A novel method for prediction of gas turbine power production: Degree-day method
Author(s) -
Ümit Ünver,
Alper Keleşoğlu,
Muhsin Kılıç
Publication year - 2018
Publication title -
thermal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.339
H-Index - 43
eISSN - 2334-7163
pISSN - 0354-9836
DOI - 10.2298/tsci170915015u
Subject(s) - production (economics) , gas turbines , degree (music) , power (physics) , environmental science , electricity generation , turbine , energy (signal processing) , computer science , linear regression , process engineering , econometrics , mathematics , statistics , mechanical engineering , economics , thermodynamics , engineering , physics , acoustics , macroeconomics , machine learning
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.
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