Short-term wind power forecasting using a Markov model

dc.contributor.authorJeyakumar, P
dc.contributor.authorKolambage, N
dc.contributor.authorGeeganage, N
dc.contributor.authorAmarasinghe, G
dc.contributor.authorAbeygunawardane, SK
dc.contributor.editorAbeykoon, AMHS
dc.contributor.editorVelmanickam, L
dc.date.accessioned2022-03-26T09:25:46Z
dc.date.available2022-03-26T09:25:46Z
dc.date.issued2021-09
dc.description.abstractLarge-scale wind power integration to power systems has been significantly increasing since the last decade. However, the reliability of power systems tends to degrade due to the intermittency and uncontrollability of wind power. Future wind power generation forecasts can be used to reduce the impacts of intermittency and uncontrollability of wind power on the reliability of power systems. This paper proposes a Markov chain-based model for the short-term forecasting of wind power. The first-order and second-order Markov chain principles are used as they require lesser memory and have lower complexities. Seasonal variation is also incorporated into the proposed model to further improve the accuracy. Results obtained from both Markov models are validated with real wind power output data and evaluated using evaluation metrics such as Mean Square Error and Root Mean Square Error. The results show that the accuracy of the first-order and second-order Markov models for a high wind regime is 81.33% and 82.61%, respectively and for a low wind regime is 83.50% and 89.27% respectively.en_US
dc.identifier.citationJeyakumar, P., Kolambage, N., Geeganage, N. & Amarasinghe, G. (2021). Short-term wind power forecasting using a Markov model. In A.M.H.S. Abeykoon & L. Velmanickam (Eds.), Proceedings of 3rd International Conference on Electrical Engineering 2021 (pp. 25-30). Institute of Electrical and Electronics Engineers, Inc. https://ieeexplore.ieee.org/xpl/conhome/9580924/proceedingen_US
dc.identifier.conference3rd International Conference on Electrical Engineering 2021en_US
dc.identifier.departmentDepartment of Electrical Engineeringen_US
dc.identifier.email160620g@uom.lken_US
dc.identifier.email160309l@uom.lken_US
dc.identifier.email160320l@uom.lken_US
dc.identifier.emailra-gihan@uom.lken_US
dc.identifier.emailsarangaa@uom.lken_US
dc.identifier.facultyEngineeringen_US
dc.identifier.pgnospp. 25-30en_US
dc.identifier.placeColomboen_US
dc.identifier.proceedingProceedings of 3rd International Conference on Electrical Engineering 2021en_US
dc.identifier.urihttp://dl.lib.uom.lk/handle/123/17474
dc.identifier.year2021en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers, Inc.en_US
dc.relation.urihttps://ieeexplore.ieee.org/xpl/conhome/9580924/proceedingen_US
dc.subjectWind power forecasten_US
dc.subjectMarkov chainen_US
dc.subjectshort-term forecasten_US
dc.subjectwind poweren_US
dc.titleShort-term wind power forecasting using a Markov modelen_US
dc.typeConference-Full-texten_US

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