Bilge, Ayşe Hümeyra
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Bilge A.
BILGE, Ayşe Hümeyra
Bilge, AYŞE HÜMEYRA
Bilge, Ayse Humeyra
AYŞE HÜMEYRA BILGE
A. Bilge
Bilge,Ayse Humeyra
Ayşe Hümeyra Bilge
Bilge,A.H.
BILGE, AYŞE HÜMEYRA
Kupeli A.
B., Ayşe Hümeyra
Bilge, Ayşe Hümeyra
B.,Ayse Humeyra
Bilge, A. H.
Ayse Humeyra, Bilge
A. H. Bilge
B., Ayse Humeyra
Bilge, A.
Ayşe Hümeyra BILGE
Hümeyra Bilge, Ayşe
Bilge, Ayşe Humeyra
Bilge, Ayşe
Bilge, Ayşe Hümeyra
Bilge, Ayşe Hümeyra
Bilge, Ayşe Hümeyra
BILGE, Ayşe Hümeyra
Bilge, AYŞE HÜMEYRA
Bilge, Ayse Humeyra
AYŞE HÜMEYRA BILGE
A. Bilge
Bilge,Ayse Humeyra
Ayşe Hümeyra Bilge
Bilge,A.H.
BILGE, AYŞE HÜMEYRA
Kupeli A.
B., Ayşe Hümeyra
Bilge, Ayşe Hümeyra
B.,Ayse Humeyra
Bilge, A. H.
Ayse Humeyra, Bilge
A. H. Bilge
B., Ayse Humeyra
Bilge, A.
Ayşe Hümeyra BILGE
Hümeyra Bilge, Ayşe
Bilge, Ayşe Humeyra
Bilge, Ayşe
Bilge, Ayşe Hümeyra
Bilge, Ayşe Hümeyra
Bilge, Ayşe Hümeyra
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Prof. Dr.
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Industrial Engineering
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Sustainable Development Goals
11
SUSTAINABLE CITIES AND COMMUNITIES
5
Research Products
10
REDUCED INEQUALITIES
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
2
ZERO HUNGER
0
Research Products
3
GOOD HEALTH AND WELL-BEING
20
Research Products
13
CLIMATE ACTION
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
5
GENDER EQUALITY
0
Research Products
6
CLEAN WATER AND SANITATION
1
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
1
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
4
QUALITY EDUCATION
0
Research Products
15
LIFE ON LAND
0
Research Products
1
NO POVERTY
0
Research Products
14
LIFE BELOW WATER
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
1
Research Products
Documents
81
Citations
552
h-index
12
Documents
68
Citations
451
Scholarly Output
67
Articles
45
Views / Downloads
20/0
Supervised MSc Theses
12
Supervised PhD Theses
1
WoS Citation Count
287
Scopus Citation Count
364
WoS h-index
9
Scopus h-index
10
Patents
0
Projects
0
WoS Citations per Publication
4.28
Scopus Citations per Publication
5.43
Open Access Source
48
Supervised Theses
13
| Journal | Count |
|---|---|
| Journal of Physics: Conference Series | 6 |
| International Journal of Energy Economics and Policy | 5 |
| International Journal of Computational and Experimental Science and Engineering | 2 |
| Modern Physics Letters B | 2 |
| Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi (Online) | 2 |
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Master Thesis Kurumsal Kredi Risk Değerlendirmesi: Bir Türk Finans Kurumu Örneği(2023) Hajjaouı, Btıssam; Bilge, Ayşe HümeyraBu tez iki bölümden oluşmaktadır. İlk bölümde, bir müşterinin ödeme yapıp yapmayacağını tahmin etmeye çalışıyoruz. İkinci bölümde, kredi başvurusunun onaylanıp onaylanmayacağına karar vermek için bir kredi skoru modeli oluşturma üzerine çalışıyoruz. Bu amaçla kullanılan veri kümesi, finans sektöründeki önde gelen kurumlardan birinden elde edilmiştir. Veri kümesi, başvuru sahibinin verilerine, kurumsal verilere, hissedar verilerine ve kredi geçmişine genel olarak atıfta bulunan 401 değişkeni içerir. Bu değişkenler içerisinden, giriş değişkenlerini ayırt ederek ve ardından bu girişleri inceleyerek kuvvetli ilişkili değişkenleri ve neredeyse tamamı eksik değerlerden oluşan değişkenleri kullanmaktan kaçınarak azaltıyoruz. Veri kümesindeki değişkenlerin büyük bir kısmında haklı sebeplerle eksik giriş bulunmaktadır. Bu sorunu çözmek için, hangi değişken grubunun hangi müşteriyle ilgili olduğunu yansıtmak adına yedi alt küme oluşturduk. Onaylanan krediler arasında yaklaşık %96 oranında ödeme yapan örnekler ve %4 oranında ödeme yapmayan örnekler bulunmaktadır. Bu tezde, eğitim kümelerindeki örnekleri dengelemek için üç örnekleme tekniği kullanıyoruz: alt örnekleme, aşırı örnekleme ve sentetik azınlık aşırı örnekleme tekniği. Ayrıca altı sınıflandırıcı uyguluyoruz: Rastgele Orman, Naif Bayes, Lojistik Regresyon, Destek Vektör Makinesi, Karar Ağacı ve K-En Yakın Komşu. Bu tekniklerin performansını ölçmek adına, çoğunluk sınıfının ve azınlık sınıfının sırasıyla ne kadar iyi tahmin edildiğini ölçmek için duyarlılık ve özgüllük kullanıyoruz. Hesaplamalar sonucunda, %50'den fazla duyarlılık ve özgüllük elde ettik, burada alt örnekleme tekniğinin azınlık sınıfı için en iyi örnekleme tekniği olduğu ve SMOTE ve aşırı örneklemenin, çoğunluk sınıfı için daha iyi performans gösterdiği gözlemlenmiştir. Seçilen değişkenlerin analizinde, neredeyse tüm değişkenlerin onaylanan ve reddedilen krediler arasında ayrım yapamadığı gözlemlendiği için lojistik regresyon kullanılarak tahmin edilen kredi puanları güvenilmez olarak değerlendirildi.Article Citation - WoS: 13Citation - Scopus: 20The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey(Elsevier Sci Ltd, 2022) Yukseltan, E.; Kok, A.; Yucekaya, A.; Bilge, A.; Aktunc, E. Agca; Hekimoglu, M.The rapid spread of COVID-19 has severely impacted many sectors, including the electricity sector. The reliability of the electricity sector is critical to the economy, health, and welfare of society; therefore, supply and demand need to be balanced in real-time, and the impact of unexpected factors should be analyzed. During the pandemic, behavioral restrictions such as lockdowns, closure of factories, schools, and shopping malls, and changing habits, such as shifted work and leisure hours at home, significantly affected the demand structure. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the estimated impact of the restrictions on total demand and daily demand profile. A modulated Fourier Series Expansion evaluates deviations from normal conditions in the aggregate demand and the daily consumption profile. The aggregate demand shows a significant decrease in the early phase of the pandemic, during the period March-June 2020. The shape of the daily demand curve is analyzed to estimate how much demand shifted from daytime to night-time. A population-based restriction index is proposed to analyze the relationship between the strength and coverage of the restrictions and the total demand. The persistency of the changes in the daily demand curve in the post-contingency period is analyzed. These findings imply that new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches in the future. The longterm policy implications for the energy transition and lessons learned from the COVID-19 pandemic experience are also presented.Article Citation - WoS: 2Citation - Scopus: 2Mathematical Models for Phase Transitions in Biogels(World Scientific Publ Co Pte Ltd, 2019) Bilge, Ayşe Hümeyra; Öğrenci, Arif Selçuk; Pekcan, ÖnderIt has been shown that reversible and irreversible phase transitions of biogels can be represented by epidemic models. The irreversible chemical sol-gel transitions are modeled by the Susceptible-Exposed-Infected-Removed (SEIR) or Susceptible-Infected-Removed (SIR) epidemic systems whereas reversible physical gels are modeled by a modification of the Susceptible-Infected-Susceptible (SIS) system. Measured sol-gel and gel-sol transition data have been fitted to the solutions of the epidemic models, either by solving the differential equations directly (SIR and SEIR models) or by nonlinear regression (SIS model). The gel point is represented as the "critical point of sigmoid," defined as the limit point of the locations of the extreme values of its derivatives. Then, the parameters of the sigmoidal curve representing the gelation process are used to predict the gel point and its relative position with respect to the transition point, that is, the maximum of the first derivative with respect to time. For chemical gels, the gel point is always located before the maximum of the first derivative and moves backward in time as the strength of the activation increases. For physical gels, the critical point for the sol-gel transition occurs before the maximum of the first derivative with respect to time, that is, it is located at the right of this maximum with respect to temperature. For gel-sol transitions, the critical point is close to the transition point; the critical point occurs after the maximum of the first derivative for low concentrations whereas the critical point occurs after the maximum of the first derivative for higher concentrations.Article Citation - WoS: 17Citation - Scopus: 24What Can We Estimate From Fatality and Infectious Case Data Using the Susceptible-Infected (sir) Model? a Case Study of Covid-19 Pandemic(Frontıers Medıa Sa, 