Kahraman, Ayşegül

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Profile URL
https://gcris.yasar.edu.tr/handle/123456789/12712
Name Variants
Job Title
Araş.Gör.
Email Address
Main Affiliation
01.01.09.04. Enerji Sistemleri Mühendisliği Bölümü
Status
Former Staff
Website
ORCID ID
0000-0001-7673-7218
Scopus Author ID
57209280177
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
GML-1595-2022

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
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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
1
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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
1
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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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
0
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Documents

12

Citations

119

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Scholarly Output

3

Articles

1

Views / Downloads

0/0

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

90

Scopus Citation Count

93

Patents

0

Projects

0

WoS Citations per Publication

30.00

Scopus Citations per Publication

31.00

Open Access Source

0

Supervised Theses

1

JournalCount
Innovations in Intelligent Systems and Applications Conference (ASYU)1
Journal of Building Engineering1
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  • Thumbnail Image
    Article
    Citation - WoS: 90
    Citation - Scopus: 93
    A predictive control strategy for optimal management of peak load thermal comfort energy storage and renewables in multi-zone buildings
    (ELSEVIER, 2019) Emrah Biyik; Aysegul Kahraman; Biyik, Emrah; Kahraman, Aysegul
    Buildings are responsible for about 40% of the global energy consumption where heating ventilation and air conditioning (HVAC) systems account for the most part of it. Continuous increase in the installation of new HVAC systems and higher penetration of renewables and energy storage in the building energy network require more sophisticated control approaches to realize the full potential of these systems. In this paper an optimal control framework to coordinate HVAC battery energy storage and renewable generation in buildings is developed. The controller aims to reduce peak load demand while achieving thermal comfort within industry standards. To facilitate this a simple lumped mathematical model that describes the zone transient thermal dynamics is structured with a minimal data from the building and is trained with actual thermal and electrical data. Next a model predictive control algorithm that takes into account building thermal dynamics battery state of charge renewable generation status and actual operational data and constraints is formulated to regulate HVAC demand battery power and building thermal comfort. The controller considers the changes in the outside dry-bulb air temperature electricity price required energy amount and comfort conditions simultaneously in order to find the proper optimal zone temperatures guaranteeing occupant comfort. The new controller was tested using data from a real building and preliminary results indicate that significant reduction in peak electrical power demand can be achieved by the proposed approach.
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    Master Thesis
    Mikro şebeke enerji yönetimi için tahmine dayalı hibrit model öngörümlü kontrol algoritması
    (2020) Kahraman, Ayşegül; Güzeliş, Cüneyt; Bıyık, Emrah
    Enerji talebindeki artış, geleneksel enerji üretiminin çevresel etkileri ve daha yüksek yenilenebilir enerji penetrasyonu, bizi akıllı şebeke, dağıtılmış üretim, elektrik depolama ve gelişmiş kontrol ve eniyileme (optimizasyon) konuları üzerine düşünmeye yönlendirmiştir. Bu tezde, bir mikro şebekenin kontrol problemi, ileri kontrol tekniği olan Model Öngörülü Kontrol (MÖK) ile yönetilirken, güneş enerjisi üretiminin ve elektrik yükü talebinin rassal doğası dikkate alınarak çalışılmıştır. İlk olarak elektrik yük talebini ve fotovoltaik (FV) çıkış gücünü tahmin etmek için farklı tahmin yöntemlerini kullandık. Türkiye'de Yaşar Üniversitesinde bulunan, binaya entegre fotovoltaik sistemin enerji üretimini ve aynı kampüste bulunan bir binanın elektrik yük tüketimini 24 saatlik zaman dilimi boyunca tahmin etmek için Doğrusal Regresyon, Mevsimsel Özbağlanımlı Tümleşik Kayan Ortalama ve Çok Katmanlı Algılayıcı (ÇKA) yöntemlerini uyguladık. Ardından üç farklı MÖK yaklaşımı tasarlayıp, bu yaklaşımların performanslarını karşılaştırdık: (i) gelecekteki yük ve FV üretimin noktasal tahminlerini alarak deterministik MÖK, (ii) geçmiş net yük dağılımını kullanarak rassal MÖK, (iii) deterministik ve rassal MÖK yöntemlerinin güçlü yönlerini birleştiren melez (hibrit) yöntem. Yük talebi ve yenilenebilir enerji üretiminin rassal yapısını ele almak için 'şans kısıtı' ve 'iki aşamalı (eklenmeli)' rassal programlama yöntemlerini MÖK yaklaşımı içinde kullanıyoruz. İki aşamalı yöntemde senaryoların sayısını azaltmak için, özgün bir Tekil Değer Ayrıştırma tabanlı model derecesi azaltma yöntemi uyguladık. Bu tezin özgün katkıları iki şekildedir: (i) gelecekteki zaman dilimleri için geçmiş verinin karakterini ve noktasal tahminleri birleştiren ve bu sayede tamamen deterministik veya rassal MÖK yaklaşımlarından daha iyi performans gösteren melez bir MÖK yaklaşımının geliştirilmesi ve (ii) iki aşamalı rassal programlamada senaryoların sayısını azaltmak için Tekil Değer Ayrıştırma tekniğinin uyarlanmasıdır.
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    Conference Object
    Stochastic Microgrid Control Problems: Effects of Load Distribution and Planning Horizon
    (IEEE, 2019) Aysegul Kahraman; Onder Bulut; Emrah Biyik; Cuneyt Guzelis; Gokhan Demirkiran; Demirkiran, Gokhan; Guzelis, Cuneyt; Kahraman, Aysegul; Bulut, Onder; Biyik, Emrah
    Microgrids enable the integration of distributed energy resources with high renewable penetration into the main power grid. In this study a microgrid problem that takes into account the stochastic nature of the net load defined as the difference between actual demand and renewable generation is studied. The problem is formulated as a Mixed Integer Linear Stochastic Optimization Programming and is solved under different net load distributions and planning horizons. Numerical results show that increasing variance causes a rise in total system cost for the approach that solves the stochastic problem by ignoring randomness (as in most real-life applications) as well as for the one that solves the problem with the true distribution. It is observed that enlarging the planning horizon also has similar effects.