Loading…
Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost
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 enginee...
Saved in:
| Published in: | Nuclear engineering and technology 2017, 49(5), , pp.1063-1070 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c440t-31eda728dbf9cfd7956f8d61989806f6077806b4aebb25b079c4dcdebae59373 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c440t-31eda728dbf9cfd7956f8d61989806f6077806b4aebb25b079c4dcdebae59373 |
| container_end_page | 1070 |
| container_issue | 5 |
| container_start_page | 1063 |
| container_title | Nuclear engineering and technology |
| container_volume | 49 |
| creator | Kim, Sungki Ko, Wonil Nam, Hyoon Kim, Chulmin Chung, Yanghon Bang, Sungsig |
| description | 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. |
| doi_str_mv | 10.1016/j.net.2017.05.007 |
| format | article |
| fullrecord | <record><control><sourceid>elsevier_nrf_k</sourceid><recordid>TN_cdi_nrf_kci_oai_kci_go_kr_ARTI_1423685</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1738573317300992</els_id><doaj_id>oai_doaj_org_article_6ce440e81eb849528cfec584b0c58e19</doaj_id><sourcerecordid>S1738573317300992</sourcerecordid><originalsourceid>FETCH-LOGICAL-c440t-31eda728dbf9cfd7956f8d61989806f6077806b4aebb25b079c4dcdebae59373</originalsourceid><addsrcrecordid>eNp9UcGOFCEQJUYTx9UP8MbVQ7fQQEPH02aj6ySbmOgc9kZoKEZmexoDjMn-vTUzxqMHeKmi3kvxHiHvOes54-PHQ79C6wfGdc9Uz5h-QTbDIGQnlHl8STZcC9MpLcRr8qbWA2OjlJptyOOP5lqqLXm30GMOsNCYy_mAd9he9_RU3JpOR_qrJA-VtkwBH46uAW0_ga4nv4BDygm5_hkL6nNtb8mr6JYK7_7iDdl9-by7-9o9fLvf3t0-dF5K1jrBITg9mDDHycegJzVGE0Y-mcmwMY5Ma8RZOpjnQc1MT14GH2B2oCahxQ35cJVdS7RPPtns0gX32T4Ve_t9t7VcDmI0Cme319mQ3cHid46uPF8Il0Yue-sKOrGAHT3gemA4zEZOajA-gldGzgxv4BNq8auWL7nWAvGfHmf2nIg9WEzEnhOxTFlMBDmfrhxAP34nKLb6BKuHkNDthluk_7D_AGP8lMc</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost</title><source>ScienceDirect Additional Titles</source><creator>Kim, Sungki ; Ko, Wonil ; Nam, Hyoon ; Kim, Chulmin ; Chung, Yanghon ; Bang, Sungsig</creator><creatorcontrib>Kim, Sungki ; Ko, Wonil ; Nam, Hyoon ; Kim, Chulmin ; Chung, Yanghon ; Bang, Sungsig</creatorcontrib><description>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.</description><identifier>ISSN: 1738-5733</identifier><identifier>EISSN: 2234-358X</identifier><identifier>DOI: 10.1016/j.net.2017.05.007</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>ARIMA Model ; Cost Driver ; Forecasting ; Nuclear Fuel Cycle Cost ; Uranium Price ; 원자력공학</subject><ispartof>Nuclear Engineering and Technology, 2017, 49(5), , pp.1063-1070</ispartof><rights>2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c440t-31eda728dbf9cfd7956f8d61989806f6077806b4aebb25b079c4dcdebae59373</citedby><cites>FETCH-LOGICAL-c440t-31eda728dbf9cfd7956f8d61989806f6077806b4aebb25b079c4dcdebae59373</cites><orcidid>0000-0003-2738-8335</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1738573317300992$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3535,27903,27904,45759</link.rule.ids><backlink>$$Uhttps://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002249747$$DAccess content in National Research Foundation of Korea (NRF)$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Sungki</creatorcontrib><creatorcontrib>Ko, Wonil</creatorcontrib><creatorcontrib>Nam, Hyoon</creatorcontrib><creatorcontrib>Kim, Chulmin</creatorcontrib><creatorcontrib>Chung, Yanghon</creatorcontrib><creatorcontrib>Bang, Sungsig</creatorcontrib><title>Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost</title><title>Nuclear engineering and technology</title><description>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.