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

Kim, Sung-Ki; Ko, W.I.; Nam, Hyoon; Kim, Chul-Min; Chung, Yanghon; Bang, Sungsig

doi:10.1016/j.net.2017.05.007

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…indicating that the residuals are random and that the model provides an adequate fit to the data [26,58]. The above set of residual diagnostics are tested on all other monitoring points and the same kind of results are achieved.…”

Section: Tablementioning

confidence: 99%

“…For these reasons, researchers have taken advantage of the use of spatial-temporal simulation models to study the effects of radioactive fallout and isotope distributions [23,24]. Moreover, the autoregressive integrated moving average (ARIMA) model has already been used in the field of nuclear physics for forecasting the 226 Ra, 232 Th and 40 K concentrations in four regions of Istanbul [25], nuclear fuel cycle price estimation [26], and radon gas concentrations time-series estimation for earthquake prediction purposes [27,28]. It seems that, this model has not been used by scholars for estimating the Fukushima air dose rates.…”

Section: Jrncmentioning

confidence: 99%

“…In many fields such as health, economic, financial, engineering and environmental applications, and physics this model has been utilized. The general form of Autoregressive (AR), Moving average (MA) and ARIMA models are given by the equations below from [26,43]:…”

Section: The Autoregressive Integrated Moving Average (Arima) Modelmentioning

confidence: 99%

See 2 more Smart Citations

A mobile simulation and ARIMA modeling for prediction of air radiation dose rates

Salh

Külahçı

Aközcan

2021

J Radioanal Nucl Chem

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A spatial simulation method in .mp4 format was proposed to determine Fukushima radioactive fallout transport and the Absorbed Dose Rate, Annual Effective Dose Equivalent, and Excess Lifetime Cancer Risk were determined for 10 months after the accident (March 11 2011). The findings of this study demonstrate that an appropriate ARIMA model can be applied for radiation dose time-series in the case of nuclear reactor accidents like Chernobyl and Fukushima to predict the future air dose rates, which can provide valuable information in determining the evacuation zones, decontamination processes, and radiation protection progresses. The model forecasted results and the actual observation data in the same period shows a gradual decrease in the air dose rates during the prediction period. Moreover, there is a good agreement between them as the prediction and observation scatter plot follows each other with small variations. These results provide important insights into the predictability of ARIMA models; thus, the models were utilized to forecast the air dose rates for the period (January 2020 -October 2020).

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Section: Tablementioning

confidence: 99%

Section: Jrncmentioning

confidence: 99%

See 1 more Smart Citation

A mobile simulation and ARIMA modeling for prediction of air radiation dose rates

Salh

Külahçı

Aközcan

2021

J Radioanal Nucl Chem

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“…e SNF will be transported to a centralized dry storage, and, after the construction of a high-level repository, will be finally transported to that repository, as shown in Figures 3 and 4. OCDE 1994 [30] Charpin et al [29] Bunn 2003 [28] BCG 2006 [27] Ramana and Suchitra [26] De Roo and Parsons [25] Park et al [24] Ko and Gao [23] Recktenwald and Deinert [22] OCDE 2013 [21] Brinton and Kazimi [20] Zhou et al [19] Kim et al [18] Kim et al [17] Gao et al [16] Ganda et al [15] Zhang et al [14] Choi et al [13] Kim et al [12] Kim et al [11] Gao et al [10] Zhang et al [9] Yue et al [8] Chen et al [7] Krasnorutskyy and Kirsanova [6] International models Models applied classification (%)…”

Section: Site Status and Decommissioning Process Of Barakah Sitementioning

confidence: 99%

Technical Options and Cost Estimates for Spent Nuclear Fuel Management at the Barakah Nuclear Power Plants

Esmail

Cheong

2021

Science and Technology of Nuclear Installations

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In the planning and management of the interim storage of spent nuclear fuel, the technical and economic parameters that are involved have a significant role in increasing the efficiency of the storage system. Optimal parameters will reduce the total economic costs for countries embarking on nuclear energy, such as the UAE. This study evaluated the design performance and economic feasibility of various structures and schedules, to determine an optimal combination of parameters for the management of spent nuclear fuel. With the introduction of various storage technology arrangements and expected costs per unit for the storage system design, we evaluated eight major scenarios, each with a cost analysis based on technological and economic issues. We executed a number of calculations based on the use of these storage technologies, and considered their investment costs. These calculations, which were aligned with the net present value approach and conducted using MS Project and MATLAB software programs, considered the capacities of the spent fuel pools and the amount of spent nuclear fuel (SNF) that will be transferred to dry storage facilities. As soon as they sufficiently cool, the spent nuclear fuel is to be stored in a pool storage facility. The results show that applying a centralized dry storage (CDS) system strategy is not an economically feasible solution, compared with using a permanent disposal facility (PDF) (unless the variable investment cost is reduced or changed). The optimal strategy involves operating a spent fuel pool island (SFPI) storage after the first 20 years of the start of the permanent shutdown of the reactor. After 20 years, the spent fuel is then transferred to a PDF. This strategy also results in a 20.9% to 26.1% reduction in the total cost compared with those of the other strategies. The total cost of the proposed strategy is approximately 4,307 million USD. The duration of the fuel storage and the investment cost, particularly the variable investment cost, directly affect the choice of facility storage.

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“…The order of an ARIMA model is typically identified in the form of (p, d, q), where p indicates the order of the autoregressive part, d the amount of the difference and q the order of the moving average part (Jiang et al, 2018;Kim et al, 2017;Lasheras et al, 2015).…”

Section: Autoregressive Integrated Moving Averagementioning

confidence: 99%

Application of Arima Modelling for the Forecasting of Solar, Wind, Spot and Options Electricity Prices: The Australian National Electricity Market

Alsaedi

Tularam

Wong

2019

IJEEP

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This study aims to develop autoregressive integrated moving average (ARIMA) models to predict the solar, wind, spot and options pricing over the next 2 years, with historical data being used in a univariate manner to understand market behaviour in terms of trends. The assessment is made in the context of the Australian National Electricity Market (ANEM). The ARIMA models predict the future values of the monthly solar, wind, spot and options prices for various Australian states using time-series data from January 2006 to March 2018. The results show increases from 30.46% to 40.42% for the spot electricity prices and from 14.80% to 15.13% for the options electricity prices in the ANEM with a 2-year horizon. The results further show that wind prices are expected to increase by an average of 5.43%. However, the results also show that the average solar electricity prices will decrease by 67.7%.

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

Cited by 12 publications

References 10 publications

A mobile simulation and ARIMA modeling for prediction of air radiation dose rates

A mobile simulation and ARIMA modeling for prediction of air radiation dose rates

Technical Options and Cost Estimates for Spent Nuclear Fuel Management at the Barakah Nuclear Power Plants

Application of Arima Modelling for the Forecasting of Solar, Wind, Spot and Options Electricity Prices: The Australian National Electricity Market

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