dc.contributor.author | Theocharides, Spyros | en |
dc.contributor.author | Venizelou, Venizelos | en |
dc.contributor.author | Makrides, George | en |
dc.contributor.author | Georghiou, George E. | en |
dc.creator | Theocharides, Spyros | en |
dc.creator | Venizelou, Venizelos | en |
dc.creator | Makrides, George | en |
dc.creator | Georghiou, George E. | en |
dc.date.accessioned | 2021-01-26T09:45:34Z | |
dc.date.available | 2021-01-26T09:45:34Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/63294 | |
dc.description.abstract | Accurate day-ahead photovoltaic (PV) power output forecasting techniques are important both for grid and plant operators. In this work, a machine learning model was implemented based on gradient boosting machine (GBM), for accurate PV production forecasting. The accuracy of the developed model was experimentally verified on a test system installed in Cyprus. The basic methodology followed was to train and optimize different developed GBM PV production day-ahead forecasting models with acquired data-sets and construct relationships between the input and output features. The final optimal developed GBM model included 7 inputs, 1000 trees with 10 minimum observations on each node and a shrinkage level set to 0.001. The prediction results obtained when the test set was applied to the model, demonstrated that the nRMSE was 0.80 %, while some days were exhibiting accuracies close to 0.50 %. Finally, the forecasting performance assessment results obtained when the test set and numerical weather prediction (NWP) data were applied to the optimal designed model, showed a nRMSE of 7.9 % with 55 % of the test set days exhibiting nRMSE below 5 %. The error relative to the capacity of the system for all points during clear sky conditions was in most cases less than 0.1 W/Wp. | en |
dc.source | 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC 34th EU PVSEC) | en |
dc.title | Day-ahead Forecasting of Solar Power Output from Photovoltaic Systems Utilising Gradient Boosting Machines | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/PVSC.2018.8547375 | |
dc.description.startingpage | 2371 | |
dc.description.endingpage | 2375 | |
dc.author.faculty | Πολυτεχνική Σχολή / Faculty of Engineering | |
dc.author.department | Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering | |
dc.type.uhtype | Conference Object | en |
dc.contributor.orcid | Georghiou, George E. [0000-0002-5872-5851] | |
dc.contributor.orcid | Venizelou, Venizelos [0000-0001-9545-0212] | |
dc.contributor.orcid | Theocharides, Spyros [0000-0003-2164-6081] | |
dc.contributor.orcid | Makrides, George [0000-0002-0327-0386] | |
dc.gnosis.orcid | 0000-0002-5872-5851 | |
dc.gnosis.orcid | 0000-0001-9545-0212 | |
dc.gnosis.orcid | 0000-0003-2164-6081 | |
dc.gnosis.orcid | 0000-0002-0327-0386 | |