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dc.contributor.author Jeyakumar, P
dc.contributor.author Kolambage, N
dc.contributor.author Geeganage, N
dc.contributor.author Amarasinghe, G
dc.contributor.author Abeygunawardane, SK
dc.contributor.editor Abeykoon, AMHS
dc.contributor.editor Velmanickam, L
dc.date.accessioned 2022-03-26T09:25:46Z
dc.date.available 2022-03-26T09:25:46Z
dc.date.issued 2021-09
dc.identifier.citation Jeyakumar, 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/proceeding en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/17474
dc.description.abstract Large-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.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers, Inc. en_US
dc.relation.uri https://ieeexplore.ieee.org/xpl/conhome/9580924/proceeding en_US
dc.subject Wind power forecast en_US
dc.subject Markov chain en_US
dc.subject short-term forecast en_US
dc.subject wind power en_US
dc.title Short-term wind power forecasting using a Markov model en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Electrical Engineering en_US
dc.identifier.year 2021 en_US
dc.identifier.conference 3rd International Conference on Electrical Engineering 2021 en_US
dc.identifier.place Colombo en_US
dc.identifier.pgnos pp. 25-30 en_US
dc.identifier.proceeding Proceedings of 3rd International Conference on Electrical Engineering 2021 en_US
dc.identifier.email 160620g@uom.lk en_US
dc.identifier.email 160309l@uom.lk en_US
dc.identifier.email 160320l@uom.lk en_US
dc.identifier.email ra-gihan@uom.lk en_US
dc.identifier.email sarangaa@uom.lk en_US


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