Ağralı, Semra
Loading...
Profile URL
Name Variants
Agrali, Semra & Semra Ağralı
Job Title
Email Address
agralis@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Research Topics
Domains
Social SciencesPhysical Sciences
Fields
Business, Management and AccountingEconomics, Econometrics and FinanceEngineeringEnergy
Subfields
Management Information SystemsEconomics and EconometricsIndustrial and Manufacturing EngineeringRenewable Energy, Sustainability and the EnvironmentFinance
Specific Research Areas
Supply Chain and Inventory Management
Climate Change Policy and Economics
Scheduling and Optimization Algorithms
Energy Efficiency and Management
Capital Investment and Risk Analysis
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
Research Products
3GOOD HEALTH AND WELL-BEING
0
Research Products
4QUALITY EDUCATION
0
Research Products
5GENDER EQUALITY
0
Research Products
6CLEAN WATER AND SANITATION
0
Research Products
7AFFORDABLE AND CLEAN ENERGY
3
Research Products
8DECENT WORK AND ECONOMIC GROWTH
0
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
10REDUCED INEQUALITIES
0
Research Products
11SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
13CLIMATE ACTION
5
Research Products
14LIFE BELOW WATER
0
Research Products
15LIFE ON LAND
0
Research Products
16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
17PARTNERSHIPS FOR THE GOALS
2
Research Products
Documents
18
Citations
382
h-index
10
Documents
19
Citations
348
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Bahçeşehir University | 10 |
| MEF University | 9 |
| Boğaziçi University | 9 |
| University of Florida | 4 |
| Koç University | 2 |
1 / 3
Data obtained from OpenAlex
Scholarly Output
19
Articles
10
Views / Downloads
1190/806
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
166
Scopus Citation Count
186
Patents
0
Projects
3
WoS Citations per Publication
8.74
Scopus Citations per Publication
9.79
Open Access Source
9
Supervised Theses
0
| Journal | Count |
|---|---|
| Journal Of Renewable And Sustainable Energy | 2 |
| Ieee Transactions On Power Systems | 1 |
| IISE Transactions | 1 |
| INFORMS Journal on Computing | 1 |
| Journal Of Environmental Management | 1 |
Current Page: 1 / 2
Scopus Quartile Distribution
Competency Cloud
19 results
Scholarly Output Search Results
Now showing 1 - 10 of 19
Master Term Project Predicttion of Brent Oil Spot Prices Using Country Based Inventory and Trading Data(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Usta, İsmail Batur; Ağralı, SemraCrude oil price forecasting has been the focus of numerous authorities, yet the task still persists on being a challenging one. The extremely volatile nature of oil market and high number of active players in it makes establishing a solid forecasting model that is constantly relevant to time very difficult. Recent advancements on data technologies, mainly ever-increasing computing power and trending big data technologies allowed new approaches to be born. From online learners to natural language processing, advanced data analytics models were employed with the help of easily accessible and diverse data. This project is an attempt on making use of such available data in order to forecast Brent oil spot price. By using monthly country by country inventory, trading and economic data, strong drivers of crude price was explored. The data used in this project comes from various sources and in multiple formats, with the final merged data frame has over 17000 observations and contains information on 86 countries. To enhance prediction power, a specialized learner is fit on each country individually and then the predictions are accumulated and filtered before outputting a single prediction. Compared to a single predictor, this approach enhanced the predictive power of the algorithm by adapting to dynamics of each country.Master Term Project Credit Card Churn Prediction With Machine Learning Algorithms(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Konuksal, Serap; Ağralı, SemraCredit card is one of the main products in banking sector and there is a big competition in credit card business. This competition makes retention of customers critical. To retain the customers, it is very important to interpret the customers that may churn. Targeting right customers with right offer is the main aim of Customer Relationship Management (CRM) in marketing. When the churn probability of customers is predicted, it is easier to retain the customers by proposing the retention offers directly to the ones with high churn probability. This will allow banks to manage their marketing budgets efficiently. In this project, a private bank’s credit card customer data is used. Data includes many different types of features of customers, such as number and type of transactions, credit card limits, feature usage, credit bureau information and demographic information. We develop a set of churn prediction models by implementing different machine learning algorithms. We compare these algorithms to find the best model with highest accuracy to be offered to the bank. We also share the main indicators that affect churn so that the bank can use them in retention activities.Conference Object A Mathematical Programming-Based Approach for an Energy Investment Planning of a Private Company(2017) Ağralı, Semra...Master Term Project Electricity Demand