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

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dc.contributor.author Shiri, Ali
dc.contributor.author Afshar, Mohammad
dc.contributor.author Rahimi-Kian, Ashkan
dc.contributor.author Maham, Behrouz
dc.date.accessioned 2016-11-30T04:59:22Z
dc.date.available 2016-11-30T04:59:22Z
dc.date.issued 2015
dc.identifier.citation Ali 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/123456789/2024 ru_RU
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/2024
dc.description.abstract Accurate 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.language.iso en ru_RU
dc.publisher 2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE) ru_RU
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Support vector machines ru_RU
dc.subject Predictive models ru_RU
dc.subject Forecasting ru_RU
dc.subject Hidden Markov models ru_RU
dc.subject Training ru_RU
dc.subject Natural gas ru_RU
dc.subject Time series analysis ru_RU
dc.title Electricity Price Modeling Using Support Vector Machines by Considering Oil and Natural Gas Price Impacts ru_RU
dc.type Conference Paper ru_RU


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