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DC Field | Value | Language |
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dc.contributor.author | Jirakom Sirisrisakulchai | en_US |
dc.contributor.author | Songsak Sriboonchitta | en_US |
dc.date.accessioned | 2018-09-04T10:12:31Z | - |
dc.date.available | 2018-09-04T10:12:31Z | - |
dc.date.issued | 2015-01-01 | en_US |
dc.identifier.issn | 03029743 | en_US |
dc.identifier.other | 2-s2.0-84958550330 | en_US |
dc.identifier.other | 10.1007/978-3-319-25135-6_43 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84958550330&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/54369 | - |
dc.description.abstract | © Springer International Publishing Switzerland 2015. Modeling of daily peak electricity demand is very crucial for reliability and security assessments of electricity suppliers as well as of electricity regulators. The aim of this paper is to model the peak electricity demand using the dynamic Peak-Over-Threshold approach. This approach uses the vector of covariates including time variable for modeling extremes. The effect of temperature and time dependence on shape and scale parameters of Generalized Pareto distribution for peak electricity demand is investigated and discussed in this article. Finally, the conditional return levels are computed for risk management. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Mathematics | en_US |
dc.title | Modeling daily peak electricity demand in Thailand | en_US |
dc.type | Conference Proceeding | en_US |
article.title.sourcetitle | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) | en_US |
article.volume | 9376 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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