Endüstri Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/204
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Browsing Endüstri Mühendisliği Bölümü Koleksiyonu by Author "0000-0001-9192-0782"
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Article Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models(Sakarya University, 2022) Sütçü, Muhammed; Şahin, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gülbahar, İbrahim Tümay; 0000-0002-8523-9103; 0000-0001-9786-6270; 0000-0001-6198-569X; 0000-0001-9264-4345; 0000-0001-9192-0782; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Şahin, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gülbahar, İbrahim TümayLoad forecasting is an essential task which is executed by electricity retail companies. By predicting the demand accurately, companies can prevent waste of resources and blackouts. Load forecasting directly affect the financial of the company and the stability of the Turkish Electricity Market. This study is conducted with an electricity retail company, and main focus of the study is to build accurate models for load. Datasets with novel features are preprocessed, then deep learning models are built in order to achieve high accuracy for these problems. Furthermore, a novel method for solving regression problems with classification approach (discretization) is developed for this study. In order to obtain more robust model, an ensemble model is developed and the success of individual models are evaluated in comparison to each otherArticle A NEW RATIONAL CLASSIFICATION APPROACH BY THE NEW MIXED DATA BINARIZATION METHOD(Süleyman Demirel Üniversitesi, 2023) Sütçü, Muhammed; Gülbahar, İbrahim Tümay; 0000-0001-9192-0782; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Gülbahar, İbrahim TümayClassification algorithm is a supervised learning technique that is used to identify the category of new observations. However, in some cases, quantitative and qualitative data must be used together. With this approach, we tried to overcome the problems encountered in using quantitative and qualitative data together. In this paper, we model a new classification technique by converting all types of data to binary data because in the real world, data are classified in different types such as binary, numeric, or categorical. By this way, we develop a more accurate and efficient mixed data binarization approach for multi-attribute data classification problems. First, we determine the classes from available dataset and then we classify the new instances into these predetermined classes by using the new proposed data binarization approach. We show how each step of this algorithm could be performed efficiently with a numeric example. Then, we apply the proposed approach on a well-known iris dataset and our model show promising results and improvements over previous approaches.Article Optimal location determination of electric vehicle charging stations: A case study on Turkey's most preferred highway(Bitlis Eren Üniversitesi, 2022) Sütçü, Muhammed; Gülbahar, İbrahim Tümay; 0000-0002-8523-9103; 0000-0001-9192-0782; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Gülbahar, İbrahim TümayToday, electric vehicles are seen as one of the most suitable and environmentally friendly alternatives to internal combustion engine vehicles. An important issue related to the dissemination of electric vehicles is the location of the vehicle charging network and specifically the optimum location selection of the charging stations. Generally, most of the studies focus on popular destinations such as city centers, shopping areas, bus stations, and airports. Although these places are often used in normal life, they can usually provide an adequate solution for daily charging needs due to the number of alternative charging stations. However, finding adequate charging stations is not possible in intercity travels especially in highways. In this paper, we proposed a decision model to determine the location of electric car charging stations in highways. We create an optimization model to decide the optimum locations for the charging stations that can meet the customer demands on the Istanbul-Ankara highway. The proposed model determines optimum charging stations that enable passengers traveling with their electric vehicles to travel in Istanbul-Ankara highway in the shortest time.Article Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand(MDPI, 2023) Gulbahar, Ibrahim Tumay; Sutcu, Muhammed; Almomany, Abedalmuhdi; Ibrahim, Babul Salam K. S. M. Kader; 0000-0001-9192-0782; 0000-0002-8523-9103; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Gulbahar, Ibrahim Tumay; Sutcu, MuhammedElectric vehicles have emerged as one of the top environmentally friendly alternatives to traditional internal combustion engine vehicles. The development of a comprehensive charging infrastructure, particularly determining the optimal locations for charging stations, is essential for the widespread adoption of electric vehicles. Most research on this subject focuses on popular areas such as city centers, shopping centers, and airports. With numerous charging stations available, these locations typically satisfy daily charging needs in routine life. However, the availability of charging stations for intercity travel, particularly on highways, remains insufficient. In this study, a decision model has been proposed to determine the optimal placement of electric vehicle charging stations along highways. To ensure a practical approach to the location of charging stations, the projected number of electric vehicles in Türkiye over the next few years is estimated by using a novel approach and the outcomes are used as crucial input in the facility location model. An optimization technique is employed to identify the ideal locations for charging stations on national highways to meet customer demand. The proposed model selects the most appropriate locations for charging stations and the required number of chargers to be installed, ensuring that electric vehicle drivers on highways do not encounter charging problems.