Show simple item record

dc.contributor.authorPun, Kesh B.
dc.contributor.authorBasnet, Saurav Man Singh
dc.contributor.authorJewell, Ward T.
dc.identifier.citationPun, K., Basnet, S. M. S., & Jewell, W. (2021). Solar power prediction in different forecasting horizons using machine learning and time series techniques. Paper presented at the 2021 IEEE Conference on Technologies for Sustainability, SusTech 2021, doi:10.1109/SusTech51236.2021.9467464en_US
dc.descriptionClick on the DOI link to access this conference paper at the publishers website (may not be free).en_US
dc.description.abstractSolar power generation is highly intermittent, nonlinear, and variable in nature. The increase in penetration level of solar energy resources poses technical challenges. An accurate forecasting model is crucial to minimizing these technical issues. Therefore, choosing the right forecasting technique for the right forecasting horizon is vital. In this study, the performance analysis of machine learning and time series forecasting techniques for various forecasting horizons has been investigated. Its accuracy, root mean square error (RMSE), and mean absolute error (MAE) have been compared to other techniques.en_US
dc.relation.ispartofseries2021 IEEE Conference on Technologies for Sustainability (SusTech);
dc.subjectRenewable energy sourcesen_US
dc.subjectTime series analysisen_US
dc.subjectMachine learningen_US
dc.subjectSolar energyen_US
dc.subjectPredictive modelsen_US
dc.subjectPerformance analysisen_US
dc.subjectSolar power generationen_US
dc.titleSolar power prediction in different forecasting horizons using machine learning and time series techniquesen_US
dc.typeConference paperen_US
dc.rights.holderCopyright © 2021, IEEEen_US

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record