Abstract
The economic climate for any process, product or industry isinfluenced by numerous variables. Any organisation willing tothrive must make deliberate effort to adequately understand theinteractions between the elements, factors and variablesinfluencing its economic climate. Economic analysis is a toolwhich determines how effectively a system is operating, or willoperate, from an economic standpoint. The insight obtainedfrom economic analysis provides useful information requiredfor informed decision making. However, the reliability of anyeconomic analysis is greatly influenced by accuracy in theadopted price of capital assets. This is especially true forinvestments demanding high capital such as power plantprojects which require largely capital intensive assets like gasturbines as prime movers.In this study, a model is developed which applies regressionanalysis to estimate the acquisition cost of gas turbine unitsfrom a dataset of historical records of gas turbine engineperformance parameters and acquisition costs. As a validationto the implemented approach, the developed model is appliedto estimate the acquisition cost of known gas turbine units.Results obtained from model predictions reveal an estimatingaccuracy between 72% and 98% with a coefficient ofdetermination (R2) of 94% and strong positive correlation (r) of0.97 between the considered dependent and independentvariables.

Keywords
Gas Turbine, Regression Analysis, Price Estimation, Acquisition Cost, Power Plant

Cite this paper
David Olusina Rowlands, Mark Savill, GAS TURBINE ENGINE PRICE ESTIMATION USING REGRESSION ANALYSIS , SCIREA Journal of Energy. Volume 5, Issue 1, February 2020 | PP. 1-31.

References

[ 1 ]	O. Akinyemi, “Gas Turbines Performance Prognostics,” Cranfield University, Bedfordshire, 2008.
[ 2 ]	M. A. Saint-Germain, “Research methods-Simple Regression,” Carlifornia State University, Long Beach, Carlifornia, 2001.
[ 3 ]	Y. G. Li and P. Nilkitsaranont, “Gas turbine performance prognostic for condition-based maintenace,” Journal of Applied Energy, vol. 86, no. 10, pp. 2152-2161, 2009.
[ 4 ]	National Aeronautics and Space Administration, NASA, “Cost Estimating Methodologies - Parametric Cost Estimating,” in NASA Cost Estimating Handbook, NASA, 2015, pp. 16-18.
[ 5 ]	D. O. Rowlands, “Application of Gas Turbine Performance Prognostics to Two Single Spool Gas Turbine Engines,” Cranfield University, Bedfordshire, 2014.
[ 6 ]	C. Liu, R. Jin, E. Gong, Y. Liu and M. Yue, “Predicition for the performance of Gas trubine Units Using Multiple Linear Regression,” in Proceeding of the Chinese Society of Electrical Engineering, China, 2017.
[ 7 ]	E. Tsoutsanis and N. Meskin, “Derivative-driven window-based regression method for gas turbine performance prognostics,” Energy, vol. 128, pp. 302-311, 2017.
[ 8 ]	A. Kumar, A. Banerjee, A. Srivastava and N. Goel, “Prediction of Exhaust Gas Temperature in GTE by Multivariate Regression Analysis And Anomaly Detection,” in IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), Toronto, ON, Canada, 2014.
[ 9 ]	J. L. Hamilton and J. T. Wormley, “Application of Regression Analysis to Cost Analysis,” US Army Materiel Command, McLean, Virginia, 1968.
[ 10 ]	Nye Thermodynamics Corporation, “Gas Turbine Prices $ per KW,” Nye Thermodynamics Corporation, Ontario, 2016.
[ 11 ]	Gas Turbine International, “Latest News - General Electric Sells,” Sawyer's Gas Turbine International, vol. 18, no. 4, p. 56, 1977.
[ 12 ]	Pequot Publishing Inc, Gas Turbine World Handbook, Southport: Pequot Publishing Inc, 1973-2017.
[ 13 ]	U.S. Bureau of Labor, “Statistics, U.S. Bureau of Labor Statistics- Databases, Tables & Calculators by Subject,” U.S. Bureau of Labor, Washington, DC, 2017.
[ 14 ]	J. Higgins, The Radical Statistician, 5th ed., Carlifornia: The Management Advantage, Inc., 2005.
[ 15 ]	D. Rowlands, “Techno-Economic Research Analysis for Effective Power Generation from Aero-Engines with Minimal Emissions,” Cranfield University, Cranfield, 2018.
[ 16 ]	Forecast International, “Industrial Trent 60 / MT30 Marine Engine,” PowerWeb - A Forecast International Inc. Subsidiary, Newtown, USA, 2018.