Show simple item record

dc.contributor.authorTheocharides, Spyrosen
dc.contributor.authorKynigos, Mariosen
dc.contributor.authorTheristis, Mariosen
dc.contributor.authorMakrides, Georgeen
dc.contributor.authorGeorghiou, George E.en
dc.creatorTheocharides, Spyrosen
dc.creatorKynigos, Mariosen
dc.creatorTheristis, Mariosen
dc.creatorMakrides, Georgeen
dc.creatorGeorghiou, George E.en
dc.description.abstractAccurate solar irradiance forecasting is important for improving forecasting precision of photovoltaic (PV) power. In this study, an intra-day (i.e. 1 to 6 hours ahead) machine learning model based on an artificial neural network (ANN) was implemented for forecasting the intra-day incident solar irradiance (GI). The methodology included the implementation of the optimal ANN topology which was trained and validated on historical yearly datasets. The forecasting results demonstrated a normalised root mean square error (nRMSE) in the range of 4.23% to 9.51%. The lowest nRMSE of 4.23% was achieved for the hour-ahead forecast while the highest nRMSE of 9.51% was observed when forecasting at a horizon of 6 hours ahead. Finally, the mean absolute percentage error (MAPE) varied from 4.10% to 8.19% for the 1 hour to 6 hours ahead forecasts respectively.en
dc.source2019 IEEE 46th Photovoltaic Specialists Conference (PVSC)en
dc.titleIntra-day Solar Irradiance Forecasting Based on Artificial Neural Networksen
dc.description.endingpage1631Πολυτεχνική Σχολή / Faculty of EngineeringΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.type.uhtypeConference Objecten
dc.contributor.orcidGeorghiou, George E. [0000-0002-5872-5851]
dc.contributor.orcidTheristis, Marios [0000-0002-7265-4922]
dc.contributor.orcidTheocharides, Spyros [0000-0003-2164-6081]
dc.contributor.orcidMakrides, George [0000-0002-0327-0386]

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record