Electricity Price Modeling Using Support Vector Machines by Considering Oil and Natural Gas Price Impacts

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dc.contributor.authorShiri, Ali
dc.contributor.authorAfshar, Mohammad
dc.contributor.authorRahimi-Kian, Ashkan
dc.contributor.authorMaham, Behrouz
dc.date.accessioned2016-11-30T04:59:22Z
dc.date.available2016-11-30T04:59:22Z
dc.date.issued2015
dc.description.abstractAccurate electricity price prediction is one of the most important parts of decision making for electricity market participants to make reasonable competing strategies. Support Vector Machine (SVM) is a novel algorithm based on a predictive modeling method and a powerful classification method in machine learning and data mining. Most of SVM-based and non-SVM-based models ignore other important factors in the electricity price dynamics and electricity price models are built regard to just historical electricity prices; However, electricity price has a strong correlation with other variables like oil and natural gas price. In this paper, single SVM model is used to combine diverse influential variables as 1-Historical Electricity Price of Germany 2-GASPOOL price as first natural gas reference price 3-Net-Connect-Germany (NCG) price as second natural gas reference price 4- West Texas Intermediate (WTI) daily price as US oil benchmark. The simulation results show that using oil and natural gas prices can improve SVM model prediction ability compared to the SVM models built on mere historical electricity price.ru_RU
dc.identifier.citationAli Shiriz, Mohammad Afshar, Ashkan Rahimi-Kian and Behrouz Maham; 2015; Electricity Price Modeling Using Support Vector Machines by Considering Oil and Natural Gas Price Impacts; 2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE); http://nur.nu.edu.kz/handle/123456789/2024ru_RU
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/2024
dc.language.isoenru_RU
dc.publisher2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE)ru_RU
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectSupport vector machinesru_RU
dc.subjectPredictive modelsru_RU
dc.subjectForecastingru_RU
dc.subjectHidden Markov modelsru_RU
dc.subjectTrainingru_RU
dc.subjectNatural gasru_RU
dc.subjectTime series analysisru_RU
dc.titleElectricity Price Modeling Using Support Vector Machines by Considering Oil and Natural Gas Price Impactsru_RU
dc.typeConference Paperru_RU

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