2020) Ahmetolan, Semra; Bilge, Ayşe Hümeyra; Demirci, Ali; Peker-Dobie, Ayşe; Ergönül, ÖnderThe rapidly spreading Covid-19 that affected almost all countries, was first reported at the end of 2019. As a consequence of its highly infectious nature, countries all over the world have imposed extremely strict measures to control its spread. Since the earliest stages of this major pandemic, academics have done a huge amount of research in order to understand the disease, develop medication, vaccines and tests, and model its spread. Among these studies, a great deal of effort has been invested in the estimation of epidemic parameters in the early stage, for the countries affected by Covid-19, hence to predict the course of the epidemic but the variability of the controls over the course of the epidemic complicated the modeling processes. In this article, the determination of the basic reproduction number, the mean duration of the infectious period, the estimation of the timing of the peak of the epidemic wave is discussed using early phase data. Daily case reports and daily fatalities for China, South Korea, France, Germany, Italy, Spain, Iran, Turkey, the United Kingdom and the United States over the period January 22, 2020-April 18, 2020 are evaluated using the Susceptible-Infected-Removed (SIR) model. For each country, the SIR models fitting cumulative infective case data within 5% error are analyzed. It is observed that the basic reproduction number and the mean duration of the infectious period can be estimated only in cases where the spread of the epidemic is over (for China and South Korea in the present case). Nevertheless, it is shown that the timing of the maximum and timings of the inflection points of the proportion of infected individuals can be robustly estimated from the normalized data. The validation of the estimates by comparing the predictions with actual data has shown that the predictions were realized for all countries except USA, as long as lock-down measures were retained.Article Citation - WoS: 7Citation - Scopus: 12An Analysis of Price Spikes and Deviations in the Deregulated Turkish Power Market(Elsevier, 2019) Gayretli, Gizem; Yücekaya, Ahmet; Bilge, Ayşe HümeyraThe successful operation of a real time market is related to the planning in the day ahead market. We analyze the day ahead and real time market data for the Turkish power market for the period 2012-2015 to classify price spikes and their causes. We also focus on the levels of deviation between the day ahead market values and the real time market values. We define price deviation and load deviation ratios to measure the level of deviation both in price and demand. The analysis for the load is based on load shedding and cycling values. We analyze the mean and standard deviation in market prices and we determine the price spike as a two sigma deviation from the mean value. It is shown that 60% of the price deviation ratios are in the range of ( +/- 20%), while 44% are in the range of ( +/- 10%) and 35% are in the range of (+/- 5%). We also show that 56.9% of the spikes are due to problems in the generation of natural gas based power plants which affect the day ahead and real time prices. A total of 29.2% of the spikes are due to power plant and system failures that affect only real time prices. The share of high temperature based spikes is 13.9% which is a result of air conditioner usage.Article Citation - Scopus: 2The Impact of Dynamic Shocks and Special Days on Time Series Data(Prof.Dr. Iskender AKKURT, 2023) Gökdağ, Zehra Hafızoğlu; Bilge, Ayşe HümeyraThis paper includes an examination of a 4-year time series data on retail delivery demand generated by a logistics company based on the dates of creation. The periodic fluctuations observed in the data's normal structure are caused by the accumulation of demands over the weekend and their fulfillment at the beginning of the week. The aim of the study is modelling the response to unexpected changes in demand, which we refer to as "shocks," similar to the weekend effect. Special days, including single-day public holidays, religious holidays, and campaign periods in November, which represent specific periods, were also analyzed to interpret the patterns during these periods. The patterns created by single-day public