Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/34692
Title: A novel method for prediction of gas turbine power production degree-day method
Authors: Ünver, Ümit
Keleşoğlu, Alper
Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği Bölümü.
0000-0003-2113-4510
Kılıç, Muhsin
O-2253-2015
57202677637
Keywords: Thermodynamics
Gas turbine
Degree day
Prediction of energy production
Ambient temperature
Energy prediction
Environmental-temperature
Ambient-temperature
Plants
Optimization
Efficiency
Performance
Parameters
Fuel
Forecasting
Gases
Regression analysis
Degree days
Degree-day method
Energy
Energy productions
Marketing sectors
Novel methods
Power production
Turbine power
Gas turbines
Issue Date: 2018
Publisher: Vinca Institute of Nuclear Science
Citation: Ünver, Ü. vd. (2018). ''A novel method for prediction of gas turbine power production degree-day method''. Thermal Science, 22(Supplement 3), S809-S817.
Abstract: Gas turbines are widely used in the energy production. The quantity of the operating machines requires a special attention for prediction of power production in the energy marketing sector. Thus, the aim of this paper is to support the sector by making the prediction of power production more computable. By using the data from an operating power plant, correlation and regression analysis are performed and linear equation obtained for calculating useful power production vs atmospheric air temperature and a novel method, the gas turbine degree day method, was developed. The method has been addressed for calculating the isolation related issues for buildings so far. But in this paper, it is utilized to predict the theoretical maximum power production of the gas turbines in various climates for the first time. The results indicated that the difference of annual energy production capacity between the best and the last province options was calculated to be 7500 MWh approximately.
URI: https://doi.org/10.2298/TSCI170915015U
https://doiserbia.nb.rs/Article.aspx?ID=0354-98361800015U
http://hdl.handle.net/11452/34692
ISSN: 0354-9836
2334-7163
Appears in Collections:Scopus
Web of Science

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