Long-term solar irradiance forecasting approaches a comparative study
| 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.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.identifier.uri | http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/5587 | |
| 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 |