Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
2 results
Search Results
Conference Object Cash Flow Forecasting by Using Time Series Methods in Geothermal District Heating Systems: Balcova - Narlidere Case(National Technical University of Athens, 2006) Erdoğmuş, Abdullah Berkan; Özerdem, BarışCash flow forecasting is one of the difficult and important tasks in an economic evaluation of a geothermal investment. Geothermal district heating systems are characterized by a high capital cost. In addition, relatively low operation and maintenance costs occur throughout their life. The aim of this research is to estimate the potential cash flows for Balcova - Narlidere Geothermal District Heating System by using historical data accumulated over a period of time and several forecasting methods: moving average, exponential smoothing, adjusted exponential smoothing and curve fitting functions. Mean absolute percentage deviation (MAPD) which is the most common approach to select the appropriate method to a particular time series is used in the selection of the most suitable model. Alternative methods are compared with each other regarding to their MAPD values. It is found that the models represented by exponential curve fitting functions have smaller MAPD values and give better results in cash flow forecasting of investment investigated.Conference 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, BilgeIdentification 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.