Yücekaya, Ahmet Deniz
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A. Yücekaya
YÜCEKAYA, AHMET DENIZ
Yücekaya, A.
Yucekaya A.
Yücekaya, Ahmet Deniz
Yücekaya,A.D.
Ahmet Deniz, Yucekaya
Ahmet Deniz YÜCEKAYA
Yücekaya, A. D.
AHMET DENIZ YÜCEKAYA
Yucekaya,Ahmet Deniz
A. D. Yücekaya
Yucekaya,A.D.
Ahmet Deniz Yücekaya
Yucekaya, Ahmet Deniz
YÜCEKAYA, Ahmet Deniz
Y.,Ahmet Deniz
Yücekaya, AHMET DENIZ
Y., Ahmet Deniz
Yücekaya, Ahmet Çelebi
Yücekaya, Ahmet Deniz
Yucekaya, Ahmet
Yücekaya, Ahmet
YÜCEKAYA, AHMET DENIZ
Yücekaya, A.
Yucekaya A.
Yücekaya, Ahmet Deniz
Yücekaya,A.D.
Ahmet Deniz, Yucekaya
Ahmet Deniz YÜCEKAYA
Yücekaya, A. D.
AHMET DENIZ YÜCEKAYA
Yucekaya,Ahmet Deniz
A. D. Yücekaya
Yucekaya,A.D.
Ahmet Deniz Yücekaya
Yucekaya, Ahmet Deniz
YÜCEKAYA, Ahmet Deniz
Y.,Ahmet Deniz
Yücekaya, AHMET DENIZ
Y., Ahmet Deniz
Yücekaya, Ahmet Çelebi
Yücekaya, Ahmet Deniz
Yucekaya, Ahmet
Yücekaya, Ahmet
Job Title
Prof. Dr.
Email Address
Main Affiliation
Industrial Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
4
QUALITY EDUCATION
0
Research Products
6
CLEAN WATER AND SANITATION
6
Research Products
10
REDUCED INEQUALITIES
0
Research Products
13
CLIMATE ACTION
2
Research Products
14
LIFE BELOW WATER
0
Research Products
2
ZERO HUNGER
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
1
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
3
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
5
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
1
NO POVERTY
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
8
Research Products
15
LIFE ON LAND
0
Research Products
3
GOOD HEALTH AND WELL-BEING
1
Research Products
7
AFFORDABLE AND CLEAN ENERGY
7
Research Products
5
GENDER EQUALITY
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
Documents
34
Citations
539
h-index
13
Documents
22
Citations
423
Scholarly Output
48
Articles
30
Views / Downloads
10/0
Supervised MSc Theses
9
Supervised PhD Theses
2
WoS Citation Count
359
Scopus Citation Count
493
WoS h-index
11
Scopus h-index
13
Patents
0
Projects
0
WoS Citations per Publication
7.48
Scopus Citations per Publication
10.27
Open Access Source
27
Supervised Theses
11
| Journal | Count |
|---|---|
| International Journal of Energy Economics and Policy | 7 |
| Journal of Hydrologic Engineering | 4 |
| Energy Sources, Part B: Economics, Planning, and Policy | 3 |
| Renewable and Sustainable Energy Reviews | 2 |
| Energy Strategy Reviews | 2 |
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48 results
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Now showing 1 - 10 of 48
Article Citation - Scopus: 14Nutrient Dynamics in Flooded Wetlands. Ii: Model Application(2013) Kalin,L.; Hantush,M.M.; Isik,S.; Yucekaya,A.; Jordan,T.In this paper, the authors applied and evaluated the wetland nutrient model that was described in Paper I. Hydrologic and water quality data from a small restored wetland located on Kent Island,Maryland, which is part of the Delmarva Peninsula on the eastern shores of the Chesapeake Bay, was used for this purpose. The model was assessed through various methods against the observed data in simulating nitrogen (N), phosphorus (P), and total suspended sediment (TSS) dynamics. Time series plots of observed and simulated concentrations and loads generally compared well; better performance was demonstrated with dissolved forms of nitrogen, i.e., ammonia and nitrate. Through qualitative and quantitative sensitivity analysis, dominant processes in the study wetland were scrutinized. Nitrification, plant uptake, and mineralization were the most important processes affecting ammonia. Denitrification in the sediment layer and diffusion to bottom sediments were identified as key processes for nitrate. Settling and resuspension were the most important processes for particulate matter (organic N, sediment) and sediment-bound phosphate (inorganic P). Order of parameter sensitivities and dominant processes exhibited seasonality. Uncertainty bands created from Monte Carlo simulations showed that parameter uncertainty is relatively small; however, uncertainty in the wetland inflow rates and loading concentrations have much more bearing on model predictive uncertainty. N, P, and TSS mass balance analysis showed that the wetland removed approximately 23, 33, and 46%, respectively, of the incoming load (runoff + atmospheric deposition) over the two-year period, with more removal in year 1 (34, 43, and 55%, respectively), which had a long stretch of a dry period. The developed model can be employed for exploring wetland response to various climatic and input conditions, and for deeper understanding of key processes in wetlands. © 2013 American Society of Civil Engineers.Review Citation - WoS: 12Citation - Scopus: 17Bidding of Price Taker Power Generators in the Deregulated Turkish Power Market(Pergamon-Elsevier Science Ltd, 2013) Yücekaya, Ahmet; Yücekaya, AhmetIn deregulated power markets power firms bid into the day-ahead power market either with buy offers or sell offers. The auction mechanism and competition determine the equilibrium price and quantity for each hour. If the bid price of a company