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dc.contributor.authorBremdal, Bernt Arild
dc.contributor.authorDadman, Shayan
dc.date.accessioned2024-01-29T10:40:08Z
dc.date.available2024-01-29T10:40:08Z
dc.date.issued2023-06
dc.description.abstractThe work presented has studied price developments in the day-ahead market. The statistical properties inherent in the time series constituting the day-to-day prices have been investigated. It is shown that these properties are radically different in 2022 than former years and resemble more chance like properties for which make predicting future peak prices especially hard. To overcome this, a space domain approach was adopted to determine whether information about co-variant price zones could improve predictions. By using a multi-variate regression method, it is possible to accurately predict prices in NO2 using information about concurrent prices in other price zones. However, an attempt to use historic prices in multiple price zones to predict future prices gave only modest results. One reason for this is the high degree of entropy underlying price developments in the day-ahead market in 2022.en_US
dc.identifier.citationBremdal, Dadman: Predicting Peak Prices in the Current Day-Ahead Market. In: CIRED 2023. 27th International Conference on Electricity Distribution - CIRED 2023, 2023. IET Digital Library p. 2385-2389en_US
dc.identifier.cristinIDFRIDAID 2183046
dc.identifier.doi10.1049/icp.2023.1244
dc.identifier.isbn978-1-83953-855-1
dc.identifier.urihttps://hdl.handle.net/10037/32757
dc.language.isoengen_US
dc.publisherThe Institution of Engineering and Technologyen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.titlePredicting Peak Prices in the Current Day-Ahead Marketen_US
dc.type.versionacceptedVersionen_US
dc.typeChapteren_US
dc.typeBokkapittelen_US


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