Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1412
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dc.contributor.authorSharika, w.
dc.contributor.authorFernando, l.
dc.contributor.authorKanagasundaram, A.
dc.contributor.authorValluvan, R.
dc.contributor.authorAnantharajah, K.
dc.date.accessioned2021-02-15T05:04:33Z
dc.date.accessioned2022-06-27T09:57:59Z-
dc.date.available2021-02-15T05:04:33Z
dc.date.available2022-06-27T09:57:59Z-
dc.date.issued2018
dc.identifier.citationSharika, W., Fernando, L., Kanagasundaram, A., Valluvan, R., & Kaneswaran, A. (2018, December). Long-term Solar Irradiance Forecasting Approaches-A Comparative Study. In 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS) (pp. 1-6). IEEE.en_US
dc.identifier.urihttp://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1412-
dc.description.abstractThe need of solar irradiation forecast at a specific location over long-time horizons has attained massive importance. In this paper, we study the machine learning techniques to predict solar irradiation in 10 min intervals using data sets from Killinochchi district, Faculty of Engineering, University of Jaffna measuring center. The accuracies of the prediction models such as ARIMA, Random Forest Regression, Neural Networks, Linear Regression and Supportive Vector Machine is compared. This study suggests that ARIMA performs well over other approaches.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectSolar irradianceen_US
dc.subjectforecasting,en_US
dc.titleLong-term Solar Irradiance Forecasting Approaches – A Comparative Studyen_US
dc.typeArticleen_US
Appears in Collections:Computer Engineering

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