is below the market clearing price then the offer of the company is accepted and rewarded with the market price. A company can be a price maker or price taker depending on the capacity it offers to the market. A price-taker unit must determine the right offer that will maximize their profit given price uncertainty and blind auction rules. This paper first examines power supply in the Turkish electricity market and bidding process. Then a marginal cost-based Monte Carlo method is developed to determine hourly and block bidding strategies of price taker units. Historical market prices are then implemented in a normal distribution to generate hourly price scenarios. A solution methodology is developed that maximizes the expected profit of each hourly and block bidding strategy over price scenarios. The generator is able to both evaluate the hourly bidding and block bidding strategies and find the best bidding strategy that will be submitted to the market using the proposed methodology. The model is illustrated for two coal units in Turkish power market and the results are presented. (C) 2013 Elsevier Ltd. All rights reserved.Doctoral Thesis Trend Forecast and Collection Management in Apparel Retail(Kadir Has Üniversitesi, 2022) Arkan, Ramazan; Agca Aktunc, Esra; Yücekaya, Ahmet DenizThis study addresses the new methods and some existing methods with a different approach for trend forecasting and using new trends in the collections in apparel retail industry. There are several approaches to determine the potential of fashion trends. This study describes several approaches for trend forecasting and develops methods for measuring the potential of new fashion trends with unknown potential and without sales data. Firstly, merchandise testing focuses on the process of testing products with new trends. It describes the test store selection, forecasting methods and analyze the accuracy of forecasting with real data. Secondly, Sales-Based Store Network of Stores model is presented to examine cross-store sales similarity and establishes a store network using Collaborative Filtering method as in recommendation systems. A clustering method like K-means is studied to cluster the stores using store network data. Moreover, Distribution of Collection into Store method focuses on distributing the main collection made for a category into each stores using some constraints such as capacity of stores, rates of product attributes in the main collection. Integer programming is used to distribute the collection. The sales potential of the new planned products is crucial. It is necessary to choose the products with highest potential among the hundreds of products. Prediction of products’ demand based on stores addresses a prediction model using sales data containing store features and product attributes with different forecasting methods with different parameters. Furthermore, store-based forecasts are used in Distribution of collection into stores method while selecting the best products for the stores.Article Citation - WoS: 32Citation - Scopus: 35Scheduling a Log Transport System Using Simulated Annealing(Elsevier Science, 2014) Haridass, Karunakaran; Valenzuela, Jorge; Yücekaya, Ahmet; McDonald, TimThe log truck scheduling problem under capacity constraints and time window constraints is an NP-hard problem that involves the design of best possible routes for a set of trucks serving multiple loggers and mills. The objective is to minimize the total unloaded miles traveled by the trucks. In this paper a simulated annealing - a meta-heuristic optimization method - that interacts with a deterministic simulation model of the log transport system in which the precedence and temporal relations among activities are explicitly accounted for is proposed. The results obtained by solving a small size problem consisting of four trucks two mills three loggers and four truck trips showed that the best solution could be found in less than two minutes. In addition the solution method is tested using data provided by a log delivery trucking firm located in Mississippi. The firm operates sixty-eight trucks to deliver loads from twenty-two logging operations to thirteen mill destinations. The routes assigned by a supervisory person are used as a benchmark to compare the manual generated solution to the solution obtained using the proposed method. (C) 2013 Elsevier Inc. All rights reserved.