships are visualized using the Power BI tool. The data were analyzed and visualized using tools such as Power BI. The analysis determined the maximum and minimum power generation and examined the impact of weather conditions on production. In this study, the results showed that the maximum output power occurs at a collector diameter of 30 m and a chimney height of 10 m. The monthly average output power produced is 45 kW in June over a year. The novelty of this research lies in the application of BI-driven analytics to renewable energy systems, providing a modern, scalable approach to monitoring and optimizing SCPP performance. Additionally, the study investigates the influence of key design parameters, specifically chimney height and collector diameter, on output power, offering insights that can guide the enhancement of solar chimney design and operational efficiency.
Understanding and mitigating household carbon emissions is crucial in the global fight against climate change, as the residential sector represents a significant source of greenhouse gas emissions. This issue is particularly critical in developing regions experiencing rapid urbanization and evolving consumption patterns. This study presents a comprehensive assessment of household carbon footprints in Sulaymaniyah, Kurdistan Region, Iraq, based on data from 412 households collected through structured questionnaires covering demographics, energy use, transportation, and waste management practices. The results indicate that only 11.7% of household energy consumption is derived from clean sources, while heating oil accounts for 62.8% of total energy use. Annual carbon emissions per household range from 6 to 151 tons, with an average of 40.65 tons, which is nearly double the national average for Iraq (22.35 tons) and the global average (21.86 tons). These findings underscore the urgent need for targeted environmental policies in the region, with an emphasis on transitioning to cleaner energy sources, improving waste management practices, and promoting more sustainable transportation systems. By identifying the primary contributors to household emissions, this research provides a data-driven foundation for policymakers to develop effective mitigation strategies that support climate resilience and sustainable development in Sulaymaniyah. The findings of this study can also directly inform the design of public awareness campaigns aimed at promoting energy conservation and sustainable lifestyles among residents. Furthermore, the data-driven framework established in this study can be adapted for continuous monitoring and evaluation of the environmental impacts of future policies in the region.
In this paper, we present a comprehensive comparative analysis of multilevel inverter (MLI) architectures, examining their operating principles, design variations, and performance characteristics with a focus on renewable energy integration. Specifically, we analyze representative topologies, including cascaded H-bridge, neutral-point-clamped, and flying-capacitor inverters, to highlight their distinct advantages and limitations. These MLIs synthesize AC output from multiple DC voltage levels, significantly enhancing power quality and reducing total harmonic distortion (THD) compared to conventional two-level (square-wave) inverters. However, these benefits come at the cost of greater circuit complexity, higher component counts, and the need for advanced control strategies, which may impact system reliability. Despite these challenges, MLIs are increasingly adopted in solar and wind energy systems as well as medium- and high-voltage applications because of their superior ability to handle fluctuating power outputs. Conversely, traditional two-level inverters continue to be used for their simplicity and low cost, although they often underperform in demanding scenarios. Through systematic evaluation of these trade-offs, our study provides critical insights to assist engineers in selecting the most appropriate inverter topologies. This work not only serves as a detailed resource for practitioners but also identifies gaps and future research directions, thereby paving the way for more sustainable and efficient power conversion technologies.
