Long-term Solar Irradiance Forecasting Approaches – A Comparative Study

Sharika, w.; Fernando, l.; Kanagasundaram, A.; Valluvan, R.; Anantharajah, K.

dc.contributor.author	Sharika, w.
dc.contributor.author	Fernando, l.
dc.contributor.author	Kanagasundaram, A.
dc.contributor.author	Valluvan, R.
dc.contributor.author	Anantharajah, K.
dc.date.accessioned	2021-02-15T05:04:33Z
dc.date.accessioned	2022-06-27T09:57:59Z
dc.date.available	2021-02-15T05:04:33Z
dc.date.available	2022-06-27T09:57:59Z
dc.date.issued	2018
dc.identifier.citation	Sharika, 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.uri	http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1412
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	IEEE	en_US
dc.subject	Solar irradiance	en_US
dc.subject	forecasting,	en_US
dc.title	Long-term Solar Irradiance Forecasting Approaches – A Comparative Study	en_US
dc.type	Article	en_US