Şahin, Kübra Nur

Profile URL
https://hdl.handle.net/20.500.12573/2706
Name Variants
Kubra Nur Şahi̇n Sahin, Kubra Nur Şahi̇n, Kübra Nur
Job Title
Arş. Gör.
Email Address
kubranur.sahin@agu.edu.tr
Main Affiliation
05.02. Endüstriyel Tasarım
Status
Current Staff
Website
ORCID ID
0000-0001-9786-6270
Scopus Author ID
58963871100
Turkish CoHE Profile ID
C63E1A81C483F571
Google Scholar ID
r28i4q8AAAAJ
WoS Researcher ID
KOC-2289-2024
Files
5388_Kubra_Nur_Sahin.jpeg (8.36 KB)
No research topics data found.

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
2
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
2
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LIFE BELOW WATER14
LIFE BELOW WATER
1
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
2
Research Products
Documents

3

Citations

10

h-index

1

Go to Scopus profile
Documents

1

Citations

3

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No records found in other affiliations.
Scholarly Output

4

Articles

2

Views / Downloads

11/16

Supervised MSc Theses

0

Supervised PhD Theses

1

WoS Citation Count

3

Scopus Citation Count

10

Patents

0

Projects

0

WoS Citations per Publication

0.75

Scopus Citations per Publication

2.50

Open Access Source

2

Supervised Theses

1

JournalCount
Advanced Wind Energy Systems: Grid Integration, Markets, and Sustainable Infrastructure1
Heliyon1
Sakarya University Journal of Science1
Current Page: 1 / 1

Scopus Quartile Distribution

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Scholarly Output Search Results

Now showing 1 - 4 of 4
  • Doctoral Thesis
    Optimal Decision-Making for Operations of Smart Grids and Microgrids
    (2025) Şahin, Kübra Nur; Sütçü, Muhammed
    Yenilenebilir enerji kaynaklarının artan entegrasyonu ve elektrik üretiminin merkeziyetsizleşerek dağıtık hale gelmesi, güç sistemlerinde koordinasyon ve sistem güvenilirliği açısından önemli zorlukları beraberinde getirmiştir. Bu çalışma, akıllı enerji toplulukları için, olasılıksal modelleme, merkezî optimizasyon ve uyarlanabilir kontrol yaklaşımlarını bir araya getiren çok katmanlı bir metodolojik çerçeve sunmaktadır. İlk aşamada, meteorolojik değişkenler arasındaki karmaşık doğrusal olmayan ilişkileri modelleyebilen ve rüzgâr enerjisi potansiyelini belirsizlik altında değerlendirebilen, kopula teorisi, derin öğrenme ve karar ağaçlarını birleştiren hibrit bir yöntem geliştirilmiştir. İkinci aşamada, farklı hane yapılarını içeren bir şebekede, dağıtık enerji kaynaklarının zamanlaması ve eşler arası (P2P) enerji ticaretinin optimizasyonu için Karışık Tamsayılı Doğrusal Programlama (MILP) tabanlı model tasarlanmıştır. Son aşamada ise, kural tabanlı karar verme yapısı, Derin Deterministik Politika Gradyanı (DDPG) algoritması ile geliştirilerek, gerçek zamanlı fiyatlandırma ve merkezsiz karar alma yeteneklerine sahip bir operasyonel kontrol ortamı oluşturulmuştur. Geliştirilen model, değişken sistem koşullarına uyum sağlamakta, enerji yönetimini optimize etmekte ve belirsizlik altında uzun vadeli sistem performansını artırmaktadır. Bu çalışma, enerji sistemlerinde kaynak değerlendirmesinden operasyonel kontrole uzanan; deterministik planlamayı gerçek zamanlı, öğrenen yapılarla bütünleştiren kapsamlı bir karar destek mimarisi sunmaktadır. Elde edilen bulgular, dağıtık yenilenebilir kaynakların entegrasyonunu destekleyen, esnek, dayanıklı ve sürdürülebilir enerji sistemlerinin geliştirilmesine katkı sunmaktadır.
  • 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: 1
    Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models
    (Sakarya University, 2022-02-28) Sutcu, Muhammed; Şahi̇n, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gulbahar, Ibrahim Tümay
    Load forecasting is an essential task which is executed by electricity retail companies. By predicting the demand accurately, companies can prevent waste of resources and blackouts. Load forecasting directly affect the financial of the company and the stability of the Turkish Electricity Market. This study is conducted with an electricity retail company, and main focus of the study is to build accurate models for load. Datasets with novel features are preprocessed, then deep learning models are built in order to achieve high accuracy for these problems. Furthermore, a novel method for solving regression problems with classification approach (discretization) is developed for this study. In order to obtain more robust model, an ensemble model is developed and the success of individual models are evaluated in comparison to each other. © 2025 Elsevier B.V., All rights reserved.
  • Book Part
    The Role of Wind Energy in a Sustainable Energy Transition Agenda
    (IGI Global, 2026-02-27) Şahin, Kübra Nur