Volume 5, Number 1 (2020)
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Home > Journals > SCIREA Journal of Energy > Archive > Paper Information


Volume 5, Issue 1, February 2020    |    PP. 1-31    |PDF (1428 K)|    Pub. Date: September 25, 2020
   151 Downloads     1389 Views  

David Olusina Rowlands, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford MK43 0AL, UK
Mark Savill, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford MK43 0AL, UK

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.

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. Vol. 5 , No. 1 , 2020 , pp. 1 - 31 .


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