Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 17Citation - Scopus: 24A Gis-Based Fahp and Fedas Analysis Framework for Suitable Site Selection of a Hybrid Offshore Wind and Solar Power Plant(Elsevier B.V., 2023) Karipoğlu, Fatih; Karipoğlu, Fatih; Ozturk, S.; Efe, B.; 03.06. Department of Energy Systems Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThis study presents a Geographic Information System (GIS) based suitable site selection methodology for a hybrid system that includes offshore wind and solar PV. The methodology utilizes open source databases about decision criteria and applies this data using GIS to determine suitable sites for offshore wind and solar PV systems. For the assessment of multi-criteria which affect the potential hybrid energy power plants and the determination of the best suitable areas, Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Evaluation based on Distance Average Solution (FEDAS) are used in the study. Results show that technical criteria has the priority weight of 0.60 while the weight of social criteria is about 0.07. Among sub-criteria, the wind speed has the highest priority weight while distance to port and visibility are the highest criteria of priority weight under economic and social main criteria, respectively. Among the alternatives, Area 2 (A-2) is determined as the best alternative for hybrid offshore power plants in the study area. This proposed methodology can be utilized by decision-makers to determine the best suitable locations for hybrid offshore wind and solar PV systems at any location. This paper suggests a new approach integrating GIS, fuzzy setbased AHP and EDAS as a novelty. © 2023 International Energy InitiativeConference Object Citation - WoS: 1Citation - Scopus: 2Fisher's Linear Discriminant Analysis Based Prediction Using Transient Features of Seismic Events in Coal Mines(Institute of Electrical and Electronics Engineers Inc., 2016) Köktürk Güzel, Başak Esin; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyIdentification of seismic activity levels in coal mines is important to avoid accidents such as rockburst. Creating an early warning system that can save lives requires an automated way of predicting. This study proposes a prediction algorithm for the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines that is based on transient activity features along with average indicators evaluated by a Fisher's linear discriminant analysis. Performance evaluation experiments on the training datasets revealed an accuracy level of around 0.9438 while the performance on the test dataset was at a level of 0.9297. These results suggest that the proposed approach achieves high accuracy in predicting danger seismic events while maintaining low complexity.