holidays and religious holidays are significantly influenced by whether these days fall on a weekend or a weekday. By excluding weeks with special days from the overall data, the presence of shock effects in the remaining ordinary weeks was examined. During this period, the shock caused by the Covid-19 pandemic and adverse weather conditions was observed. The impact of the Covid-19 shock lasted longer compared to other shocks. When the increase in demand due to shocks exceeds the capacity of existing vehicles, the problem can be resolved by arranging daily rental vehicles from companies that provide vehicle allocations. Extracting the demand model for special days and unexpected shocks will ensure operational preparedness and prevent process delays. When ordinary weeks were examined, a monotonically decreasing trend from Monday to Sunday was observed based on the weekly average demand. The maximum demand was 58.3% on Monday, 17.2% on Tuesday, 15.9% on Wednesday, 7.3% on Thursday and 1.3% on Friday. The provided graphs also demonstrate a significant increase in demands in early 2020 due to the widespread adoption of e-commerce as a result of the Covid-19 pandemicArticle Citation - Scopus: 2The Impact of Dynamic Shocks and Special Days on Time Series Data(Prof.Dr. İskender AKKURT, 2023) Gökdağ,Z.H.; Bilge,A.H.This paper includes an examination of a 4-year time series data on retail delivery demand generated by a logistics company based on the dates of creation. The periodic fluctuations observed in the data's normal structure are caused by the accumulation of demands over the weekend and their fulfillment at the beginning of the week. The aim of the study is modeling the response to unexpected changes in demand, which we refer to as "shocks," similar to the weekend effect. Special days, including single-day public holidays, religious holidays, and campaign periods in November, which represent specific periods, were also analyzed to interpret the patterns during these periods. The patterns created by single-day public holidays and religious holidays are significantly influenced by whether these days fall on a weekend or a weekday. By excluding weeks with special days from the overall data, the presence of shock effects in the remaining ordinary weeks was examined. During this period, the shock caused by the Covid-19 pandemic and adverse weather conditions was observed. The impact of the Covid-19 shock lasted longer compared to other shocks. When the increase in demand due to shocks exceeds the capacity of existing vehicles, the problem can be resolved by arranging daily rental vehicles from companies that provide vehicle allocations. Extracting the demand model for special days and unexpected shocks will ensure operational preparedness and prevent process delays. When ordinary weeks were examined, a monotonically decreasing trend from Monday to Sunday was observed based on the weekly average demand. The maximum demand was 58.3% on Monday, 17.2% on Tuesday, 15.9% on Wednesday, 7.3% on Thursday, and 1.3% on Friday. The provided graphs also demonstrate a significant increase in demands in early 2020 due to the widespread adoption of e-commerce as a result of the Covid-19 pandemic. © IJCESEN.Master Thesis Sabit Oranlı Portföy Sigortalama Stratejisinde Sabit Çarpanın Fiyat / Kazanç Oranı Yardımı ile Belirlenmesi ve Bir Uygulama(Kadir Has Üniversitesi, 2014) Özer, Özen; Bilge, Ayşe Hümeyra; Horasanlı, MehmetSabit Oranlı Portföy Sigortası temelde riskli ve risksiz varlıktan oluşmaktadır. Portföydeki riskli varlık oranı "Sabit Çarpan" ve "Yastık Tutarı" parametreleri ile belirlenmektedir. Portföy sigortasında yatırımcıya önceden belirlenen bir oranda garanti verilmektedir. Bu strateji literatürde Sabit Çarpan'ın portföyün ömrü boyunca sabit olduğu durumlar için incelenmiştir. Bu çalışmada Sabit Çarpan'm piyasa koşullarına göre revize edildiği durum incelenmiştir. Portföydeki riskli varlığın getirisini maksimize etmek için en ideal senaryo, piyasanın yükseliş trendinde olduğu zamanlarda Sabit Çarpanı arttırarak kazancı maksimize etmek, düşüş trendinde olduğu zamanlarda ise azaltarak kaybı minimize etmek, diğer zamanlarda ise Sabit Çarpanı optimal bir değerde tutmaktır. Bu çalışmada, riskli varlığa ait geriye dönük F/K oranı verileri