• Energy Research

Minimizing the photon losses by depositing an anti-reflection layer can increase the conversion efficiency of the solar cells. In this paper, the impact of anti-reflection coating (ARC) for enhancing the efficiency of silicon solar cells is presented. Initially, the refractive indices and reflectance of various ARC materials were computed numerically using the OPAL2 calculator. After which, the reflectance of SiO2,TiO2,SiNx with different refractive indices (n) were used for analyzing the performance of a silicon solar cells coated with these materials using PC1D simulator. SiNx and TiO2 as single-layer anti-reflection coating (SLARC) yielded a short circuit current density (Jsc) of 38.4 mA/cm2 and 38.09mA/cm2 respectively. Highest efficiency of 20.7% was obtained for the SiNx ARC layer with n=2.15. With Double-layer anti-reflection coating (DLARC), the Jsc improved by ∼0.5 mA/cm2 for SiO2/SiNx layer and hence the efficiency by 0.3%. Blue loss reduces significantly for the DLARC compared with SLARC and hence increase in Jsc by 1 mA/cm2 is observed. The Jsc values obtained is in good agreement with the reflectance values of the ARC layers. The solar cell with DLARC obtained from the study showed that improved conversion efficiency of 21.1% is obtained. Finally, it is essential to understand that the key parameters identified in this simulation study concerning the DLARC fabrication will make experimental validation faster and cheaper.

The rapidly increasing use of renewable energy resources in power generation systems in recent years has accentuated the need to find an optimum and efficient scheme for forecasting meteorological parameters, such as solar radiation, temperature, wind speed, and sun exposure. Integrating wind power prediction systems into electrical grids has witnessed a powerful economic impact, along with the supply and demand balance of the power generation scheme. Academic interest in formulating accurate forecasting models of the energy yields of solar energy systems has significantly increased around the world. This significant rise has contributed to the increase in the share of solar power, which is evident from the power grids set up in Germany (5 GW) and Bavaria. The Spanish government has also taken initiative measures to develop the use of renewable energy, by providing incentives for the accurate day-ahead forecasting. Forecasting solar power outputs aids the critical components of the energy market, such as the management, scheduling, and decision making related to the distribution of the generated power. In the current study, a mathematical forecasting model, optimized using differential evolution and the particle swarm optimization (DEPSO) technique utilized for the short-term photovoltaic (PV) power output forecasting of the PV system located at Deakin University (Victoria, Australia), is proposed. A hybrid self-energized datalogging system is utilized in this setup to monitor the PV data along with the local environmental parameters used in the proposed forecasting model. A comparison study is carried out evaluating the standard particle swarm optimization (PSO) and differential evolution (DE), with the proposed DEPSO under three different time horizons (1-h, 2-h, and 4-h). Results of the 1-h time horizon shows that the root mean square error (RMSE), mean relative error (MRE), mean absolute error (MAE), mean bias error (MBE), weekly mean error (WME), and variance of the prediction errors (VAR) of the DEPSO based forecasting is 4.4%, 3.1%, 0.03, −1.63, 0.16, and 0.01, respectively. Results demonstrate that the proposed DEPSO approach is more efficient and accurate compared with the PSO and DE.

Green roofs are increasingly recognised as a crucial urban solution, addressing climate change, enhancing energy efficiency, and promoting sustainable architecture in densely populated areas. In this manuscript, the research study delves into the influence of green roofs on energy consumption, focusing on the Treasury Place building in Melbourne, Australia. The utilisation of DesignBuilder and EnergyPlus simulations was explored. Various green roof parameters such as the Leaf Area Index (LAI), plant height, soil moisture, and tree coverage were optimised and compared against base case scenarios. The key findings indicate an optimal LAI of 1.08 for maximum energy savings, with diminishing returns beyond an LAI of 2.5. The soil moisture content was most effective, around 50%, while a plant height of approximately 0.33 m optimised energy reduction. The introduction of 50% canopy tree coverage provided temperature regulation, but increased soil moisture due to trees and their influence on wind flow had an adverse energy impact. These results emphasise the necessity for precise green roof representation and parameter optimisation for maximum energy efficiency. This research offers essential insights for those in urban planning and building design, endorsing green roofs as a pivotal solution for sustainable urban environments.

