Visualising Climate Change using Extended Reality: A Review

AlQallaf, Noor; Bhatti, Satyam; Suett, Rachael; Aly, Sherif G.; Khalil, Ahmed S. G.; Ghannam, Rami

doi:10.1109/icecs202256217.2022.9970808

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…Empirical evidence of the effectiveness of VR in changing environmental attitudes has been reported in the literature [26,27]. For example, Markowitz et al in [28] assessed the effectiveness of an immersive VR environment as a teaching tool to elucidate climate change effects, particularly ocean acidification.…”

Section: Background and Motivationmentioning

confidence: 99%

Empathy, Education, and Awareness: A VR Hackathon’s Approach to Tackling Climate Change

AlQallaf,

Elnagar,

Aly

et al. 2024

Sustainability

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Climate change education is crucial for fostering informed and engaged future generations. However, traditional pedagogies often fail to engage learners fully and provide real-world, experiential learning. This paper presents a novel approach to climate change education through a three-day virtual reality (VR) hackathon. The hackathon focused on four United Nations (UN) Sustainable Development Goals (SDGs)—Quality Education, Affordable and Clean Energy, Sustainable Cities and Communities, and Climate Action. Using VR technology and game design software, engineering students worked in teams. They competed against each other in designing immersive environments that demonstrated their understanding of these SDGs and climate change. Our goal was to encourage the development of empathy, education, and awareness around these critical global issues. The hackathon also integrated authentic assessments, mirroring real-world engineering tasks and providing a more practical and relevant learning experience. Our findings suggest that this VR hackathon has significantly enhanced students’ understanding of the SDGs and climate change issues, their competency with VR technologies, as well as their teamwork and problem-solving skills. This paper discusses the hackathon’s design, implementation, and outcomes, highlighting the potential of such innovative approaches in tackling climate change education and awareness.

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Section: Background and Motivationmentioning

confidence: 99%

Empathy, Education, and Awareness: A VR Hackathon’s Approach to Tackling Climate Change

AlQallaf,

Elnagar,

Aly

et al. 2024

Sustainability

Self Cite

View full text Add to dashboard Cite

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A Machine Learning Frontier for Predicting LCOE of Photovoltaic System Economics

Bhatti,

Khan,

Zoha

et al. 2024

Adv Energy and Sustain Res

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In this research article, the objective is to determine the return on investment (ROI) of photovoltaic (PV) power plants by employing machine learning (ML) techniques. Special focus is done on the levelized cost of electricity (LCOE) as a pivotal economic parameter crucial for facilitating economic decision‐making and enabling quantitative comparisons among different energy generation technologies. Traditional methods of calculating LCOE often rely on fixed singular input values, which may fall short in addressing uncertainties associated with assessing the financial feasibility of PV projects. In response, a dynamic model that integrates essential demographic, energy, and policy data, is introduced encompassing factors such as interest rates, inflation rates, and energy yield, which are anticipated to undergo changes over the lifetime of a PV system. This dynamic model provides a more accurate estimation of LCOE. The comparative analysis of ML algorithms indicates that the auto‐regression integration moving average (ARIMA) model exhibits a high accuracy of 93.8% in predicting consumer electricity prices. The validation of the model is highlighted through two case studies in the United States and the Philippines underscores the potential impact on LCOE values. For instance, in California, LCOE values could vary by nearly 30% (5.03 cents kWh−1 for singular values vs 7.09 cents kWh−1 using our ML model), influencing the perceived risk or economic feasibility of a PV power plant. Additionally, the ML model estimates the ROI for a grid‐connected PV plant in the Philippines at 5.37 years, in contrast to 4.23 years using traditional methods.

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Computational Optimization for CdS/CIGS/GaAs Layered Solar Cell Architecture

Bhatti,

Manzoor,

Zoha

et al. 2024

Energies

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Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance compared to those utilizing cadmium sulfide (CdS). Likewise, CIGS-based devices are more efficient according to their device performance, environmentally benign nature, and thus, reduced cost. Therefore, the paper introduces an optimization process of three-layered n-CdS/p-CIGS/p-GaAs (NPP)) solar cell architecture based on thickness and carrier charge density. An in-depth investigation of the numerical analysis for homojunction PPN-junction with the ’GaAs’ layer structure along with n-ZnO front contact was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Subsequently, various computational optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture were examined. The electronic characteristics by adding the GaAs layer on the top of the conventional (PN) junction further led to optimized values of the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF), and short-circuit current density (JSC) of the solar cell. Lastly, the paper concludes by highlighting the most promising results of our study, showcasing the impact of adding the GaAs layer. Hence, using the optimized values from the analysis, thickness of 5 (μm) and carrier density of 1×1020 (1/cm) resulted in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16 V, 89.52%, and 43.88 (mA/m2), respectively, for the proposed solar cell architecture. The outcomes of the study aim to pave the path for highly efficient, optimized, and robust multi-junction solar cells.

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Visualising Climate Change using Extended Reality: A Review

Cited by 4 publications

References 20 publications

Empathy, Education, and Awareness: A VR Hackathon’s Approach to Tackling Climate Change

Empathy, Education, and Awareness: A VR Hackathon’s Approach to Tackling Climate Change

A Machine Learning Frontier for Predicting LCOE of Photovoltaic System Economics

Computational Optimization for CdS/CIGS/GaAs Layered Solar Cell Architecture

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