normalize edilerek, F/K oranının standart sapmasının genel seyrini incelememizi sağlayacak bir hareket bandı oluşturulmuş ve riskli varlığın aşırı değer kazandığı ve aşırı değer kaybettiği seviyeler standart sapmalar cinsinden belirlenmiştir. Bu seviyeler destek ve direnç indikatörleri olarak yorumlanarak, riskli varlığın aşırı değer kaybettiği sınır seviyesinde çarpan seviyesini arttırmak sureti ile riskli varlığın yükseliş trendinde getirisinin maksimize edilmesi, riskli varlığın aşırı değer kazandığı durumlarda ise çarpan seviyesini düşürmek sureti ile riskli varlığın düşüş trendinde zararın minimize edilmesi amaçlanmıştır. Bu çalışmada 14.08.2007-12.09.2013 tarihleri arasındaki BİST30 endeksi riskli varlık getirisi, KYD182 endeksi (iki yıllık tahvil getirisi endeksi) ise risksiz varlık getirisi olarak kabul edilmiş ve riskli varlık verileri üzerinde geriye dönük teste tabi tutularak Sabit Oranlı Portföy Sigortası'nm en önemli özelliği olan Koruma Tabanı'nm ihlali ve portföy getirisi üzerindeki etkileri incelenmiştir.Article Citation - WoS: 12Citation - Scopus: 15Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation(Elsevier Ltd, 2020) Yükseltan, Ergün; Yücekaya, Ahmet; Bilge, Ayşe Hümeyra; Ağca Aktunç, EsraDue to expensive infrastructure and the difficulties in storage, supply conditions of natural gas are different from those of other traditional energy sources like petroleum or coal. To overcome these challenges, supplier countries require take-or-pay agreements for requested natural gas quantities. These contracts have many pre-clauses; even if they are not met due to low/high consumption or other external factors, buyers must completely fulfill them. A similar contract is then imposed on distributors and wholesale consumers. It is, thus, important for all parties to forecast their daily, monthly, and annual natural gas demand to minimize their risk. In this paper, a model consisting of a modulated expansion in Fourier series, supplemented by deviations from comfortable temperatures as a regressor is proposed for the forecast of monthly and weekly consumption over a one-year horizon. This model is supplemented by a day-ahead feedback mechanism for the forecast of daily consumption. The method is applied to the study of natural gas consumption for major residential areas in Turkey, on a yearly, monthly, weekly, and daily basis. It is shown that residential heating dominates winter consumption and masks all other variations. On the other hand, weekend and holiday effects are visible in summer consumption and provide an estimate for residential and industrial use. The advantage of the proposed method is the capability of long term projections, reflecting causality, and providing accurate forecasts even with minimal information.Article Citation - WoS: 44Citation - Scopus: 52Hourly Electricity Demand Forecasting Using Fourier Analysis With Feedback(Elsevıer, 2020) Yükseltan, Ergün; Yücekaya, Ahmet; Bilge, Ayşe HumeyraWhether it be long-term, like year-ahead, or short-term, such as hour-ahead or day-ahead, forecasting of electricity demand is crucial for the success of deregulated electricity markets. The stochastic nature of the demand for electricity, along with parameters such as temperature, humidity, and work habits, eventually causes deviations from expected demand. In this paper, we propose a feedback-based forecasting methodology in which the hourly prediction by a Fourier series expansion is updated by using the error at the current hour for the forecast at the next hour. The proposed methodology is applied to the Turkish power market for the period 2012-2017 and provides a powerful tool to forecasts the demand in hourly, daily and yearly horizons using only the past demand data. The hourly forecasting errors in the demand, in the Mean Absolute Percentage Error (MAPE) norm, are 0.87% in hour-ahead, 2.90% in day-ahead, and 3.54% in year-ahead horizons, respectively. An autoregressive (AR) model is also applied to the predictions by the Fourier series expansion to obtain slightly better results. As predictions are updated on an hourly basis using the already realized data for the current hour, the model can be considered as reliable and practical in circumstances needed to make bidding and dispatching decisions.