Expeditious urbanization and rapid industrialization have significantly influenced the rise of energy demand globally in the past two decades. Solar energy is considered a vital energy source that addresses this demand in a cost-effective and environmentally friendly manner. Improving solar cell efficiency is considered a prerequisite to reinforcing silicon solar cells’ growth in the energy market. In this study, the influence of various parameters like the thickness of the absorber or wafer, doping concentration, bulk resistivity, lifetime, and doping levels of the emitter and back surface field, along with the surface recombination velocity (front and back) on solar cell efficiency was investigated using PC1D simulation software. Inferences from the results indicated that the bulk resistivity of 1 Ω·cm; bulk lifetime of 2 ms; emitter (n+) doping concentration of 1×1020 cm−3 and shallow back surface field doping concentration of 1×1018 cm−3; surface recombination velocity maintained in the range of 102 and 103 cm/s obtained a solar cell efficiency of 19%. The Simulation study presented in this article allows faster, simpler, and easier impact analysis of the design considerations on the Si solar cell wafer fabrications with increased performance.

Abstract The static, clean and movement free characteristics of solar energy along with its contribution towards global warming mitigation, enhanced stability and increased efficiency advocates solar power systems as one of the most feasible energy generation resources. Considering the influence of stochastic weather conditions over the output power of photovoltaic (PV) systems, the necessity of a sophisticated forecasting model is increased rapidly. A genetic algorithm-based support vector machine (GASVM) model for short-term power forecasting of residential scale PV system is proposed in this manuscript. The GASVM model classifies the historical weather data using an SVM classifier initially and later it is optimized by the genetic algorithm using an ensemble technique. In this research, a local weather station was installed along with the PV system at Deakin University for accurately monitoring the immediate surrounding environment avoiding the inaccuracy caused by the remote collection of weather parameters (Bureau of Meteorology). The forecasting accuracy of the proposed GASVM model is evaluated based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Experimental results demonstrated that the proposed GASVM model outperforms the conventional SVM model by the difference of about 669.624 W in the RMSE value and 98.7648% of the MAPE error.

The rapid advancement of technology, including the internet of things (IoT), industry 4.0, and smart cities, revealed an excess need for career-ready graduates. It is expected that a career-ready graduate is technically competent and possess professional skills acquired via the experiential learning incorporated into the curriculum. But the gap exists with the learners understanding of requirements and opportunities associated with graduate employability. In this research, we focus on evaluating the learners’ experiences, expectations, and perceptions of graduate employability in an engineering curriculum. In this research, the interpretations of students on the graduate employability and the extent of influence that exists based on the learning outcomes of the graduate course are examined. The gaps between the academic environment and graduate employability awareness are highlighted. Later, a national language processing-based sentiment analyzer is used to evaluate the student’s perceptions. Results from the analysis portrayed that the different levels of expectation and experiences that prevailed in the graduate course based on the conceptual idea of graduate employability need substantial focus in future curriculum development.

The vibrant, noiseless, and low-maintenance characteristics of photovoltaic (PV) systems make them one of the fast-growing technologies in the modern era. This on-demand source of energy suffers from low-output efficiency compared with other alternatives. Given that PV systems must be installed in outdoor spaces, their efficiency is significantly affected by the inevitable complication called partial shading (PS). Partial shading occurs when different sections of the solar array are subjected to different levels of solar irradiance, which then leads to a multiple-peak function in the output characteristics of the system. Conventional tracking techniques, along with some nascent/novel approaches used for the tracking maximum power point (MPP), are unsatisfactory when subjected to PS, eventually leading to the reduced efficiency of the PV system. This study aims at investigating the use of the bat algorithm (BA), a nature-inspired metaheuristic algorithm for MPP tracking (MPPT) subjected to PS conditions. A brief explanation of the behavior of the PV system under the PS condition and the advantages of using BA for estimating the MPPT of the PV system under PS condition is discussed. The deployment of the BA for the MPPT in PV systems is then explained in detail highlighting the simulation results which verifies whether the proposed method is faster, more efficient, sustainable and more reliable than conventional and other soft computing-based methods. Three testing conditions are considered in the simulation, and the results indicate that the proposed technique has high efficiency and reliability even when subjected to an acute shading condition.