Ünlü, Kamil Demirberk
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Kamil Demirberk, Unlu
Unlu,K.D.
Unlu, Kamil Demirberk
U., Kamil Demirberk
K.D.Unlu
Ünlü,K.D.
Ü.,Kamil Demirberk
K.D.Ünlü
Kamil Demirberk, Ünlü
K. D. Unlu
U.,Kamil Demirberk
K.,Ünlü
Ünlü, Kamil Demirberk
Unlu K.
Unlu, K. D.
Ünlü K.
K., Unlu
Ü., Kamil Demirberk
K. D. Ünlü
Unlu,K.D.
Unlu, Kamil Demirberk
U., Kamil Demirberk
K.D.Unlu
Ünlü,K.D.
Ü.,Kamil Demirberk
K.D.Ünlü
Kamil Demirberk, Ünlü
K. D. Unlu
U.,Kamil Demirberk
K.,Ünlü
Ünlü, Kamil Demirberk
Unlu K.
Unlu, K. D.
Ünlü K.
K., Unlu
Ü., Kamil Demirberk
K. D. Ünlü
Job Title
Doçent Doktor
Email Address
demirberk.unlu@atilim.edu.tr
Main Affiliation
Industrial Engineering
Status
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
3
GOOD HEALTH AND WELL-BEING
3
Research Products
7
AFFORDABLE AND CLEAN ENERGY
5
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
1
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
5
Research Products
13
CLIMATE ACTION
1
Research Products
14
LIFE BELOW WATER
1
Research Products
Scholarly Output
21
Articles
16
Citation Count
131
Supervised Theses
1
20 results
Scholarly Output Search Results
Now showing 1 - 10 of 20
Article Citation - WoS: 13Citation - Scopus: 16Forecasting Air Quality in Tripoli: an Evaluation of Deep Learning Models for Hourly Pm2.5 Surface Mass Concentrations(Mdpi, 2023) Esager, Marwa Winis Misbah; Unlu, Kamil Demirberk; Industrial EngineeringIn this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli.Conference Object Relationships Between Stock Markets: Causality Between G8 Countries and Turkey(Springer international Publishing Ag, 2020) Unlu, Kamil Demirberk; Potas, Nihan; Yilmaz, Mehmet; Industrial EngineeringThis study investigated relationships between stock markets in the Group of Eight (G8) countries (Canada, France, Germany, Italy, Japan, Russia, the UK, and the USA) and the Istanbul Stock Exchange (ISE) by estimating eight different vector autoregressions (VARs). We applied the Johansen and Juselius cointegration test to identify the long-run relations between the indices. The modified Granger causality test proposed by Toda and Yamamoto was conducted to identify the causality, then forecast variance decomposition and impulse response analysis were employed to explore the impacts of unexpected shocks in the G8 countries' stock markets on the ISE. The results showed that there was no cointegration between the ISE and the G8 countries' markets, but they still affected the ISE to different degrees, and the DAX-ISE 100, CAC 40-ISE 100, and FTSE MIB-ISE 100 causal relationships were bidirectional.Article Citation - WoS: 16Citation - Scopus: 19A New Generalized Δ-Shock Model and Its Application To 1-out-of-(m+1):g Cold Standby System(Elsevier Sci Ltd, 2023) Eryilmaz, Serkan; Unlu, Kamil Demirberk; Industrial EngineeringAccording to the classical delta-shock model, the system failure occurs upon the occurrence of a new shock that arrives in a time length less than delta, a given positive value. In this paper, a new generalized version of the delta-shock model is introduced. Under the proposed model, the system fails if there are m shocks that arrive in a time length less than delta after a previous shock, m >= 1. The mean time to failure of the system is approximated for both discretely and continuously distributed intershock time distributions. The usefulness of the model is also shown to study 1-out-of-(m + 1):G cold standby system. Illustrative numerical results are presented for geometric, exponential, discrete and continuous phase-type intershock time distributions.Book Part Determining Harmonic Fluctuations in Food Inflation(World Scientific Publishing Co., 2022) Akdi,Y.; Ünlü,K.D.; Baş,C.; Karamanoğlu,Y.E.; Industrial EngineeringIn this study, we start with a brief expression of consumer price index of Turkey. In the next step, we give the theoretical essentials of periodogram-based unit root and harmonic regression model. Periodogram-based unit root test is used to identify both the stationarity of data and periodicities. Periodicity is beyond seasonality; it is the hidden cycles in the data. Thus, it is harder to detect them compared to seasonal cycles. Harmonic-regression-type trigonometric regression models are useful in modeling data which have hidden periodicity. Afterward, the stationarity properties of monthly inflation and monthly food inflation of Turkey for the period between 2004 and 2020 are investigated. Standard augmented Dickey-Fuller unit root test shows that both series are integrated of order one. However, the periodogram-based unit root test shows that monthly inflation has unit root but monthly food inflation does not. After examining the unit root, the hidden cycles in the food inflation are revealed. The cycles in food inflation are important because they may trigger a headline inflation. The main contribution of this study is the identification of the hidden cycles in food inflation. It has cycles of approximately two, four, six and eight years. These cycles, in short, correspond to cycles of two years of consecutive periods. © 2022 by World Scientific Publishing Europe Ltd.Article Wavelet-Enhanced Sequence-To Modeling With Attention Mechanism for Short-Term Wind Power Forecasting(Taylor & Francis inc, 2025) Karaca, Burak; Unlu, Kamil Demirberk; Turkan, Semra; Industrial EngineeringElectricity load forecasting is crucial to managing electric systems, especially loads produced from renewable energy sources since the load from renewable energy sources varies when compared with nonrenewable sources. Turkey is producing an increasing amount of electricity from wind energy every day. The aim of this study is to introduce a hybrid deep learning model based on sequence-to-sequence learning (seq-2-seq), attention mechanisms, and wavelet transformation. Long Short-Term Memory (LSTM), Gated Recurrent Unit, and Bidirectional Long Short-Term