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 - Scopus: 19Optimization of Wastewater Treatment Systems for Growing Industrial Parks(Elsevier B.V., 2023) Savun-Hekimoğlu, B.; İşler, Z.; Hekimoğlu, M.; Burak, S.; Karlı, D.; Yücekaya, A.; Akpınar, E.Wastewater treatment is one of the crucial functions of industrial parks as wastewater from industrial facilities usually contains toxic compounds that can cause damage to the environment. To control their environmental loads, industrial parks make investment decisions for wastewater treatment plants. For this, they need to consider technical and economic factors as well as future growth projections as substantial construction and operational costs of wastewater treatment plants have to be shared by all companies in an industrial park. In this paper, we consider the long-term capacity planning problem for wastewater treatment facilities of a stochastically growing industrial park. By explicitly modeling randomness in the arrival of new tenants and their random wastewater discharges, our model calculates the future mean and variance of wastewater flow in the industrial park. Mean and variance are used in a Mixed Integer Programming Model to optimize wastewater treatment plant selection over a long planning horizon (30 years). By fitting our first model to empirical data from an industrial park in Turkey, we find that considering the variance of wastewater load is critical for long-term planning. Also, we quantify the economic significance of lowering wastewater discharges which can be achieved by water recycling or interplant water exchange. © 2023 Elsevier B.V.Conference Object A Comparative Application of Machine Learning Approaches To Win-Back Lost Customers(Institute of Electrical and Electronics Engineers Inc., 2023) Yildirim, S.; Yucekaya, A.D.; Hekimoglu, M.; Ozcan, B.Today's consumer is more knowledgeable and conscious than in the past. For this reason, it is quite possible for consumers to leave their service/product providers and start receiving service from another service/product provider. Without a recovery strategy, companies often do not target their lost disloyal customer portfolio correctly and encounter the problem of lost customers. Lost customers can cause loss both in economic terms and in terms of business potential. At the same time, lost customers can also be considered as profits given to rival companies. What if the companies could foresee lost customers who would not want to receive service from them again? Could companies win back their customers? At this point, the article proposes using machine learning methods to recover lost customers for service providers. The customers that are likely to be lost in the future are estimated using the article's past stories of an automotive company's lost customers. The data used is completely real. LGBM, XGBoost, and Random Forest methods were used to estimate lost customers. Finally, the authors select the machine learning with the highest predictive success for customer recovery and discuss why this method might have worked well. © 2023 IEEE.Conference Object Citation - Scopus: 1Genesys-Mod Turkey: Quantitative Scenarios for Low Carbon Futures of the Turkish Energy System(IEEE Computer Society, 2022) Hasturk, I.S.; Celebi, E.; Yucekaya, A.D.; Kirkil, G.This paper examines the quantitative scenarios for low-carbon futures of the Turkish energy system at aggregated (country level) and regionally disaggregated (NUTS-1 level) levels. We have employed four different storylines for the future European energy system. They are quantified and implemented for the European energy system (30 regions, mostly single countries, including Turkey) using the open-source global energy system model, GENeSYS-MOD v3.0. We have compared the results of all scenarios at aggregated and disaggregated levels and found that there are significant differences among them. Specifically, the hydrogen production (and its use) has increased considerably in the disaggregated model when compared to the aggregated level results. The major reason for these differences is found to be the better estimation of regional renewable capacity factors (wind and solar) in the disaggregated level compared to aggregated level. © 2022 IEEE.Doctoral Thesis Models for Electricity Demand Forecasting, Classification, and Imbalance Reduction in Competitive Markets(Kadir Has Üniversitesi, 2023) Yükseltan, Ergün; Yücekaya, Ahmet Deniz; Bilge, AyşeIn liberalized energy markets, hourly forecasts of consumers and producers are crucial for efficiently using energy resources and reducing environmental impacts. In this study, the countries’ consumption in the ENTSO-E common network between 2006 and 2018 was analyzed using the time series method. With the created model, short, medium, and long-term demand forecasts are made using Fourier Series Expansion. In order to improve the error rate of short-term forecasts, a hybrid model was created with alternatively created feedback and autoregressive methods. While annual forecasts are made with an average error rate of 6%, the error rate in daily forecasts is around 4.5%. With the hybrid models created, hourly estimates can be made with approximately 1.5% and 1% error rates. Accurate estimations are of great importance in terms of the efficiency of energy markets, and the emergence of energy storage opportunities with the developing technology increases this importance. For this reason, the amount of imbalance was estimated by using the forecast result of the hybrid model in the Turkish Energy Market, and a strategy was developed to reduce the imbalance cost accordingly. With this strategy, simulations have been made for situations with and without storage, and the results have been shared.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.