</description><subject>ARIMA Model</subject><subject>Cost Driver</subject><subject>Forecasting</subject><subject>Nuclear Fuel Cycle Cost</subject><subject>Uranium Price</subject><subject>원자력공학</subject><issn>1738-5733</issn><issn>2234-358X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9UcGOFCEQJUYTx9UP8MbVQ7fQQEPH02aj6ySbmOgc9kZoKEZmexoDjMn-vTUzxqMHeKmi3kvxHiHvOes54-PHQ79C6wfGdc9Uz5h-QTbDIGQnlHl8STZcC9MpLcRr8qbWA2OjlJptyOOP5lqqLXm30GMOsNCYy_mAd9he9_RU3JpOR_qrJA-VtkwBH46uAW0_ga4nv4BDygm5_hkL6nNtb8mr6JYK7_7iDdl9-by7-9o9fLvf3t0-dF5K1jrBITg9mDDHycegJzVGE0Y-mcmwMY5Ma8RZOpjnQc1MT14GH2B2oCahxQ35cJVdS7RPPtns0gX32T4Ve_t9t7VcDmI0Cme319mQ3cHid46uPF8Il0Yue-sKOrGAHT3gemA4zEZOajA-gldGzgxv4BNq8auWL7nWAvGfHmf2nIg9WEzEnhOxTFlMBDmfrhxAP34nKLb6BKuHkNDthluk_7D_AGP8lMc</recordid><startdate>201708</startdate><enddate>201708</enddate><creator>Kim, Sungki</creator><creator>Ko, Wonil</creator><creator>Nam, Hyoon</creator><creator>Kim, Chulmin</creator><creator>Chung, Yanghon</creator><creator>Bang, Sungsig</creator><general>Elsevier B.V</general><general>Elsevier</general><general>한국원자력학회</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope><scope>ACYCR</scope><orcidid>https://orcid.org/0000-0003-2738-8335</orcidid></search><sort><creationdate>201708</creationdate><title>Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost</title><author>Kim, Sungki ; Ko, Wonil ; Nam, Hyoon ; Kim, Chulmin ; Chung, Yanghon ; Bang, Sungsig</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c440t-31eda728dbf9cfd7956f8d61989806f6077806b4aebb25b079c4dcdebae59373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>ARIMA Model</topic><topic>Cost Driver</topic><topic>Forecasting</topic><topic>Nuclear Fuel Cycle Cost</topic><topic>Uranium Price</topic><topic>원자력공학</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Sungki</creatorcontrib><creatorcontrib>Ko, Wonil</creatorcontrib><creatorcontrib>Nam, Hyoon</creatorcontrib><creatorcontrib>Kim, Chulmin</creatorcontrib><creatorcontrib>Chung, Yanghon</creatorcontrib><creatorcontrib>Bang, Sungsig</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Directory of Open Access Journals</collection><collection>Korean Citation Index</collection><jtitle>Nuclear engineering and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Sungki</au><au>Ko, Wonil</au><au>Nam, Hyoon</au><au>Kim, Chulmin</au><au>Chung, Yanghon</au><au>Bang, Sungsig</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost</atitle><jtitle>Nuclear engineering and technology</jtitle><date>2017-08</date><risdate>2017</risdate><volume>49</volume><issue>5</issue><spage>1063</spage><epage>1070</epage><pages>1063-1070</pages><issn>1738-5733</issn><eissn>2234-358X</eissn><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.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.net.2017.05.007</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-2738-8335</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1738-5733 |
| ispartof | Nuclear Engineering and Technology, 2017, 49(5), , pp.1063-1070 |
| issn | 1738-5733 2234-358X |
| language | eng |
| recordid | cdi_nrf_kci_oai_kci_go_kr_ARTI_1423685 |
| source | ScienceDirect Additional Titles |
| subjects | ARIMA Model Cost Driver Forecasting Nuclear Fuel Cycle Cost Uranium Price 원자력공학 |
| title | Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T12%3A04%3A13IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_nrf_k&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Statistical%20model%20for%20forecasting%20uranium%20prices%20to%20estimate%20the%20nuclear%20fuel%20cycle%20cost&rft.jtitle=Nuclear%20engineering%20and%20technology&rft.au=Kim,%20Sungki&rft.date=2017-08&rft.volume=49&rft.issue=5&rft.spage=1063&rft.epage=1070&rft.pages=1063-1070&rft.issn=1738-5733&rft.eissn=2234-358X&rft_id=info:doi/10.1016/j.net.2017.05.007&rft_dat=%3Celsevier_nrf_k%3ES1738573317300992%3C/elsevier_nrf_k%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c440t-31eda728dbf9cfd7956f8d61989806f6077806b4aebb25b079c4dcdebae59373%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |