Exploiting artificial neural networks for the prediction of ancillary energy market prices

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Giovanelli, Christian
dc.contributor.author Sierla, Seppo
dc.contributor.author Ichise, Ryutaro
dc.contributor.author Vyatkin, Valeriy
dc.date.accessioned 2018-09-04T11:12:57Z
dc.date.available 2018-09-04T11:12:57Z
dc.date.issued 2018-01-01
dc.identifier.citation Giovanelli , C , Sierla , S , Ichise , R & Vyatkin , V 2018 , ' Exploiting artificial neural networks for the prediction of ancillary energy market prices ' Energies , vol 11 , no. 7 , 1906 . DOI: 10.3390/en11071906 en
dc.identifier.issn 1996-1073
dc.identifier.other PURE UUID: 16bdeef2-03f5-429e-a21d-31186215a161
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/exploiting-artificial-neural-networks-for-the-prediction-of-ancillary-energy-market-prices(16bdeef2-03f5-429e-a21d-31186215a161).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85051205985&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/27527703/ELEC_Giovanelli_etal_Exploiting_Artificial_Energies_11_7_1906_2018.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33798
dc.description.abstract The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price peaks and hours of low prices or zero prices, as well as to control the resource in such a way that exploits the price fluctuations. Thus, this study presents a solution in which artificial neural networks are exploited to predict the day-ahead ancillary energy market prices. The study employs the frequency containment reserve for the normal operations market as a case study and presents the methodology utilized for the prediction of the case study ancillary market prices. The relevant data sources for predicting the market prices are identified, then the frequency containment reserve market prices are analyzed and compared with the spot market prices. In addition, the methodology describes the choices behind the definition of the model validation method and the performance evaluation coefficient utilized in the study. Moreover, the empirical processes for designing an artificial neural network model are presented. The performance of the artificial neural network model is evaluated in detail by means of several experiments, showing robustness and adaptiveness to the fast-changing price behaviors. Finally, the developed artificial neural network model is shown to have better performance than two state of the art models, support vector regression and ARIMA, respectively. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Energies en
dc.relation.ispartofseries Volume 11, issue 7 en
dc.rights openAccess en
dc.subject.other Renewable Energy, Sustainability and the Environment en
dc.subject.other Energy Engineering and Power Technology en
dc.subject.other Energy (miscellaneous) en
dc.subject.other Control and Optimization en
dc.subject.other Electrical and Electronic Engineering en
dc.subject.other 213 Electronic, automation and communications engineering, electronics en
dc.title Exploiting artificial neural networks for the prediction of ancillary energy market prices en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Electrical Engineering and Automation
dc.contributor.department National Institute of Informatics
dc.subject.keyword Ancillary markets
dc.subject.keyword Demand response
dc.subject.keyword Energy markets
dc.subject.keyword Frequency containment reserve
dc.subject.keyword Machine learning
dc.subject.keyword Neural network
dc.subject.keyword Price prediction
dc.subject.keyword Smart grid
dc.subject.keyword Renewable Energy, Sustainability and the Environment
dc.subject.keyword Energy Engineering and Power Technology
dc.subject.keyword Energy (miscellaneous)
dc.subject.keyword Control and Optimization
dc.subject.keyword Electrical and Electronic Engineering
dc.subject.keyword 213 Electronic, automation and communications engineering, electronics
dc.identifier.urn URN:NBN:fi:aalto-201809044918
dc.identifier.doi 10.3390/en11071906
dc.type.version publishedVersion

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