Forecasting for Turkey(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Yiğit, Hakan; Ağralı, SemraForecasting demand for products and services accurately provides competitive advantage to companies. This capstone project focuses on electricity demand forecasting for Turkey. Since energy storage is not yet a viable option, generated electricity is consumed simultaneously; and this fact exposes the importance of electricity demand forecast. Main aims of this project are to perform exploratory data analysis of the Turkish power market and apply machine learning algorithms to forecast electricity demand. Turkey’s electricity demand is predicted using real-life data obtained for years between 2017 and 2018. The results show that electricity demand can be modeled using machine learning algorithms, and the models can be used to predict future electricity demand.Master Term Project Sales Lead Ranking for Extended Coverage Automobile Insurance Policies Using the Online Quotes(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tekince, Ahmetcan; Ağralı, SemraThis study analyzes the features that are important for the extended coverage automobile insurance sale decision of the client and the improvement strategies for insurance sales using the information gained from the analysis of algorithms. We start with a binary classification stating that whether a sale is made after each quote or not. All quotes are scored and ranked in the decreasing order in which a sale was predicted but not realized. We use the Two-Class- Boosted Decision Tree, Two -Class Neural Networks and the Two-Class Locally Deep SVM models. The Neural Network model provided the best results; and a list of quotes that were not sold and also seemed very possible to be converted into sales was generated, which can be used by the sales staff for realizing these sales.Article Citation - WoS: 24Citation - Scopus: 23Modeling of Carbon Credit Prices Using Regime Switching Approach(AIP Publishing, 2018-05-01) Çanakoğlu, Ethem; Ağralı, Semra; Adıyeke, Esra; Aǧralı, Semra; Adlyeke, Esra; Aǧrall, SemraIn this study, we analyze the price dynamics of carbon certificates that are traded under the European Union's Emissions Trading System (EU-ETS). With the aim of investigating the joint relations among carbon, electricity, and fuel prices, we model historical prices using several methods and incorporating structural changes, such as econometric time series, regime switching, and multivariate vector autoregression models. We compare the results of the structural model with the results of traditional Markov switching and autoregressive models with breaks and present performance analysis based on the mean average percentage error, root mean squared error, and coefficient of determination. According to these performance tests, models with regimes outperform the approaches where breaks are defined using ex ante dummy variables. Moreover, we conclude that among regime switching models, univariate models are better than multivariate counterparts for modeling carbon price series for the analysis of both in-sample and out-of-samples. Published by AIP Publishing.Article Citation - WoS: 65Citation - Scopus: 72Carbon Price Forecasting Models Based on Big Data Analytics(Taylor and Francis Ltd., 2019-02-17) Çanakoğlu, Ethem; Ağralı, Semra; Yahşi, MustafaAfter the establishment of the European Union's Emissions Trading System (EU-ETS) carbon pricing attracted many researchers. This paper aims to develop a prediction model that anticipates future carbon prices given a real-world data set. We treat the carbon pricing issue as part of big data analytics to achieve this goal. We apply three fundamental methodologies to characterize the carbon price. First method is the artificial neural network, which mimics the principle of human brain to process relevant data. As a second approach, we apply the decision tree algorithm. This algorithm is structured through making multiple binary decisions, and it is mostly used for classification. We employ two different decision tree algorithms, namely traditional and conditional, to determine the type of decision tree that gives better results in terms of prediction. Finally, we exploit the random forest, which is a more complex algorithm compared to the decision tree. Similar to the decision tree, we test both traditional and conditional random forest algorithms to analyze their performances. We use Brent crude futures, coal, electricity and natural gas prices, and DAX and S&P Clean Energy Index as explanatory variables. We analyze the variables' effects on carbon price forecasting. According to our results, S&P Clean Energy Index is the most influential variable in explaining the changes in carbon price, followed by DAX Index and coal price. Moreover, we conclude that the traditional random forest is the best algorithm based on all indicators. We provide the details of these methods and their comparisons.Article Citation - WoS: 51Citation - Scopus: 58An Optimization Model for Carbon Capture & Storage/Utilization Vs. Carbon Trading: a Case Study of Fossil-Fired Power Plants in Turkey(Academic Press Ltd- Elsevier Science Ltd, 2018-06-01) Uctug, Fehmi Görkem; Ağralı, Semra; Türkmen, Burçin AtılganWe consider fossil-fired power plants that operate in an environment where a cap and trade system is