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http://dx.doi.org/10.1016/j.net.2017.05.007

Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost  

Kim, Sungki (Nuclear Fuel Cycle Analysis, Korea Atomic Energy Research Institute)
Ko, Wonil (Nuclear Fuel Cycle Analysis, Korea Atomic Energy Research Institute)
Nam, Hyoon (Nuclear Fuel Cycle Analysis, Korea Atomic Energy Research Institute)
Kim, Chulmin (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology)
Chung, Yanghon (Department of Business and Technology Management, Korea Advanced Institute of Science and Technology)
Bang, Sungsig (Department of Business and Technology Management, Korea Advanced Institute of Science and Technology)
Publication Information
Nuclear Engineering and Technology / v.49, no.5, 2017 , pp. 1063-1070 More about this Journal
Abstract
This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation.
Keywords
ARIMA Model; Cost Driver; Forecasting; Nuclear Fuel Cycle Cost; Uranium Price;
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Times Cited By KSCI : 1  (Citation Analysis)
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