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Abstract Transparent conducting oxide (TCO) is widely utilized in various fields of electronics and optoelectronics, including touch-screen technology, thin-film solar cells, dye-synthesized solar cells (DSSC), and more. Yet, the costs associated with TCO films can be substantial, for instance, roughly 40% of the total expenses of the dye-synthesized solar cells are due to the use of this component. Therefore, fabricating TCO using Tin oxide (SnO2) using the sol–gel dip coating method is the central theme of this study. Where emphasis has been given to attaining high performance using locally available low-cost materials in Bangladesh. Moreover, to find out the electrical and optical properties of the fabricated SnO2 thin film UV spectroscopy, energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), four-point probe measurement, and thickness measurement have been done. In addition, uniformity and cracks in the crystals, and morphological images have been observed using scanning electron microscopy (SEM). A thin layer with a thickness of 144 nm and a resistivity of 2.27 Ω-cm has been created after 16 times dipping in sol–gel solution. The findings indicate that the thin films have an average transmittance of roughly 70% in the 300–1200 nm wavelength range. Furthermore, the outcome also reveals that the thin films’ average band gap is approximately 3.533 eV. The experimentally obtained data have been included in the simulation for performance analysis. As demonstrated by the simulation, by using this TCO, a maximum efficiency of 17.525% may be attained in a CdTe solar cell. If manufactured correctly, SnO2 TCO could potentially rival traditional FTO or ITO based TCOs in solar cell research.

Solar cell simulation software offers an intuitive platform enabling researchers to efficiently model, simulate, analyze, and optimize photovoltaic devices and accelerate desired innovations in solar cell technologies. This paper systematically reviews the numerical techniques and algorithms behind major solar cell simulators reported in the literature. The status, scopes, and limitations of these simulators have been critically evaluated after recording their use in nearly two hundred published articles. For the first time, we present a comparative study of the simulators in terms of their availability, applications, and system requirements. We anticipate that this review will aid in selecting the most appropriate solar cell simulator for the numerical study of preferred type of solar cells.

Organic-inorganic lead-based perovskite solar cells (PSCs) have emerged as a promising and rapidly evolving photovoltaic technology in recent years. However, the use of high-toxic lead material in PSC is incompatible with the 12th sustainable development goal and restricts the potential for commercialization. Recently, lead-free methylammonium germanium tri-iodide (CH3NH3GeI3)-based hybrid perovskite solar cells have garnered substantial attention in the field of photovoltaics due to their remarkable combination of high efficiency. The exceptional photovoltaic performance of hybrid CH3NH3GeI3 perovskite solar cells is the central theme of this study. Where emphasis has been given to attaining high power conversion efficiency by adjusting various parameters of the CH3NH3GeI3 perovskite layer using a solar cell capacitance simulator (SCAPS-1D). Moreover, the influence on parameter changes, i.e., thickness, doping concentration, quantum efficiency, and defect density of perovskite, ETL, HTL, HTL/CH3NH3GeI3, and CH3NH3GeI3/ETL layers, has been explored. The unique FTO/ZnO/CH3NH3GeI3/Cu2O/Ni-structured perovskite solar cell demonstrates impressive simulative performance with a VOC of 1.39 V, JSC of 21.93 mA/cm2, a fill factor of 79.82 %, and an efficiency of 24.46 %. CH3NH3GeI3 is a promising candidate that can be employed as a perovskite layer, and if fabricated properly, FTO/ZnO/CH3NH3GeI3/Cu2O/Ni perovskite solar cell has the possibility to be a competitive alternative to conventional silicon-based photovoltaics.

Abstract The challenge of predicting wind speeds to facilitate site selection and the consistent operation of wind power plants in coastal regions is a global concern. The output of wind turbines is subject to fluctuations corresponding to changes in wind speed. The unpredictable characteristics of wind patterns introduce vulnerabilities to wind power facilities in wind power plants. To address this unpredictability, an effective strategy involves forecasting wind speeds at specific locations during wind power plant operations. While previous research has explored various machine learning algorithms to tackle these issues, satisfactory results have not been achieved, and Bangladesh faces challenges in this regard, especially in low-wind speed areas. This study aims to identify the most accurate machine learning-based algorithm to forecast the short-term wind speed of two areas (Kutubdia and Cox's Bazar) located on the eastern coast of Bangladesh. Wind speed data for a span of 21.5 years, ranging from January 2001 to June 2022, were sourced from two outlets: the Bangladesh Meteorological Department and the website of NASA. Wind speed has been forecasted using 14 different regression-based machine learning models with a comprehensive overview. The results of the experiment highlight the exceptional predictive performance of a boosting-based ensemble method known as categorical boosting, especially in the context of forecasting wind speed data obtained from NASA. Based on the testing data, the evaluation yields remarkable results, with coefficients of determination measuring 0.8621 and 0.8758 for wind speed in Kutubdia and Cox's Bazar, respectively. The study underscores the critical importance of prioritizing optimal turbine site selection in the context of wind power facilities in Bangladesh. This approach can yield benefits for stakeholders, including engineers and project owners associated with wind projects.

Abstract Bangladesh is one of the world’s most susceptible countries to climate change. Global warming has significantly increased surface temperatures worldwide, including in Bangladesh. According to meteorological observations, the average temperature of the world has risen approximately 1.2 °C to 1.3 °C over the last century. Researchers and decision-makers have recently paid attention into the climate change studies. Climate models are used extensively throughout the nation in studies on global climate change to determine future estimates and uncertainties. This paper outlines a perceptible stacking ensemble learning model to estimate the temperature of a tropical region—Cox’s Bazar, Bangladesh. The next day’s temperature, maximum temperature, and minimum temperature are estimated based on the daily weather database collected from the weather station of Cox’s Bazar for a period of 20 years between 2001 and 2021. Five machine learning (ML) models, namely linear regression (LR), ridge, support vector regression (SVR), random forest (RF), and light gradient boosting machine (LGBM) are selected out of twelve ML models and combined to integrate the outputs of each model to attain the desired predictive performance. Different statistical schemes based on time-lag values play a significant role in the feature engineering stage. Evaluation metrics like mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) are determined to compare the predictive performance of the models. The findings imply that the stacking approach presented in this paper prevails over the standalone models. Specifically, the study reached the highest attainable R2 values (0.925, 0.736, and 0.965) for forecasting temperature, maximum temperature, and minimum temperature. The statistical test and trend analysis provide additional evidence of the excellent performance of the suggested model.