Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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  • Article
    Citation - WoS: 3
    Citation - Scopus: 9
    Probabilistic Assessment of Wind Power Plant Energy Potential Through a Copula-Deep Learning Approach in Decision Trees
    (Cell Press, 2024-04) Sahin, Kubra Nur; Sutcu, Muhammed
    In the face of environmental degradation and diminished energy resources, there is an urgent need for clean, affordable, and sustainable energy solutions, which highlights the importance of wind energy. In the global transition to renewable energy sources, wind power has emerged as a key player that is in line with the Paris Agreement, the Net Zero Target by 2050, and the UN 2030 Goals, especially SDG-7. It is critical to consider the variable and intermittent nature of wind to efficiently harness wind energy and evaluate its potential. Nonetheless, since wind energy is inherently variable and intermittent, a comprehensive assessment of a prospective site's wind power generation potential is required. This analysis is crucial for stakeholders and policymakers to make well-informed decisions because it helps them assess financial risks and choose the best locations for wind power plant installations. In this study, we introduce a framework based on Copula-Deep Learning within the context of decision trees. The main objective is to enhance the assessment of the wind power potential of a site by exploiting the intricate and non-linear dependencies among meteorological variables through the fusion of copulas and deep learning techniques. An empirical study was carried out using wind power plant data from Turkey. This dataset includes hourly power output measurements as well as comprehensive meteorological data for 2021. The results show that acknowledging and addressing the non-independence of variables through innovative frameworks like the Copula-LSTM based decision tree approach can significantly improve the accuracy and reliability of wind power plant potential assessment and analysis in other real-world data scenarios. The implications of this research extend beyond wind energy to inform decision-making processes critical for a sustainable energy future.
  • Article
    Citation - Scopus: 7
    Impact of Sustainable Energy, Fossil Fuels and Green Finance on Ecosystem: Evidence From China
    (Elsevier Ltd, 2024) Wang, Zuoteng; Zeng, Sheng; Khan, Zohan
    The adoption of sustainable energy has increased as a substitute for petroleum derivatives due to growing concerns about environmental degradation caused by pollution and non-renewable energy sources. This study aims to investigate the impact of sustainable energy, green finance, and fossil fuels on the ecology of China. Instead of using traditional intermediaries like CO2 and EF, we employed the ecosystem habitat index to evaluate the conservation of terrestrial ecosystems. This index measures the extent of habitat destruction, deterioration, and fragmentation. The research demonstrated that implementing ecological power and green finance in China has enhanced the country's ability to safeguard and enhance its ecosystem in the short and long term. Furthermore, the findings suggest that using non-renewable energy sources in China has heightened the risk to biodiversity and the ecosystem. The analysis indicates that prioritizing green funding and renewable energy sources is crucial for policymakers, legislators, and investors to safeguard and enhance ecosystem diversity. © 2024 Elsevier B.V., All rights reserved.