Ağralı, Semra
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Agrali, Semra
Semra Ağralı
Semra Ağralı
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agralis@mef.edu.tr
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02.01. Department of Industrial Engineering
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Current Staff
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Sustainable Development Goals
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
7
AFFORDABLE AND CLEAN ENERGY
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4
QUALITY EDUCATION
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5
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PEACE, JUSTICE AND STRONG INSTITUTIONS
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2
ZERO HUNGER
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1
NO POVERTY
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11
SUSTAINABLE CITIES AND COMMUNITIES
0
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14
LIFE BELOW WATER
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15
LIFE ON LAND
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8
DECENT WORK AND ECONOMIC GROWTH
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13
CLIMATE ACTION
5
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6
CLEAN WATER AND SANITATION
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17
PARTNERSHIPS FOR THE GOALS
2
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12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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Documents
18
Citations
371
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10
Documents
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Citations
341
| 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
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Master Thesis 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.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.Article Citation - Scopus: 2Nonlinear Benefit-Cost Optimization-Based Selection of Insulation Material and Window Type: a Case Study in Turkey(2017) Ağralı, Semra; Uctuğ, Fehmi GörkemIn 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.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.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.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(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.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.