in operation. These plants need to choose between carbon capture and storage (CCS), carbon capture and utilization (CCU), or carbon trading in order to obey emissions limits enforced by the government. We develop a mixed-integer programming model that decides on the capacities of carbon capture units, if it is optimal to install them, the transportation network that needs to be built for transporting the carbon captured, and the locations of storage sites, if they are decided to be built. Main restrictions on the system are the minimum and maximum capacities of the different parts of the pipeline network, the amount of carbon that can be sold to companies for utilization, and the capacities on the storage sites. Under these restrictions, the model aims to minimize the net present value of the sum of the costs associated with installation and operation of the carbon capture unit and the transportation of carbon, the storage cost in case of CCS, the cost (or revenue) that results from the emissions trading system, and finally the negative revenue of selling the carbon to other entities for utilization. We implement the model on General Algebraic Modeling System (GAMS) by using data associated with two coal-fired power plants located in different regions of Turkey. We choose enhanced oil recovery (EOR) as the process in which carbon would be utilized. The results show that CCU is preferable to CCS as long as there is sufficient demand in the EOR market. The distance between the location of emission and location of utilization/storage, and the capacity limits on the pipes are an important factor in deciding between carbon capture and carbon trading. At carbon prices over $15/ton, carbon capture becomes preferable to carbon trading. These results show that as far as Turkey is concerned, CCU should be prioritized as a means of reducing nationwide carbon emissions in an environmentally and economically rewarding manner. The model developed in this study is generic, and it can be applied to any industry at any location, as long as the required inputs are available. (C) 2018 Elsevier Ltd. All rights reserved.Article Citation - WoS: 1Citation - Scopus: 2A Decomposition Algorithm for Single and Multiobjective Integrated Market Selection and Production Planning(Informs, 2023-11-01) van den Heuvel, Wilco; Ağralı, Semra; Taşkın, Z. CanerWe study an integrated market selection and production planning problem. There is a set of markets with deterministic demand, and each market has a certain revenue that is obtained if the market's demand is satisfied throughout a planning horizon. The demand is satisfied with a production scheme that has a lot-sizing structure. The problem is to decide on which markets' demand to satisfy and plan the production simultaneously. We consider both single and multiobjective settings. The single objective problem maximizes the profit, whereas the multiobjective problem includes the maximization of the revenue and the minimization of the production cost objectives. We develop a decomposition-based exact solution algorithm for the single objective setting and show how it can be used in a proposed three-phase algorithm for the multiobjective setting. The master problem chooses a subset of markets, and the subproblem calculates an optimal production plan to satisfy the selected markets' demand. We investigate the subproblem from a cooperative game theory perspective to devise cuts and strengthen them based on lifting. We also propose a set of valid inequalities and preprocessing rules to improve the proposed algorithm. We test the efficacy of our solution method over a suite of problem instances and show that our algorithm substantially decreases solution times for all problem instances.Article Citation - WoS: 1Citation - Scopus: 2Simple Nonlinear Optimization-Based Selection of Insulation Material and Window Type in Turkey: Effect of Heating and Cooling Base Temperatures(College Publishing, 2017) Ağralı, Semra; Uçtuğ, Fehmi GörkemThe energy-savings of four hypothetical households in different climatic regions of Turkey were calculated via a nonlinear mixed integer optimization model. The ideal insulation material, its optimum thickness, and the ideal window type were determined. The standard degree days method was used with five different base temperatures for heating and five different base temperatures for cooling. The climatic conditions of the region, the properties of the insulation options, the unit price of fuel and electricity and the base temperature are used as model inputs, whereas the combination of selected insulation material with its optimum thickness and window type are given as model outputs. Stone Wool was found to be the ideal wall insulation material in all scenarios. The optimum window type was found to depend on the heating or cooling requirements of the house, as well as the lifetime of insulation. The region where the energy saving actions are deemed most feasible has been identified as Erzurum (Region 4), followed by Antalya (Region 1). Finally, the effect of changing the base temperature on energy savings was investigated and the results showed that an approximate average increase of $15/degrees C in annual savings is possible. Our model can be used by any prospective home-owner who would like to maximize their energy savings.