Memory (BiLSTM) are used as decoders and encoders in the seq-2-seq model. We proposed six different models. All models are univariate type, requiring only the data itself. The model can be used on any wind farms without requiring the meteorological data. We test the proposed model on four different wind farms in Turkey: Soma, Biga, Balikesir, and Mersin. We utilize four different performance metrics to test the model's performance: mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determinations (R2). The best model is seen as Wavelet-Seq2Seq-BiLSTM-LSTM at Biga Wind Farm, which achieved the best performance with a MAE of 0.127, an MSE of 0.001, a MAPE of 0.28, and an R2 of 0.997.Article Citation - WoS: 17Citation - Scopus: 17Periodicity in Precipitation and Temperature for Monthly Data of Turkey(Springer Wien, 2021) Akdi, Yilmaz; Unlu, Kamil Demirberk; Industrial EngineeringIn this study, we model and forecast monthly average temperature and monthly average precipitation of Turkey by employing periodogram-based time series methodology. We compare autoregressive integrated moving average methodology and harmonic regression. We show that harmonic regression performs better than the classical methodology in both time series. Also, we find that the monthly average temperature and monthly average precipitation have two different periodic structures of 6 months and 12 months which coincide with the seasonal pattern of the time series.Article Citation - WoS: 4Citation - Scopus: 5Strategic Electricity Production Planning of Turkey Via Mixed Integer Programming Based on Time Series Forecasting(Mdpi, 2023) Yoruk, Gokay; Bac, Ugur; Yerlikaya-Ozkurt, Fatma; Unlu, Kamil Demirberk; Industrial EngineeringThis study examines Turkey's energy planning in terms of strategic planning, energy policy, electricity production planning, technology selection, and environmental policies. A mixed integer optimization model is proposed for strategic electricity planning in Turkey. A set of energy resources is considered simultaneously in this research, and in addition to cost minimization, different strategic level policies, such as CO2 emission reduction policies, energy resource import/export restriction policies, and renewable energy promotion policies, are also considered. To forecast electricity demand over the planning horizon, a variety of forecasting techniques, including regression methods, exponential smoothing, Winter's method, and Autoregressive Integrated Moving Average methods, are used, and the best method is chosen using various error measures. The optimization model constructed for Turkey's Strategic Electricity Planning is obtained for two different planning intervals. The findings indicate that the use of renewable energy generation options, such as solar, wind, and hydroelectric alternatives, will increase significantly, while the use of fossil fuels in energy generation will decrease sharply. The findings of this study suggest a gradual increase in investments in renewable energy-based electricity production strategies are required to eventually replace fossil fuel alternatives. This change not only reduces investment, operation, and maintenance costs, but also reduces emissions in the long term.Article Citation - WoS: 16Citation - Scopus: 18Modeling and Forecasting of Monthly Pm2.5 Emission of Paris by Periodogram-Based Time Series Methodology(Springer, 2021) Akdi, Yilmaz; Golveren, Elif; Unlu, Kamil Demirberk; Yucel, Mustafa Eray; Industrial EngineeringIn this study, monthly particulate matter (PM2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.Conference Object Forecasting Direction of Bist 100 Index: an Integrated Machine Learning Approach(Springer Science and Business Media B.V., 2021) Ünlü,K.D.; Potas,N.; Yılmaz,M.; Civil Engineering; Industrial EngineeringIn recent years trends in analyzing and forecasting financial time series moves from classical Box-Jenkins methodology to machine learning algorithms because of the non-linearity and non-stationary of the time series. In this study, we employed a machine learning algorithm called support vector machine to predict the daily price direction of BIST 100 index. In addition, we use random forest algorithm for feature selection and showed that by removing some features from the model, performance of the model increases. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Article Citation - WoS: 17Citation - Scopus: 21A Data-Driven Model To Forecast Multi-Step Ahead Time Series of Turkish Daily Electricity Load(Mdpi, 2022) Unlu, Kamil Demirberk; Ünlü, Kamil Demirberk; Ünlü, Kamil Demirberk; Industrial Engineering; Industrial EngineeringIt is critical to maintain a balance between the supply and the demand for electricity because of its non-storable feature. For power-producing facilities and traders, an electrical load is a piece of fundamental and vital information to have, particularly in terms of production planning, daily operations, and unit obligations, among other things. This study offers a deep learning methodology to model and forecast multistep daily Turkish electricity loads using the data between 5 January 2015, and 26 December 2021. One major reason for the growing popularity of deep learning is the creation of new and creative deep neural network topologies and significant computational advancements. Long Short-Term Memory (LSTM), Gated Recurrent Network, and Convolutional Neural Network are trained and compared to forecast 1 day to 7 days ahead of daily electricity load. Three different performance metrics including coefficient of determination (R-2), root mean squared error, and mean absolute error were used to evaluate the performance of the proposed algorithms. The forecasting results on the test set showed that the best performance is achieved by LSTM. The algorithm has an R-2 of 0.94 for 1 day ahead forecast, and the metric decreases to 0.73 in 7 days ahead forecast.
