Ağralı, Semra
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Agrali, Semra & Semra Ağralı
Job Title
Email Address
agralis@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
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Turkish CoHE Profile ID
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No research topics data found.
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
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3GOOD HEALTH AND WELL-BEING
0
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4QUALITY EDUCATION
0
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
3
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
0
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
5
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14LIFE BELOW WATER
0
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15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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17PARTNERSHIPS FOR THE GOALS
2
Research Products
Documents
18
Citations
371
h-index
10
Documents
19
Citations
341
No records found in other affiliations.
Scholarly Output
19
Articles
10
Views / Downloads
4155/14868
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
159
Scopus Citation Count
175
Patents
0
Projects
3
WoS Citations per Publication
8.37
Scopus Citations per Publication
9.21
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
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19 results
Scholarly Output Search Results
Now showing 1 - 10 of 19
Article Citation - Scopus: 2Nonlinear Benefit-Cost Optimization-Based Selection of Insulation Material and Window Type: a Case Study in Turkey(Amer Inst Physics, 2017) Ağralı, Semra; Uctuğ, Fehmi Görkem; Aǧrall, SemraIn this study, we maximize the energy savings of a hypothetical household by choosing an optimal insulation material with its optimal thickness and also the optimal window type. We develop a nonlinear mixed integer optimization model that maximizes the net present value of the benefits obtained by insulation over the lifespan of the house. Savings are calculated based on the gains from the electricity usage for air conditioning during cooling-required days and the fuel usage for heaters in heating-required days. The heat transfer calculations consider conductive, convective, and radiative components simultaneously. The optimization model takes the climate conditions of the region where the house is located, the consumer's desired indoor temperature, and the properties of the insulation options; then, it returns a combination of selected insulation materials with its optimum thickness and window type as output. We applied the optimization model developed to hypothetical houses in four different climatic regions of Turkey for different lifespans. For all reasonable lifespans, the model choses stonewool as the ideal insulation material. For high interest rates, single windows or double-glazed windows are preferable, but as the interest rate decreases and the net present value of the energy-savings increases, the model prefers triple-glazed windows as the ideal material. Erzurum, a city in climatic region 4, characterized by very cold winters and cool summers, was found to require the highest optimum insulation thickness, and the economic return resulting from the above-mentioned energy-saving actions was also found to be the highest in the case of Erzurum. In all the regions, the energy-saving investments were found to be feasible via applying the feasibility assessment techniques of net present value and payback period. The model developed in this study is applicable to any household as long as the required input data are available. Published by AIP Publishing.Master Term Project Carbon Price Forecasting(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Karakaya, Nurhak; Ağralı, SemraIn last twenty years great improvements occurred both in technological advances and in the world economic capacity. The total production capacity of countries has been increasing rapidly. These increases need great usage of energy. For that reason, prices of energy related products are very important as they dramatically affect company budgets. Energy budgets get a great deal in total budget of companies and countries. A unit increase in an energy related product can severely affect the budget. The carbon price is one of those products. Besides carbon prices, carbon usage also affects global environment so its price also has an impact on global temperature. To forecast future carbon price different machine learning methods are used. In literature, support vector machines (SVM) [1, 2, 3], random forest (RF) [4, 5], artificial neural networks (ANN) [6, 7, 8] and Auto Regressive Moving Average (ARMA) [9] are commonly used methods. All these methods have pros and cons over the others. In this project, we also apply different machine learning methods, ANN, SVM, RF, Lasso Regression (LG)[11] and Ridge Regression (RR) [10] to forecast the carbon price over time, and give an explanation for future price movements. Then, we compare those five models by analyzing model validation methods. Finally, we choose the best model for further experiments. We have four data types: daily carbon price (CP), electricity price (EP), natural gas price (NG) and coal price (COP) that cover the period of 2009 and 2017. Prices are provided in different currencies. First of all, we work on the data to have all prices in the same currency. We completely eliminate null data. Then, graphically we investigate overall trend by smoothing the data. For analyzing data, we look for daily, monthly, yearly and seasonally time scales. For every weekday or weekends in train data set we keep a day in test data set so that we can keep the time effect in our model. After the data management process, we apply different forecasting methods to explain future carbon price tendencies.Article Citation - WoS: 6Citation - Scopus: 6A Strong Integer Programming Formulation for Hybrid Flowshop Scheduling(Taylor & Francis, 2019) Ağralı, Semra; Ünal, A. Tamer; Taşkın, Z. CanerWe consider a hybrid flowshop scheduling problem that includes parallel unrelated discrete machines or batch processing machines in different stages of a production system. The problem is motivated by a bottleneck process within the production system of a transformer producer located in the Netherlands. We develop an integer programming model that minimises the total tardiness of jobs over a finite planning horizon. Our model is applicable to a wide range of production systems organised as hybrid flowshops. We strengthen our integer program by exploiting the special properties of some constraints in our formulation. We develop a decision support system (DSS) based on our proposed optimisation model. We compare the results of our initial optimisation model with an improved formulation as well as with a heuristic that was in use at the company before the implementation of our DSS. Our results show that the improved optimisation model significantly outperforms the heuristic and the initial optimisation model in terms of both the solution time and the strength of its linear programming relaxation.Article Citation - WoS: 10Citation - Scopus: 13Energy Investment Planning at a Private Company: a Mathematical Programming-Based Model and Its Application in Turkey(IEEE-Inst Electrical Electronics Engineers Inc, 2017) Ağralı, Semra; Canakoglu, Ethem; Arikan, Yildiz; Terzi, Fulya; Adıyeke, Esra; Adyeke, Esra; Agral, Semra; Çanakolu, EthemWe 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 Term Project 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.Article Citation - WoS: 23Citation - Scopus: 22Modeling of Carbon Credit Prices Using Regime Switching Approach(AIP Publishing, 2018) 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: 62Citation - Scopus: 66Carbon 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.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.Article Citation - WoS: 49Citation - Scopus: 57An 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) 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.