Ağralı, Semra
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Agrali, Semra
Semra Ağralı
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
Sustainable Development Goals
2
ZERO HUNGER
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
1
NO POVERTY
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
3
Research Products
10
REDUCED INEQUALITIES
0
Research Products
3
GOOD HEALTH AND WELL-BEING
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
5
GENDER EQUALITY
0
Research Products
14
LIFE BELOW WATER
0
Research Products
13
CLIMATE ACTION
5
Research Products
15
LIFE ON LAND
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
2
Research Products
4
QUALITY EDUCATION
0
Research Products
Documents
18
Citations
369
h-index
10
Documents
19
Citations
339
Scholarly Output
19
Articles
10
Views / Downloads
4142/14566
Supervised MSc Theses
8
Supervised PhD Theses
0
WoS Citation Count
157
Scopus Citation Count
173
WoS h-index
6
Scopus h-index
6
Patents
0
Projects
3
WoS Citations per Publication
8.26
Scopus Citations per Publication
9.11
Open Access Source
9
Supervised Theses
8
| 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 |
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19 results
Scholarly Output Search Results
Now showing 1 - 10 of 19
Master Thesis 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 Thesis 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: 23Citation - Scopus: 22Modeling of Carbon Credit Prices Using Regime Switching Approach(2018) Çanakoğlu, Ethem; Ağralı, Semra; Adıyeke, EsraIn 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: 61Citation - Scopus: 65Carbon Price Forecasting Models Based on Big Data Analytics(Taylor and Francis Ltd., 2019) 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: 49Citation - Scopus: 56An Optimization Model for Carbon Capture & Storage/Utilization Vs. Carbon Trading: a Case Study of Fossil-Fired Power Plants in Turkey(2018) 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: 1A Decomposition Algorithm for Single and Multiobjective Integrated Market Selection and Production Planning(Informs, 2023) 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(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.Conference Object A Mathematical Programming-Based Approach for an Energy Investment Planning of a Private Company(2017) Ağralı, Semra...Article Citation - WoS: 10Citation - Scopus: 13Energy Investment Planning at a Private Company: a Mathematical Programming-Based Model and Its Application in Turkey(2017) Ağralı, Semra; Canakoglu, Ethem; Arikan, Yildiz; Terzi, Fulya; Adıyeke, EsraWe consider a mid-sized private electricity generating company that plans to enter the market that is partially regulated. There is a cap and trade system in operation in the industry. There are nine possible power plant types that the company considers to invest on through a planning horizon. Some of these plants may include a carbon capture and storage technology. We develop a stochastic mixed-integer linear program for this problem where the objective is to maximize the expected net present value of the profit obtained. We include restrictions on the maximum and minimum possible amount of investment for every type of investment option. We also enforce market share conditions such that the percentage of the total investments of the company over the total installed capacity of the country stay between upper and lower bounds. Moreover, in order to distribute the investment risk, the percentage of each type of power plant investment is restricted by some upper bound. We tested the model for a hypothetical company operating in Turkey. The results show that the model is suitable to be used for determining the investment strategy of the company.Master Thesis Game Recommendation System for Steam Platform(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Bayram, Serhan; Semra AğralıIncreasing number of choices and competition in the markets, force companies to differ in services they provide to their customers. Offering better services have a positive impact on customer loyalty, and to do so, companies should understand their customers’ interests and act accordingly. One popular method for this purpose is building recommendation engines to make personalized suggestions. In this project, collaborative filtering methods with implicit feedback are used to make recommendations to users of theSteam platform. The recommendation systems are built using two different matrix factorization techniques, Alternating Least Squares and Bayesian Personalized Ranking. Different models are created with implicit playtime data of the users and the results are evaluated by using Precision at k metric. Additionally, similar items that are offered by the models are analyzed. Results show that the models are considerably successful at finding personal choices and similar items. The best model finds the item in the libraries of 33% ofthe users.
