Long-term solar irradiance forecasting approaches a comparative study

Sharika, W.; Fernando, L.; Kanagasundaram, A.; Valluvan, R.; Kaneswaran, A.

dc.contributor.author	Sharika, W.
dc.contributor.author	Fernando, L.
dc.contributor.author	Kanagasundaram, A.
dc.contributor.author	Valluvan, R.
dc.contributor.author	Kaneswaran, A.
dc.date.accessioned	2022-03-11T02:19:01Z
dc.date.accessioned	2022-06-27T10:02:04Z
dc.date.available	2022-03-11T02:19:01Z
dc.date.available	2022-06-27T10:02:04Z
dc.date.issued	2018
dc.identifier.uri	http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/5587
dc.description.abstract	The 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.iso	en	en_US
dc.publisher	University of Jaffna	en_US
dc.subject	Solar irradiance	en_US
dc.subject	Forecasting	en_US
dc.subject	Correlation	en_US
dc.subject	Exogenous inputs	en_US
dc.subject	prediction	en_US
dc.subject	Models	en_US
dc.title	Long-term solar irradiance forecasting approaches a comparative study	en_US
dc.type	Article	en_US